Slide 1

GIS and Health
Martin Charlton1
Ronan Foley2
Dennis Pringle2
1National

Centre for Geocomputation
2Department of Geography
NUI Maynooth

Outline
• Preliminary observations on GIS
and health
• Examples of GIS in Health:
research and practice
• Mapping people not places

Patterns in Health Data
• Three main areas:
– Mapping of disease incidence
• Relative risk estimation

– Identification of disease clustering
• Surveillance

– Ecological analysis
• Relationship between incidence and
covariates
(Lawson et al, 2003)

Health Data
• Typically patient data is available
in two forms
• Individual – perhaps anonymised
to postcode/zip code level
• Aggregate – counts are available
for a set of zones
• The nature of the data affects the
nature of the analysis

Geographic Information Systems
• GIS is often confused with mapping
• A range of activities which includes
inter alia mapping and analysis
• Will typically involve several datasets
• May also involve several different
pieces of software

Mapping: EpiINFO
• This is a rather simple analysis and
mapping program
• It can be downloaded from the internet
• It doesn’t provide much in the way of
spatial data manipulation…
• … nor are its analytical capabilities very
advanced
• It is easy to use

Spatial Data Manipulation
• GIS isn’t merely concerned with
data display or query
• It’s useful in bringing together
different spatial datasets in the
first stages of an analysis
• This may well require a range of
different geometrical and
database operations

Analysis with Spatial Data
• Statistical analysis usually involves
some separate software.
• Spatial data have some properties
which mean that SPSS isn’t always
appropriate.
• There is now a range of specialist
software – much of it available for
download

GIS may be ideal for data manipulation, display and query,
but it’s not always ideal for analysis – in epidemiology a
variety of analytical software is available … WinBUGS,
GeoBUGS (MRC Biostatistics Unit, Cambridge)

Software to implement
particular methods – for
example scan statistics - can be
downloaded quickly and easily
from the Internet. Kulldorf’s
SaTScan system implements a
range of different scan
statistical models

GIS in Health in Practice
• Facility location – dialysis units
• Facility location – obs & gyn
• Data integration – schizophrenia
in Cavan
• Data manipulation and display –
GMS card holders in Maynooth

1: Provision of Dialysis Units
• There are 5 dialysis units for renal
patients in a region with a population
of about 3,000,000
• Major population centre has two units
about 5 miles apart
• Where would you locate an extra one?
• What would happen if one was
closed?

Location-allocation
• Location-allocation problems deal
with optimum location of facilities
• Attempt to minimise total
weighted travel time/distance
from users to facilities
• What data is needed?

Data requirements
• Digitised road network with classification
of road types
• Mean travel speeds on each type for
urban/non-urban roads
• Extents of urban areas
• Anonymised postcoded patient records
• Postcode->grid reference lookup
• Locations of hospitals in region (both with
and without dialysis units)

GIS processing
• Data integration
• Determine which segments of road network
are urban
• Determine travel times using road type
urban/non-urban speeds
• Tally numbers of patients at each node in
the road network
• Find nearest nodes in network to each
hospital
• Code hospitals as ‘existing’ or ‘potential’
units

Modelling/Analysis
• Carried out using standard GIS
functions
• Convenient ‘front end’ written
using system’s macro language
(save a lot of typing)
• Explore a range of alternatives

Hospital with
dialysis unit

Study region showing hospital locations and urban areas

Patients requiring dialysis (tallied to road nodes)

Classified road network – includes roads outside study area

•

Some alternative
scenarios:

(a) Optimal allocation
of patients
(b) Provide 1 extra
unit
(c) Provide 2 extra
units

(d) Close 1 unit in
city and reallocate
its patients

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

integrate data
process data
model and analyse
generate alternative scenarios
display the results

2: Facility Location
• Two hospitals both provided facilities
for obstetrics, gynaecology, paediatrics
and a special care baby unit.
• Was there a case for rationalisation and
improving the facilities at one hospital
only?
• Would closure disadvantage any
patients?

Data
• Locations of hospitals in study region
• Anonymised postcoded finished
consultant episodes for each of the
four specialities at each hospital
• Classified road network
• Speeds associated with each road
type

Data preparation
• Postcoded records allocated grid
references using Central Postcode
Directory (POSTZON)
• Vector data converted to raster
• Patient/specialty/hospital counts
produced for each raster
• Passage times computed for each
raster cell based on road
classification and speed estimates

Roads, urban areas, patient locations, hospitals in study area

Processing
• Travel time surfaces computed for
each hospital
• Each raster cell contains time
required to reach hospital in minutes
• Assumed walking speed in ‘non road’
areas

Travel time surface for hospital S – darker = shorter time

Travel time surface for hospital T

Time differences
• If we subtract the travel time surfaces areas
nearer one hospital will have positive
values for the difference, and areas nearer
the other will have negative ones
• Boundary between positive and negative
values is boundary of catchment for each
hospital.
• If you’re on the boundary (difference is
zero), it doesn’t matter which hospital you
choose

Roads, patient locations, hospital catchments

Indifference
• If we identify those rasters where the time
difference (ignoring the sign) is, say, 5 –
people in those areas are in the 5 minute
‘indifference’ zone.
• Patients might not be worried about a 5
minute increase in their travel time, they
might be worried about 30 minutes
• Indifference zones can be computed quite
easily

15-minute indifference zone between the two hospitals

Mean access time
• For each cell in the raster we can
compute the product of the number
of patients and the time required to
reach each hospital
• Dividing this by the number of
patients gives a patient weighted
average time for each hospital

Average travel times for the various specialties. There
isn’t much to chose between specialities with the
exception of paediatric episodes.

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

gather data
integrate data
process data
model data
display results

• All carried out using raster
operations

3: Schizophrenia in Cavan
• In 1996 Pringle, Waddington and
Youssef reported on evidence for the
causes of schizophrenia
• Ecological analysis of variation among
EDs
– Cases varied from 0-11 per ED
– Populations varied from 250-2750

• Poisson probabilities suggested that
one area had an unusually high number
of cases – these can be mapped

Male cases
superimposed on plot
of prevalence by ED

Male cases
superimposed on
rectified Landsat
image with ED
boundaries

4: GMS card holders: Maynooth
•Data on GMS – Medical Card holders
drawn under strict data protection
protocols from DOHC
• Anonymised address lists data matched
with GeoDirectory
•Address matching rate of around 77% in
Maynooth

• Interesting range of results around
average for the whole ED

•ED – single value 14.6 % (national average around 30%)
•Small Area – Range from 2% to 70% - noticeable internal variation and
good indicator of elderly or poor populations

•Strategic value in comparing datasets
•Important in policy analysis

Mapping invites comparison

Role of GIS
• Data integration
– Tally of cases in small areas requires
data on locations of cases and small
area boundaries
– ‘spatial join’ places small area codes
on the case records
– Counting is a standard database
operation

• Data display

Finally
• GIS encourages you to think about
space
• In a ‘conventional’ map of Ireland
areas (EDs, Counties…) are shown in
proportion to their physical size
• There are alternative ways of
displaying maps…

In a cartogram the areas are drawn
proportional to their populations –
this is a “density-equalised” map
projection

A map of the boundaries of counties and
electoral divisions – the areas are drawn
proportional to their physical size

By comparison, the more deprived
western area of Dublin, northern area
of Cork and south east of Limerick are
clearly apparent in the cartogram.

Haase’s deprivation index suggests that the
most deprived populations are in peripheral
rural areas in the north west.

Redrawing the data on the cartogram
reveals wide variation in the SMR, and also
uncovers the urban dimension which is
hidden in the conventional display

Patient data matched to Electoral Divisions –
hospital admissions for psychiatric problems – the
patchwork effect does not suggest any pattern

The cartogram reveals the urban nature
of the problem – those areas which are
more deprived are also those with
higher levels of illness/disability

Residents aged 15+ who are unable to work
through illness or disability: the map pattern
suggests that might be a largely rural
phenomenon

The roads, rail, and urban areas data have
been transformed into the same space as
the cartogram. Dublin and Cork seem
poorly provided with roads…

Other spatial information can be transformed
to the space of the density equalised
projection. Here is the familiar map of main
roads and towns.

Using a cartogram we see that the
incidence of lung cancer is randomly
spatially distributed

Lung cancer in males in New York State [Gastner & Newman: 2004]

Plotting locations of lung cancer cases, we
obtain a map which follows the underlying
population distribution – there appear to be
“clusters”

What about Ireland?
• Potentially hampered by challenge of
geocoding patient records
• Lack of small area population data
– EDs have an average population of 1150
– Wide variation in size (50-28000)
– Socially inhomogenous

Rural addresses
• A major challenge is non-uniqueness
of addresses in rural areas.
• For instance in Moy ED, 11 properties
share the address
‘Lackamore, Lahinch, Co. Clare’

What’s needed
• Postcoding should provide a
mechanism for geocoding patient data
with the granularity required for
planning the delivery of health care –
this has been the case in the UK for a
for long time
• Census data should be made available
for areas with small and similar
populations (cf: UK Output Areas)

Finally…
• GIS has an important role to play
in health studies
• Health data analysis is a rapidly
developing field
• However, in Ireland we are
hampered by data deficiencies –
now is the time for change

Maynooth: image in Google Earth

Central Dublin: image in Google Earth

Taoiseach’s Office: image in Google Earth

Downing Street, London: image in Google Earth

Maynooth: OSi orthophotograph added locally uisng KML

Sellafield: image in Google Earth

NCU – On Line Atlas of Cancer Mortality

The Philippine Institute
for Development
Studies makes an online atlas of socioeconomic indicators
available – contains
some health measures

The future
• Patient empowerment…

• GIS used to merge information from
regulatory agencies on 19196 landfill sites
• Sites buffered to 2km and postcodes
within buffer identified
• Postcoded live births (8.2x106), stillbirths
(43471) and congenital anomalies
(124,597) between 1982 and 1997
• Poisson regression modelling undertaken
• Evidence found for excess risk of
congenital anomaly and low birthweight

• Distance from each
census ward to
nearest hospital
computed
• Map shows number
of patients waiting
longer than 6
months for elective
inpatient care per
available and
unoccupied bed
within 60 minutes
travel time


Slide 2

GIS and Health
Martin Charlton1
Ronan Foley2
Dennis Pringle2
1National

Centre for Geocomputation
2Department of Geography
NUI Maynooth

Outline
• Preliminary observations on GIS
and health
• Examples of GIS in Health:
research and practice
• Mapping people not places

Patterns in Health Data
• Three main areas:
– Mapping of disease incidence
• Relative risk estimation

– Identification of disease clustering
• Surveillance

– Ecological analysis
• Relationship between incidence and
covariates
(Lawson et al, 2003)

Health Data
• Typically patient data is available
in two forms
• Individual – perhaps anonymised
to postcode/zip code level
• Aggregate – counts are available
for a set of zones
• The nature of the data affects the
nature of the analysis

Geographic Information Systems
• GIS is often confused with mapping
• A range of activities which includes
inter alia mapping and analysis
• Will typically involve several datasets
• May also involve several different
pieces of software

Mapping: EpiINFO
• This is a rather simple analysis and
mapping program
• It can be downloaded from the internet
• It doesn’t provide much in the way of
spatial data manipulation…
• … nor are its analytical capabilities very
advanced
• It is easy to use

Spatial Data Manipulation
• GIS isn’t merely concerned with
data display or query
• It’s useful in bringing together
different spatial datasets in the
first stages of an analysis
• This may well require a range of
different geometrical and
database operations

Analysis with Spatial Data
• Statistical analysis usually involves
some separate software.
• Spatial data have some properties
which mean that SPSS isn’t always
appropriate.
• There is now a range of specialist
software – much of it available for
download

GIS may be ideal for data manipulation, display and query,
but it’s not always ideal for analysis – in epidemiology a
variety of analytical software is available … WinBUGS,
GeoBUGS (MRC Biostatistics Unit, Cambridge)

Software to implement
particular methods – for
example scan statistics - can be
downloaded quickly and easily
from the Internet. Kulldorf’s
SaTScan system implements a
range of different scan
statistical models

GIS in Health in Practice
• Facility location – dialysis units
• Facility location – obs & gyn
• Data integration – schizophrenia
in Cavan
• Data manipulation and display –
GMS card holders in Maynooth

1: Provision of Dialysis Units
• There are 5 dialysis units for renal
patients in a region with a population
of about 3,000,000
• Major population centre has two units
about 5 miles apart
• Where would you locate an extra one?
• What would happen if one was
closed?

Location-allocation
• Location-allocation problems deal
with optimum location of facilities
• Attempt to minimise total
weighted travel time/distance
from users to facilities
• What data is needed?

Data requirements
• Digitised road network with classification
of road types
• Mean travel speeds on each type for
urban/non-urban roads
• Extents of urban areas
• Anonymised postcoded patient records
• Postcode->grid reference lookup
• Locations of hospitals in region (both with
and without dialysis units)

GIS processing
• Data integration
• Determine which segments of road network
are urban
• Determine travel times using road type
urban/non-urban speeds
• Tally numbers of patients at each node in
the road network
• Find nearest nodes in network to each
hospital
• Code hospitals as ‘existing’ or ‘potential’
units

Modelling/Analysis
• Carried out using standard GIS
functions
• Convenient ‘front end’ written
using system’s macro language
(save a lot of typing)
• Explore a range of alternatives

Hospital with
dialysis unit

Study region showing hospital locations and urban areas

Patients requiring dialysis (tallied to road nodes)

Classified road network – includes roads outside study area

•

Some alternative
scenarios:

(a) Optimal allocation
of patients
(b) Provide 1 extra
unit
(c) Provide 2 extra
units

(d) Close 1 unit in
city and reallocate
its patients

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

integrate data
process data
model and analyse
generate alternative scenarios
display the results

2: Facility Location
• Two hospitals both provided facilities
for obstetrics, gynaecology, paediatrics
and a special care baby unit.
• Was there a case for rationalisation and
improving the facilities at one hospital
only?
• Would closure disadvantage any
patients?

Data
• Locations of hospitals in study region
• Anonymised postcoded finished
consultant episodes for each of the
four specialities at each hospital
• Classified road network
• Speeds associated with each road
type

Data preparation
• Postcoded records allocated grid
references using Central Postcode
Directory (POSTZON)
• Vector data converted to raster
• Patient/specialty/hospital counts
produced for each raster
• Passage times computed for each
raster cell based on road
classification and speed estimates

Roads, urban areas, patient locations, hospitals in study area

Processing
• Travel time surfaces computed for
each hospital
• Each raster cell contains time
required to reach hospital in minutes
• Assumed walking speed in ‘non road’
areas

Travel time surface for hospital S – darker = shorter time

Travel time surface for hospital T

Time differences
• If we subtract the travel time surfaces areas
nearer one hospital will have positive
values for the difference, and areas nearer
the other will have negative ones
• Boundary between positive and negative
values is boundary of catchment for each
hospital.
• If you’re on the boundary (difference is
zero), it doesn’t matter which hospital you
choose

Roads, patient locations, hospital catchments

Indifference
• If we identify those rasters where the time
difference (ignoring the sign) is, say, 5 –
people in those areas are in the 5 minute
‘indifference’ zone.
• Patients might not be worried about a 5
minute increase in their travel time, they
might be worried about 30 minutes
• Indifference zones can be computed quite
easily

15-minute indifference zone between the two hospitals

Mean access time
• For each cell in the raster we can
compute the product of the number
of patients and the time required to
reach each hospital
• Dividing this by the number of
patients gives a patient weighted
average time for each hospital

Average travel times for the various specialties. There
isn’t much to chose between specialities with the
exception of paediatric episodes.

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

gather data
integrate data
process data
model data
display results

• All carried out using raster
operations

3: Schizophrenia in Cavan
• In 1996 Pringle, Waddington and
Youssef reported on evidence for the
causes of schizophrenia
• Ecological analysis of variation among
EDs
– Cases varied from 0-11 per ED
– Populations varied from 250-2750

• Poisson probabilities suggested that
one area had an unusually high number
of cases – these can be mapped

Male cases
superimposed on plot
of prevalence by ED

Male cases
superimposed on
rectified Landsat
image with ED
boundaries

4: GMS card holders: Maynooth
•Data on GMS – Medical Card holders
drawn under strict data protection
protocols from DOHC
• Anonymised address lists data matched
with GeoDirectory
•Address matching rate of around 77% in
Maynooth

• Interesting range of results around
average for the whole ED

•ED – single value 14.6 % (national average around 30%)
•Small Area – Range from 2% to 70% - noticeable internal variation and
good indicator of elderly or poor populations

•Strategic value in comparing datasets
•Important in policy analysis

Mapping invites comparison

Role of GIS
• Data integration
– Tally of cases in small areas requires
data on locations of cases and small
area boundaries
– ‘spatial join’ places small area codes
on the case records
– Counting is a standard database
operation

• Data display

Finally
• GIS encourages you to think about
space
• In a ‘conventional’ map of Ireland
areas (EDs, Counties…) are shown in
proportion to their physical size
• There are alternative ways of
displaying maps…

In a cartogram the areas are drawn
proportional to their populations –
this is a “density-equalised” map
projection

A map of the boundaries of counties and
electoral divisions – the areas are drawn
proportional to their physical size

By comparison, the more deprived
western area of Dublin, northern area
of Cork and south east of Limerick are
clearly apparent in the cartogram.

Haase’s deprivation index suggests that the
most deprived populations are in peripheral
rural areas in the north west.

Redrawing the data on the cartogram
reveals wide variation in the SMR, and also
uncovers the urban dimension which is
hidden in the conventional display

Patient data matched to Electoral Divisions –
hospital admissions for psychiatric problems – the
patchwork effect does not suggest any pattern

The cartogram reveals the urban nature
of the problem – those areas which are
more deprived are also those with
higher levels of illness/disability

Residents aged 15+ who are unable to work
through illness or disability: the map pattern
suggests that might be a largely rural
phenomenon

The roads, rail, and urban areas data have
been transformed into the same space as
the cartogram. Dublin and Cork seem
poorly provided with roads…

Other spatial information can be transformed
to the space of the density equalised
projection. Here is the familiar map of main
roads and towns.

Using a cartogram we see that the
incidence of lung cancer is randomly
spatially distributed

Lung cancer in males in New York State [Gastner & Newman: 2004]

Plotting locations of lung cancer cases, we
obtain a map which follows the underlying
population distribution – there appear to be
“clusters”

What about Ireland?
• Potentially hampered by challenge of
geocoding patient records
• Lack of small area population data
– EDs have an average population of 1150
– Wide variation in size (50-28000)
– Socially inhomogenous

Rural addresses
• A major challenge is non-uniqueness
of addresses in rural areas.
• For instance in Moy ED, 11 properties
share the address
‘Lackamore, Lahinch, Co. Clare’

What’s needed
• Postcoding should provide a
mechanism for geocoding patient data
with the granularity required for
planning the delivery of health care –
this has been the case in the UK for a
for long time
• Census data should be made available
for areas with small and similar
populations (cf: UK Output Areas)

Finally…
• GIS has an important role to play
in health studies
• Health data analysis is a rapidly
developing field
• However, in Ireland we are
hampered by data deficiencies –
now is the time for change

Maynooth: image in Google Earth

Central Dublin: image in Google Earth

Taoiseach’s Office: image in Google Earth

Downing Street, London: image in Google Earth

Maynooth: OSi orthophotograph added locally uisng KML

Sellafield: image in Google Earth

NCU – On Line Atlas of Cancer Mortality

The Philippine Institute
for Development
Studies makes an online atlas of socioeconomic indicators
available – contains
some health measures

The future
• Patient empowerment…

• GIS used to merge information from
regulatory agencies on 19196 landfill sites
• Sites buffered to 2km and postcodes
within buffer identified
• Postcoded live births (8.2x106), stillbirths
(43471) and congenital anomalies
(124,597) between 1982 and 1997
• Poisson regression modelling undertaken
• Evidence found for excess risk of
congenital anomaly and low birthweight

• Distance from each
census ward to
nearest hospital
computed
• Map shows number
of patients waiting
longer than 6
months for elective
inpatient care per
available and
unoccupied bed
within 60 minutes
travel time


Slide 3

GIS and Health
Martin Charlton1
Ronan Foley2
Dennis Pringle2
1National

Centre for Geocomputation
2Department of Geography
NUI Maynooth

Outline
• Preliminary observations on GIS
and health
• Examples of GIS in Health:
research and practice
• Mapping people not places

Patterns in Health Data
• Three main areas:
– Mapping of disease incidence
• Relative risk estimation

– Identification of disease clustering
• Surveillance

– Ecological analysis
• Relationship between incidence and
covariates
(Lawson et al, 2003)

Health Data
• Typically patient data is available
in two forms
• Individual – perhaps anonymised
to postcode/zip code level
• Aggregate – counts are available
for a set of zones
• The nature of the data affects the
nature of the analysis

Geographic Information Systems
• GIS is often confused with mapping
• A range of activities which includes
inter alia mapping and analysis
• Will typically involve several datasets
• May also involve several different
pieces of software

Mapping: EpiINFO
• This is a rather simple analysis and
mapping program
• It can be downloaded from the internet
• It doesn’t provide much in the way of
spatial data manipulation…
• … nor are its analytical capabilities very
advanced
• It is easy to use

Spatial Data Manipulation
• GIS isn’t merely concerned with
data display or query
• It’s useful in bringing together
different spatial datasets in the
first stages of an analysis
• This may well require a range of
different geometrical and
database operations

Analysis with Spatial Data
• Statistical analysis usually involves
some separate software.
• Spatial data have some properties
which mean that SPSS isn’t always
appropriate.
• There is now a range of specialist
software – much of it available for
download

GIS may be ideal for data manipulation, display and query,
but it’s not always ideal for analysis – in epidemiology a
variety of analytical software is available … WinBUGS,
GeoBUGS (MRC Biostatistics Unit, Cambridge)

Software to implement
particular methods – for
example scan statistics - can be
downloaded quickly and easily
from the Internet. Kulldorf’s
SaTScan system implements a
range of different scan
statistical models

GIS in Health in Practice
• Facility location – dialysis units
• Facility location – obs & gyn
• Data integration – schizophrenia
in Cavan
• Data manipulation and display –
GMS card holders in Maynooth

1: Provision of Dialysis Units
• There are 5 dialysis units for renal
patients in a region with a population
of about 3,000,000
• Major population centre has two units
about 5 miles apart
• Where would you locate an extra one?
• What would happen if one was
closed?

Location-allocation
• Location-allocation problems deal
with optimum location of facilities
• Attempt to minimise total
weighted travel time/distance
from users to facilities
• What data is needed?

Data requirements
• Digitised road network with classification
of road types
• Mean travel speeds on each type for
urban/non-urban roads
• Extents of urban areas
• Anonymised postcoded patient records
• Postcode->grid reference lookup
• Locations of hospitals in region (both with
and without dialysis units)

GIS processing
• Data integration
• Determine which segments of road network
are urban
• Determine travel times using road type
urban/non-urban speeds
• Tally numbers of patients at each node in
the road network
• Find nearest nodes in network to each
hospital
• Code hospitals as ‘existing’ or ‘potential’
units

Modelling/Analysis
• Carried out using standard GIS
functions
• Convenient ‘front end’ written
using system’s macro language
(save a lot of typing)
• Explore a range of alternatives

Hospital with
dialysis unit

Study region showing hospital locations and urban areas

Patients requiring dialysis (tallied to road nodes)

Classified road network – includes roads outside study area

•

Some alternative
scenarios:

(a) Optimal allocation
of patients
(b) Provide 1 extra
unit
(c) Provide 2 extra
units

(d) Close 1 unit in
city and reallocate
its patients

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

integrate data
process data
model and analyse
generate alternative scenarios
display the results

2: Facility Location
• Two hospitals both provided facilities
for obstetrics, gynaecology, paediatrics
and a special care baby unit.
• Was there a case for rationalisation and
improving the facilities at one hospital
only?
• Would closure disadvantage any
patients?

Data
• Locations of hospitals in study region
• Anonymised postcoded finished
consultant episodes for each of the
four specialities at each hospital
• Classified road network
• Speeds associated with each road
type

Data preparation
• Postcoded records allocated grid
references using Central Postcode
Directory (POSTZON)
• Vector data converted to raster
• Patient/specialty/hospital counts
produced for each raster
• Passage times computed for each
raster cell based on road
classification and speed estimates

Roads, urban areas, patient locations, hospitals in study area

Processing
• Travel time surfaces computed for
each hospital
• Each raster cell contains time
required to reach hospital in minutes
• Assumed walking speed in ‘non road’
areas

Travel time surface for hospital S – darker = shorter time

Travel time surface for hospital T

Time differences
• If we subtract the travel time surfaces areas
nearer one hospital will have positive
values for the difference, and areas nearer
the other will have negative ones
• Boundary between positive and negative
values is boundary of catchment for each
hospital.
• If you’re on the boundary (difference is
zero), it doesn’t matter which hospital you
choose

Roads, patient locations, hospital catchments

Indifference
• If we identify those rasters where the time
difference (ignoring the sign) is, say, 5 –
people in those areas are in the 5 minute
‘indifference’ zone.
• Patients might not be worried about a 5
minute increase in their travel time, they
might be worried about 30 minutes
• Indifference zones can be computed quite
easily

15-minute indifference zone between the two hospitals

Mean access time
• For each cell in the raster we can
compute the product of the number
of patients and the time required to
reach each hospital
• Dividing this by the number of
patients gives a patient weighted
average time for each hospital

Average travel times for the various specialties. There
isn’t much to chose between specialities with the
exception of paediatric episodes.

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

gather data
integrate data
process data
model data
display results

• All carried out using raster
operations

3: Schizophrenia in Cavan
• In 1996 Pringle, Waddington and
Youssef reported on evidence for the
causes of schizophrenia
• Ecological analysis of variation among
EDs
– Cases varied from 0-11 per ED
– Populations varied from 250-2750

• Poisson probabilities suggested that
one area had an unusually high number
of cases – these can be mapped

Male cases
superimposed on plot
of prevalence by ED

Male cases
superimposed on
rectified Landsat
image with ED
boundaries

4: GMS card holders: Maynooth
•Data on GMS – Medical Card holders
drawn under strict data protection
protocols from DOHC
• Anonymised address lists data matched
with GeoDirectory
•Address matching rate of around 77% in
Maynooth

• Interesting range of results around
average for the whole ED

•ED – single value 14.6 % (national average around 30%)
•Small Area – Range from 2% to 70% - noticeable internal variation and
good indicator of elderly or poor populations

•Strategic value in comparing datasets
•Important in policy analysis

Mapping invites comparison

Role of GIS
• Data integration
– Tally of cases in small areas requires
data on locations of cases and small
area boundaries
– ‘spatial join’ places small area codes
on the case records
– Counting is a standard database
operation

• Data display

Finally
• GIS encourages you to think about
space
• In a ‘conventional’ map of Ireland
areas (EDs, Counties…) are shown in
proportion to their physical size
• There are alternative ways of
displaying maps…

In a cartogram the areas are drawn
proportional to their populations –
this is a “density-equalised” map
projection

A map of the boundaries of counties and
electoral divisions – the areas are drawn
proportional to their physical size

By comparison, the more deprived
western area of Dublin, northern area
of Cork and south east of Limerick are
clearly apparent in the cartogram.

Haase’s deprivation index suggests that the
most deprived populations are in peripheral
rural areas in the north west.

Redrawing the data on the cartogram
reveals wide variation in the SMR, and also
uncovers the urban dimension which is
hidden in the conventional display

Patient data matched to Electoral Divisions –
hospital admissions for psychiatric problems – the
patchwork effect does not suggest any pattern

The cartogram reveals the urban nature
of the problem – those areas which are
more deprived are also those with
higher levels of illness/disability

Residents aged 15+ who are unable to work
through illness or disability: the map pattern
suggests that might be a largely rural
phenomenon

The roads, rail, and urban areas data have
been transformed into the same space as
the cartogram. Dublin and Cork seem
poorly provided with roads…

Other spatial information can be transformed
to the space of the density equalised
projection. Here is the familiar map of main
roads and towns.

Using a cartogram we see that the
incidence of lung cancer is randomly
spatially distributed

Lung cancer in males in New York State [Gastner & Newman: 2004]

Plotting locations of lung cancer cases, we
obtain a map which follows the underlying
population distribution – there appear to be
“clusters”

What about Ireland?
• Potentially hampered by challenge of
geocoding patient records
• Lack of small area population data
– EDs have an average population of 1150
– Wide variation in size (50-28000)
– Socially inhomogenous

Rural addresses
• A major challenge is non-uniqueness
of addresses in rural areas.
• For instance in Moy ED, 11 properties
share the address
‘Lackamore, Lahinch, Co. Clare’

What’s needed
• Postcoding should provide a
mechanism for geocoding patient data
with the granularity required for
planning the delivery of health care –
this has been the case in the UK for a
for long time
• Census data should be made available
for areas with small and similar
populations (cf: UK Output Areas)

Finally…
• GIS has an important role to play
in health studies
• Health data analysis is a rapidly
developing field
• However, in Ireland we are
hampered by data deficiencies –
now is the time for change

Maynooth: image in Google Earth

Central Dublin: image in Google Earth

Taoiseach’s Office: image in Google Earth

Downing Street, London: image in Google Earth

Maynooth: OSi orthophotograph added locally uisng KML

Sellafield: image in Google Earth

NCU – On Line Atlas of Cancer Mortality

The Philippine Institute
for Development
Studies makes an online atlas of socioeconomic indicators
available – contains
some health measures

The future
• Patient empowerment…

• GIS used to merge information from
regulatory agencies on 19196 landfill sites
• Sites buffered to 2km and postcodes
within buffer identified
• Postcoded live births (8.2x106), stillbirths
(43471) and congenital anomalies
(124,597) between 1982 and 1997
• Poisson regression modelling undertaken
• Evidence found for excess risk of
congenital anomaly and low birthweight

• Distance from each
census ward to
nearest hospital
computed
• Map shows number
of patients waiting
longer than 6
months for elective
inpatient care per
available and
unoccupied bed
within 60 minutes
travel time


Slide 4

GIS and Health
Martin Charlton1
Ronan Foley2
Dennis Pringle2
1National

Centre for Geocomputation
2Department of Geography
NUI Maynooth

Outline
• Preliminary observations on GIS
and health
• Examples of GIS in Health:
research and practice
• Mapping people not places

Patterns in Health Data
• Three main areas:
– Mapping of disease incidence
• Relative risk estimation

– Identification of disease clustering
• Surveillance

– Ecological analysis
• Relationship between incidence and
covariates
(Lawson et al, 2003)

Health Data
• Typically patient data is available
in two forms
• Individual – perhaps anonymised
to postcode/zip code level
• Aggregate – counts are available
for a set of zones
• The nature of the data affects the
nature of the analysis

Geographic Information Systems
• GIS is often confused with mapping
• A range of activities which includes
inter alia mapping and analysis
• Will typically involve several datasets
• May also involve several different
pieces of software

Mapping: EpiINFO
• This is a rather simple analysis and
mapping program
• It can be downloaded from the internet
• It doesn’t provide much in the way of
spatial data manipulation…
• … nor are its analytical capabilities very
advanced
• It is easy to use

Spatial Data Manipulation
• GIS isn’t merely concerned with
data display or query
• It’s useful in bringing together
different spatial datasets in the
first stages of an analysis
• This may well require a range of
different geometrical and
database operations

Analysis with Spatial Data
• Statistical analysis usually involves
some separate software.
• Spatial data have some properties
which mean that SPSS isn’t always
appropriate.
• There is now a range of specialist
software – much of it available for
download

GIS may be ideal for data manipulation, display and query,
but it’s not always ideal for analysis – in epidemiology a
variety of analytical software is available … WinBUGS,
GeoBUGS (MRC Biostatistics Unit, Cambridge)

Software to implement
particular methods – for
example scan statistics - can be
downloaded quickly and easily
from the Internet. Kulldorf’s
SaTScan system implements a
range of different scan
statistical models

GIS in Health in Practice
• Facility location – dialysis units
• Facility location – obs & gyn
• Data integration – schizophrenia
in Cavan
• Data manipulation and display –
GMS card holders in Maynooth

1: Provision of Dialysis Units
• There are 5 dialysis units for renal
patients in a region with a population
of about 3,000,000
• Major population centre has two units
about 5 miles apart
• Where would you locate an extra one?
• What would happen if one was
closed?

Location-allocation
• Location-allocation problems deal
with optimum location of facilities
• Attempt to minimise total
weighted travel time/distance
from users to facilities
• What data is needed?

Data requirements
• Digitised road network with classification
of road types
• Mean travel speeds on each type for
urban/non-urban roads
• Extents of urban areas
• Anonymised postcoded patient records
• Postcode->grid reference lookup
• Locations of hospitals in region (both with
and without dialysis units)

GIS processing
• Data integration
• Determine which segments of road network
are urban
• Determine travel times using road type
urban/non-urban speeds
• Tally numbers of patients at each node in
the road network
• Find nearest nodes in network to each
hospital
• Code hospitals as ‘existing’ or ‘potential’
units

Modelling/Analysis
• Carried out using standard GIS
functions
• Convenient ‘front end’ written
using system’s macro language
(save a lot of typing)
• Explore a range of alternatives

Hospital with
dialysis unit

Study region showing hospital locations and urban areas

Patients requiring dialysis (tallied to road nodes)

Classified road network – includes roads outside study area

•

Some alternative
scenarios:

(a) Optimal allocation
of patients
(b) Provide 1 extra
unit
(c) Provide 2 extra
units

(d) Close 1 unit in
city and reallocate
its patients

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

integrate data
process data
model and analyse
generate alternative scenarios
display the results

2: Facility Location
• Two hospitals both provided facilities
for obstetrics, gynaecology, paediatrics
and a special care baby unit.
• Was there a case for rationalisation and
improving the facilities at one hospital
only?
• Would closure disadvantage any
patients?

Data
• Locations of hospitals in study region
• Anonymised postcoded finished
consultant episodes for each of the
four specialities at each hospital
• Classified road network
• Speeds associated with each road
type

Data preparation
• Postcoded records allocated grid
references using Central Postcode
Directory (POSTZON)
• Vector data converted to raster
• Patient/specialty/hospital counts
produced for each raster
• Passage times computed for each
raster cell based on road
classification and speed estimates

Roads, urban areas, patient locations, hospitals in study area

Processing
• Travel time surfaces computed for
each hospital
• Each raster cell contains time
required to reach hospital in minutes
• Assumed walking speed in ‘non road’
areas

Travel time surface for hospital S – darker = shorter time

Travel time surface for hospital T

Time differences
• If we subtract the travel time surfaces areas
nearer one hospital will have positive
values for the difference, and areas nearer
the other will have negative ones
• Boundary between positive and negative
values is boundary of catchment for each
hospital.
• If you’re on the boundary (difference is
zero), it doesn’t matter which hospital you
choose

Roads, patient locations, hospital catchments

Indifference
• If we identify those rasters where the time
difference (ignoring the sign) is, say, 5 –
people in those areas are in the 5 minute
‘indifference’ zone.
• Patients might not be worried about a 5
minute increase in their travel time, they
might be worried about 30 minutes
• Indifference zones can be computed quite
easily

15-minute indifference zone between the two hospitals

Mean access time
• For each cell in the raster we can
compute the product of the number
of patients and the time required to
reach each hospital
• Dividing this by the number of
patients gives a patient weighted
average time for each hospital

Average travel times for the various specialties. There
isn’t much to chose between specialities with the
exception of paediatric episodes.

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

gather data
integrate data
process data
model data
display results

• All carried out using raster
operations

3: Schizophrenia in Cavan
• In 1996 Pringle, Waddington and
Youssef reported on evidence for the
causes of schizophrenia
• Ecological analysis of variation among
EDs
– Cases varied from 0-11 per ED
– Populations varied from 250-2750

• Poisson probabilities suggested that
one area had an unusually high number
of cases – these can be mapped

Male cases
superimposed on plot
of prevalence by ED

Male cases
superimposed on
rectified Landsat
image with ED
boundaries

4: GMS card holders: Maynooth
•Data on GMS – Medical Card holders
drawn under strict data protection
protocols from DOHC
• Anonymised address lists data matched
with GeoDirectory
•Address matching rate of around 77% in
Maynooth

• Interesting range of results around
average for the whole ED

•ED – single value 14.6 % (national average around 30%)
•Small Area – Range from 2% to 70% - noticeable internal variation and
good indicator of elderly or poor populations

•Strategic value in comparing datasets
•Important in policy analysis

Mapping invites comparison

Role of GIS
• Data integration
– Tally of cases in small areas requires
data on locations of cases and small
area boundaries
– ‘spatial join’ places small area codes
on the case records
– Counting is a standard database
operation

• Data display

Finally
• GIS encourages you to think about
space
• In a ‘conventional’ map of Ireland
areas (EDs, Counties…) are shown in
proportion to their physical size
• There are alternative ways of
displaying maps…

In a cartogram the areas are drawn
proportional to their populations –
this is a “density-equalised” map
projection

A map of the boundaries of counties and
electoral divisions – the areas are drawn
proportional to their physical size

By comparison, the more deprived
western area of Dublin, northern area
of Cork and south east of Limerick are
clearly apparent in the cartogram.

Haase’s deprivation index suggests that the
most deprived populations are in peripheral
rural areas in the north west.

Redrawing the data on the cartogram
reveals wide variation in the SMR, and also
uncovers the urban dimension which is
hidden in the conventional display

Patient data matched to Electoral Divisions –
hospital admissions for psychiatric problems – the
patchwork effect does not suggest any pattern

The cartogram reveals the urban nature
of the problem – those areas which are
more deprived are also those with
higher levels of illness/disability

Residents aged 15+ who are unable to work
through illness or disability: the map pattern
suggests that might be a largely rural
phenomenon

The roads, rail, and urban areas data have
been transformed into the same space as
the cartogram. Dublin and Cork seem
poorly provided with roads…

Other spatial information can be transformed
to the space of the density equalised
projection. Here is the familiar map of main
roads and towns.

Using a cartogram we see that the
incidence of lung cancer is randomly
spatially distributed

Lung cancer in males in New York State [Gastner & Newman: 2004]

Plotting locations of lung cancer cases, we
obtain a map which follows the underlying
population distribution – there appear to be
“clusters”

What about Ireland?
• Potentially hampered by challenge of
geocoding patient records
• Lack of small area population data
– EDs have an average population of 1150
– Wide variation in size (50-28000)
– Socially inhomogenous

Rural addresses
• A major challenge is non-uniqueness
of addresses in rural areas.
• For instance in Moy ED, 11 properties
share the address
‘Lackamore, Lahinch, Co. Clare’

What’s needed
• Postcoding should provide a
mechanism for geocoding patient data
with the granularity required for
planning the delivery of health care –
this has been the case in the UK for a
for long time
• Census data should be made available
for areas with small and similar
populations (cf: UK Output Areas)

Finally…
• GIS has an important role to play
in health studies
• Health data analysis is a rapidly
developing field
• However, in Ireland we are
hampered by data deficiencies –
now is the time for change

Maynooth: image in Google Earth

Central Dublin: image in Google Earth

Taoiseach’s Office: image in Google Earth

Downing Street, London: image in Google Earth

Maynooth: OSi orthophotograph added locally uisng KML

Sellafield: image in Google Earth

NCU – On Line Atlas of Cancer Mortality

The Philippine Institute
for Development
Studies makes an online atlas of socioeconomic indicators
available – contains
some health measures

The future
• Patient empowerment…

• GIS used to merge information from
regulatory agencies on 19196 landfill sites
• Sites buffered to 2km and postcodes
within buffer identified
• Postcoded live births (8.2x106), stillbirths
(43471) and congenital anomalies
(124,597) between 1982 and 1997
• Poisson regression modelling undertaken
• Evidence found for excess risk of
congenital anomaly and low birthweight

• Distance from each
census ward to
nearest hospital
computed
• Map shows number
of patients waiting
longer than 6
months for elective
inpatient care per
available and
unoccupied bed
within 60 minutes
travel time


Slide 5

GIS and Health
Martin Charlton1
Ronan Foley2
Dennis Pringle2
1National

Centre for Geocomputation
2Department of Geography
NUI Maynooth

Outline
• Preliminary observations on GIS
and health
• Examples of GIS in Health:
research and practice
• Mapping people not places

Patterns in Health Data
• Three main areas:
– Mapping of disease incidence
• Relative risk estimation

– Identification of disease clustering
• Surveillance

– Ecological analysis
• Relationship between incidence and
covariates
(Lawson et al, 2003)

Health Data
• Typically patient data is available
in two forms
• Individual – perhaps anonymised
to postcode/zip code level
• Aggregate – counts are available
for a set of zones
• The nature of the data affects the
nature of the analysis

Geographic Information Systems
• GIS is often confused with mapping
• A range of activities which includes
inter alia mapping and analysis
• Will typically involve several datasets
• May also involve several different
pieces of software

Mapping: EpiINFO
• This is a rather simple analysis and
mapping program
• It can be downloaded from the internet
• It doesn’t provide much in the way of
spatial data manipulation…
• … nor are its analytical capabilities very
advanced
• It is easy to use

Spatial Data Manipulation
• GIS isn’t merely concerned with
data display or query
• It’s useful in bringing together
different spatial datasets in the
first stages of an analysis
• This may well require a range of
different geometrical and
database operations

Analysis with Spatial Data
• Statistical analysis usually involves
some separate software.
• Spatial data have some properties
which mean that SPSS isn’t always
appropriate.
• There is now a range of specialist
software – much of it available for
download

GIS may be ideal for data manipulation, display and query,
but it’s not always ideal for analysis – in epidemiology a
variety of analytical software is available … WinBUGS,
GeoBUGS (MRC Biostatistics Unit, Cambridge)

Software to implement
particular methods – for
example scan statistics - can be
downloaded quickly and easily
from the Internet. Kulldorf’s
SaTScan system implements a
range of different scan
statistical models

GIS in Health in Practice
• Facility location – dialysis units
• Facility location – obs & gyn
• Data integration – schizophrenia
in Cavan
• Data manipulation and display –
GMS card holders in Maynooth

1: Provision of Dialysis Units
• There are 5 dialysis units for renal
patients in a region with a population
of about 3,000,000
• Major population centre has two units
about 5 miles apart
• Where would you locate an extra one?
• What would happen if one was
closed?

Location-allocation
• Location-allocation problems deal
with optimum location of facilities
• Attempt to minimise total
weighted travel time/distance
from users to facilities
• What data is needed?

Data requirements
• Digitised road network with classification
of road types
• Mean travel speeds on each type for
urban/non-urban roads
• Extents of urban areas
• Anonymised postcoded patient records
• Postcode->grid reference lookup
• Locations of hospitals in region (both with
and without dialysis units)

GIS processing
• Data integration
• Determine which segments of road network
are urban
• Determine travel times using road type
urban/non-urban speeds
• Tally numbers of patients at each node in
the road network
• Find nearest nodes in network to each
hospital
• Code hospitals as ‘existing’ or ‘potential’
units

Modelling/Analysis
• Carried out using standard GIS
functions
• Convenient ‘front end’ written
using system’s macro language
(save a lot of typing)
• Explore a range of alternatives

Hospital with
dialysis unit

Study region showing hospital locations and urban areas

Patients requiring dialysis (tallied to road nodes)

Classified road network – includes roads outside study area

•

Some alternative
scenarios:

(a) Optimal allocation
of patients
(b) Provide 1 extra
unit
(c) Provide 2 extra
units

(d) Close 1 unit in
city and reallocate
its patients

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

integrate data
process data
model and analyse
generate alternative scenarios
display the results

2: Facility Location
• Two hospitals both provided facilities
for obstetrics, gynaecology, paediatrics
and a special care baby unit.
• Was there a case for rationalisation and
improving the facilities at one hospital
only?
• Would closure disadvantage any
patients?

Data
• Locations of hospitals in study region
• Anonymised postcoded finished
consultant episodes for each of the
four specialities at each hospital
• Classified road network
• Speeds associated with each road
type

Data preparation
• Postcoded records allocated grid
references using Central Postcode
Directory (POSTZON)
• Vector data converted to raster
• Patient/specialty/hospital counts
produced for each raster
• Passage times computed for each
raster cell based on road
classification and speed estimates

Roads, urban areas, patient locations, hospitals in study area

Processing
• Travel time surfaces computed for
each hospital
• Each raster cell contains time
required to reach hospital in minutes
• Assumed walking speed in ‘non road’
areas

Travel time surface for hospital S – darker = shorter time

Travel time surface for hospital T

Time differences
• If we subtract the travel time surfaces areas
nearer one hospital will have positive
values for the difference, and areas nearer
the other will have negative ones
• Boundary between positive and negative
values is boundary of catchment for each
hospital.
• If you’re on the boundary (difference is
zero), it doesn’t matter which hospital you
choose

Roads, patient locations, hospital catchments

Indifference
• If we identify those rasters where the time
difference (ignoring the sign) is, say, 5 –
people in those areas are in the 5 minute
‘indifference’ zone.
• Patients might not be worried about a 5
minute increase in their travel time, they
might be worried about 30 minutes
• Indifference zones can be computed quite
easily

15-minute indifference zone between the two hospitals

Mean access time
• For each cell in the raster we can
compute the product of the number
of patients and the time required to
reach each hospital
• Dividing this by the number of
patients gives a patient weighted
average time for each hospital

Average travel times for the various specialties. There
isn’t much to chose between specialities with the
exception of paediatric episodes.

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

gather data
integrate data
process data
model data
display results

• All carried out using raster
operations

3: Schizophrenia in Cavan
• In 1996 Pringle, Waddington and
Youssef reported on evidence for the
causes of schizophrenia
• Ecological analysis of variation among
EDs
– Cases varied from 0-11 per ED
– Populations varied from 250-2750

• Poisson probabilities suggested that
one area had an unusually high number
of cases – these can be mapped

Male cases
superimposed on plot
of prevalence by ED

Male cases
superimposed on
rectified Landsat
image with ED
boundaries

4: GMS card holders: Maynooth
•Data on GMS – Medical Card holders
drawn under strict data protection
protocols from DOHC
• Anonymised address lists data matched
with GeoDirectory
•Address matching rate of around 77% in
Maynooth

• Interesting range of results around
average for the whole ED

•ED – single value 14.6 % (national average around 30%)
•Small Area – Range from 2% to 70% - noticeable internal variation and
good indicator of elderly or poor populations

•Strategic value in comparing datasets
•Important in policy analysis

Mapping invites comparison

Role of GIS
• Data integration
– Tally of cases in small areas requires
data on locations of cases and small
area boundaries
– ‘spatial join’ places small area codes
on the case records
– Counting is a standard database
operation

• Data display

Finally
• GIS encourages you to think about
space
• In a ‘conventional’ map of Ireland
areas (EDs, Counties…) are shown in
proportion to their physical size
• There are alternative ways of
displaying maps…

In a cartogram the areas are drawn
proportional to their populations –
this is a “density-equalised” map
projection

A map of the boundaries of counties and
electoral divisions – the areas are drawn
proportional to their physical size

By comparison, the more deprived
western area of Dublin, northern area
of Cork and south east of Limerick are
clearly apparent in the cartogram.

Haase’s deprivation index suggests that the
most deprived populations are in peripheral
rural areas in the north west.

Redrawing the data on the cartogram
reveals wide variation in the SMR, and also
uncovers the urban dimension which is
hidden in the conventional display

Patient data matched to Electoral Divisions –
hospital admissions for psychiatric problems – the
patchwork effect does not suggest any pattern

The cartogram reveals the urban nature
of the problem – those areas which are
more deprived are also those with
higher levels of illness/disability

Residents aged 15+ who are unable to work
through illness or disability: the map pattern
suggests that might be a largely rural
phenomenon

The roads, rail, and urban areas data have
been transformed into the same space as
the cartogram. Dublin and Cork seem
poorly provided with roads…

Other spatial information can be transformed
to the space of the density equalised
projection. Here is the familiar map of main
roads and towns.

Using a cartogram we see that the
incidence of lung cancer is randomly
spatially distributed

Lung cancer in males in New York State [Gastner & Newman: 2004]

Plotting locations of lung cancer cases, we
obtain a map which follows the underlying
population distribution – there appear to be
“clusters”

What about Ireland?
• Potentially hampered by challenge of
geocoding patient records
• Lack of small area population data
– EDs have an average population of 1150
– Wide variation in size (50-28000)
– Socially inhomogenous

Rural addresses
• A major challenge is non-uniqueness
of addresses in rural areas.
• For instance in Moy ED, 11 properties
share the address
‘Lackamore, Lahinch, Co. Clare’

What’s needed
• Postcoding should provide a
mechanism for geocoding patient data
with the granularity required for
planning the delivery of health care –
this has been the case in the UK for a
for long time
• Census data should be made available
for areas with small and similar
populations (cf: UK Output Areas)

Finally…
• GIS has an important role to play
in health studies
• Health data analysis is a rapidly
developing field
• However, in Ireland we are
hampered by data deficiencies –
now is the time for change

Maynooth: image in Google Earth

Central Dublin: image in Google Earth

Taoiseach’s Office: image in Google Earth

Downing Street, London: image in Google Earth

Maynooth: OSi orthophotograph added locally uisng KML

Sellafield: image in Google Earth

NCU – On Line Atlas of Cancer Mortality

The Philippine Institute
for Development
Studies makes an online atlas of socioeconomic indicators
available – contains
some health measures

The future
• Patient empowerment…

• GIS used to merge information from
regulatory agencies on 19196 landfill sites
• Sites buffered to 2km and postcodes
within buffer identified
• Postcoded live births (8.2x106), stillbirths
(43471) and congenital anomalies
(124,597) between 1982 and 1997
• Poisson regression modelling undertaken
• Evidence found for excess risk of
congenital anomaly and low birthweight

• Distance from each
census ward to
nearest hospital
computed
• Map shows number
of patients waiting
longer than 6
months for elective
inpatient care per
available and
unoccupied bed
within 60 minutes
travel time


Slide 6

GIS and Health
Martin Charlton1
Ronan Foley2
Dennis Pringle2
1National

Centre for Geocomputation
2Department of Geography
NUI Maynooth

Outline
• Preliminary observations on GIS
and health
• Examples of GIS in Health:
research and practice
• Mapping people not places

Patterns in Health Data
• Three main areas:
– Mapping of disease incidence
• Relative risk estimation

– Identification of disease clustering
• Surveillance

– Ecological analysis
• Relationship between incidence and
covariates
(Lawson et al, 2003)

Health Data
• Typically patient data is available
in two forms
• Individual – perhaps anonymised
to postcode/zip code level
• Aggregate – counts are available
for a set of zones
• The nature of the data affects the
nature of the analysis

Geographic Information Systems
• GIS is often confused with mapping
• A range of activities which includes
inter alia mapping and analysis
• Will typically involve several datasets
• May also involve several different
pieces of software

Mapping: EpiINFO
• This is a rather simple analysis and
mapping program
• It can be downloaded from the internet
• It doesn’t provide much in the way of
spatial data manipulation…
• … nor are its analytical capabilities very
advanced
• It is easy to use

Spatial Data Manipulation
• GIS isn’t merely concerned with
data display or query
• It’s useful in bringing together
different spatial datasets in the
first stages of an analysis
• This may well require a range of
different geometrical and
database operations

Analysis with Spatial Data
• Statistical analysis usually involves
some separate software.
• Spatial data have some properties
which mean that SPSS isn’t always
appropriate.
• There is now a range of specialist
software – much of it available for
download

GIS may be ideal for data manipulation, display and query,
but it’s not always ideal for analysis – in epidemiology a
variety of analytical software is available … WinBUGS,
GeoBUGS (MRC Biostatistics Unit, Cambridge)

Software to implement
particular methods – for
example scan statistics - can be
downloaded quickly and easily
from the Internet. Kulldorf’s
SaTScan system implements a
range of different scan
statistical models

GIS in Health in Practice
• Facility location – dialysis units
• Facility location – obs & gyn
• Data integration – schizophrenia
in Cavan
• Data manipulation and display –
GMS card holders in Maynooth

1: Provision of Dialysis Units
• There are 5 dialysis units for renal
patients in a region with a population
of about 3,000,000
• Major population centre has two units
about 5 miles apart
• Where would you locate an extra one?
• What would happen if one was
closed?

Location-allocation
• Location-allocation problems deal
with optimum location of facilities
• Attempt to minimise total
weighted travel time/distance
from users to facilities
• What data is needed?

Data requirements
• Digitised road network with classification
of road types
• Mean travel speeds on each type for
urban/non-urban roads
• Extents of urban areas
• Anonymised postcoded patient records
• Postcode->grid reference lookup
• Locations of hospitals in region (both with
and without dialysis units)

GIS processing
• Data integration
• Determine which segments of road network
are urban
• Determine travel times using road type
urban/non-urban speeds
• Tally numbers of patients at each node in
the road network
• Find nearest nodes in network to each
hospital
• Code hospitals as ‘existing’ or ‘potential’
units

Modelling/Analysis
• Carried out using standard GIS
functions
• Convenient ‘front end’ written
using system’s macro language
(save a lot of typing)
• Explore a range of alternatives

Hospital with
dialysis unit

Study region showing hospital locations and urban areas

Patients requiring dialysis (tallied to road nodes)

Classified road network – includes roads outside study area

•

Some alternative
scenarios:

(a) Optimal allocation
of patients
(b) Provide 1 extra
unit
(c) Provide 2 extra
units

(d) Close 1 unit in
city and reallocate
its patients

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

integrate data
process data
model and analyse
generate alternative scenarios
display the results

2: Facility Location
• Two hospitals both provided facilities
for obstetrics, gynaecology, paediatrics
and a special care baby unit.
• Was there a case for rationalisation and
improving the facilities at one hospital
only?
• Would closure disadvantage any
patients?

Data
• Locations of hospitals in study region
• Anonymised postcoded finished
consultant episodes for each of the
four specialities at each hospital
• Classified road network
• Speeds associated with each road
type

Data preparation
• Postcoded records allocated grid
references using Central Postcode
Directory (POSTZON)
• Vector data converted to raster
• Patient/specialty/hospital counts
produced for each raster
• Passage times computed for each
raster cell based on road
classification and speed estimates

Roads, urban areas, patient locations, hospitals in study area

Processing
• Travel time surfaces computed for
each hospital
• Each raster cell contains time
required to reach hospital in minutes
• Assumed walking speed in ‘non road’
areas

Travel time surface for hospital S – darker = shorter time

Travel time surface for hospital T

Time differences
• If we subtract the travel time surfaces areas
nearer one hospital will have positive
values for the difference, and areas nearer
the other will have negative ones
• Boundary between positive and negative
values is boundary of catchment for each
hospital.
• If you’re on the boundary (difference is
zero), it doesn’t matter which hospital you
choose

Roads, patient locations, hospital catchments

Indifference
• If we identify those rasters where the time
difference (ignoring the sign) is, say, 5 –
people in those areas are in the 5 minute
‘indifference’ zone.
• Patients might not be worried about a 5
minute increase in their travel time, they
might be worried about 30 minutes
• Indifference zones can be computed quite
easily

15-minute indifference zone between the two hospitals

Mean access time
• For each cell in the raster we can
compute the product of the number
of patients and the time required to
reach each hospital
• Dividing this by the number of
patients gives a patient weighted
average time for each hospital

Average travel times for the various specialties. There
isn’t much to chose between specialities with the
exception of paediatric episodes.

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

gather data
integrate data
process data
model data
display results

• All carried out using raster
operations

3: Schizophrenia in Cavan
• In 1996 Pringle, Waddington and
Youssef reported on evidence for the
causes of schizophrenia
• Ecological analysis of variation among
EDs
– Cases varied from 0-11 per ED
– Populations varied from 250-2750

• Poisson probabilities suggested that
one area had an unusually high number
of cases – these can be mapped

Male cases
superimposed on plot
of prevalence by ED

Male cases
superimposed on
rectified Landsat
image with ED
boundaries

4: GMS card holders: Maynooth
•Data on GMS – Medical Card holders
drawn under strict data protection
protocols from DOHC
• Anonymised address lists data matched
with GeoDirectory
•Address matching rate of around 77% in
Maynooth

• Interesting range of results around
average for the whole ED

•ED – single value 14.6 % (national average around 30%)
•Small Area – Range from 2% to 70% - noticeable internal variation and
good indicator of elderly or poor populations

•Strategic value in comparing datasets
•Important in policy analysis

Mapping invites comparison

Role of GIS
• Data integration
– Tally of cases in small areas requires
data on locations of cases and small
area boundaries
– ‘spatial join’ places small area codes
on the case records
– Counting is a standard database
operation

• Data display

Finally
• GIS encourages you to think about
space
• In a ‘conventional’ map of Ireland
areas (EDs, Counties…) are shown in
proportion to their physical size
• There are alternative ways of
displaying maps…

In a cartogram the areas are drawn
proportional to their populations –
this is a “density-equalised” map
projection

A map of the boundaries of counties and
electoral divisions – the areas are drawn
proportional to their physical size

By comparison, the more deprived
western area of Dublin, northern area
of Cork and south east of Limerick are
clearly apparent in the cartogram.

Haase’s deprivation index suggests that the
most deprived populations are in peripheral
rural areas in the north west.

Redrawing the data on the cartogram
reveals wide variation in the SMR, and also
uncovers the urban dimension which is
hidden in the conventional display

Patient data matched to Electoral Divisions –
hospital admissions for psychiatric problems – the
patchwork effect does not suggest any pattern

The cartogram reveals the urban nature
of the problem – those areas which are
more deprived are also those with
higher levels of illness/disability

Residents aged 15+ who are unable to work
through illness or disability: the map pattern
suggests that might be a largely rural
phenomenon

The roads, rail, and urban areas data have
been transformed into the same space as
the cartogram. Dublin and Cork seem
poorly provided with roads…

Other spatial information can be transformed
to the space of the density equalised
projection. Here is the familiar map of main
roads and towns.

Using a cartogram we see that the
incidence of lung cancer is randomly
spatially distributed

Lung cancer in males in New York State [Gastner & Newman: 2004]

Plotting locations of lung cancer cases, we
obtain a map which follows the underlying
population distribution – there appear to be
“clusters”

What about Ireland?
• Potentially hampered by challenge of
geocoding patient records
• Lack of small area population data
– EDs have an average population of 1150
– Wide variation in size (50-28000)
– Socially inhomogenous

Rural addresses
• A major challenge is non-uniqueness
of addresses in rural areas.
• For instance in Moy ED, 11 properties
share the address
‘Lackamore, Lahinch, Co. Clare’

What’s needed
• Postcoding should provide a
mechanism for geocoding patient data
with the granularity required for
planning the delivery of health care –
this has been the case in the UK for a
for long time
• Census data should be made available
for areas with small and similar
populations (cf: UK Output Areas)

Finally…
• GIS has an important role to play
in health studies
• Health data analysis is a rapidly
developing field
• However, in Ireland we are
hampered by data deficiencies –
now is the time for change

Maynooth: image in Google Earth

Central Dublin: image in Google Earth

Taoiseach’s Office: image in Google Earth

Downing Street, London: image in Google Earth

Maynooth: OSi orthophotograph added locally uisng KML

Sellafield: image in Google Earth

NCU – On Line Atlas of Cancer Mortality

The Philippine Institute
for Development
Studies makes an online atlas of socioeconomic indicators
available – contains
some health measures

The future
• Patient empowerment…

• GIS used to merge information from
regulatory agencies on 19196 landfill sites
• Sites buffered to 2km and postcodes
within buffer identified
• Postcoded live births (8.2x106), stillbirths
(43471) and congenital anomalies
(124,597) between 1982 and 1997
• Poisson regression modelling undertaken
• Evidence found for excess risk of
congenital anomaly and low birthweight

• Distance from each
census ward to
nearest hospital
computed
• Map shows number
of patients waiting
longer than 6
months for elective
inpatient care per
available and
unoccupied bed
within 60 minutes
travel time


Slide 7

GIS and Health
Martin Charlton1
Ronan Foley2
Dennis Pringle2
1National

Centre for Geocomputation
2Department of Geography
NUI Maynooth

Outline
• Preliminary observations on GIS
and health
• Examples of GIS in Health:
research and practice
• Mapping people not places

Patterns in Health Data
• Three main areas:
– Mapping of disease incidence
• Relative risk estimation

– Identification of disease clustering
• Surveillance

– Ecological analysis
• Relationship between incidence and
covariates
(Lawson et al, 2003)

Health Data
• Typically patient data is available
in two forms
• Individual – perhaps anonymised
to postcode/zip code level
• Aggregate – counts are available
for a set of zones
• The nature of the data affects the
nature of the analysis

Geographic Information Systems
• GIS is often confused with mapping
• A range of activities which includes
inter alia mapping and analysis
• Will typically involve several datasets
• May also involve several different
pieces of software

Mapping: EpiINFO
• This is a rather simple analysis and
mapping program
• It can be downloaded from the internet
• It doesn’t provide much in the way of
spatial data manipulation…
• … nor are its analytical capabilities very
advanced
• It is easy to use

Spatial Data Manipulation
• GIS isn’t merely concerned with
data display or query
• It’s useful in bringing together
different spatial datasets in the
first stages of an analysis
• This may well require a range of
different geometrical and
database operations

Analysis with Spatial Data
• Statistical analysis usually involves
some separate software.
• Spatial data have some properties
which mean that SPSS isn’t always
appropriate.
• There is now a range of specialist
software – much of it available for
download

GIS may be ideal for data manipulation, display and query,
but it’s not always ideal for analysis – in epidemiology a
variety of analytical software is available … WinBUGS,
GeoBUGS (MRC Biostatistics Unit, Cambridge)

Software to implement
particular methods – for
example scan statistics - can be
downloaded quickly and easily
from the Internet. Kulldorf’s
SaTScan system implements a
range of different scan
statistical models

GIS in Health in Practice
• Facility location – dialysis units
• Facility location – obs & gyn
• Data integration – schizophrenia
in Cavan
• Data manipulation and display –
GMS card holders in Maynooth

1: Provision of Dialysis Units
• There are 5 dialysis units for renal
patients in a region with a population
of about 3,000,000
• Major population centre has two units
about 5 miles apart
• Where would you locate an extra one?
• What would happen if one was
closed?

Location-allocation
• Location-allocation problems deal
with optimum location of facilities
• Attempt to minimise total
weighted travel time/distance
from users to facilities
• What data is needed?

Data requirements
• Digitised road network with classification
of road types
• Mean travel speeds on each type for
urban/non-urban roads
• Extents of urban areas
• Anonymised postcoded patient records
• Postcode->grid reference lookup
• Locations of hospitals in region (both with
and without dialysis units)

GIS processing
• Data integration
• Determine which segments of road network
are urban
• Determine travel times using road type
urban/non-urban speeds
• Tally numbers of patients at each node in
the road network
• Find nearest nodes in network to each
hospital
• Code hospitals as ‘existing’ or ‘potential’
units

Modelling/Analysis
• Carried out using standard GIS
functions
• Convenient ‘front end’ written
using system’s macro language
(save a lot of typing)
• Explore a range of alternatives

Hospital with
dialysis unit

Study region showing hospital locations and urban areas

Patients requiring dialysis (tallied to road nodes)

Classified road network – includes roads outside study area

•

Some alternative
scenarios:

(a) Optimal allocation
of patients
(b) Provide 1 extra
unit
(c) Provide 2 extra
units

(d) Close 1 unit in
city and reallocate
its patients

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

integrate data
process data
model and analyse
generate alternative scenarios
display the results

2: Facility Location
• Two hospitals both provided facilities
for obstetrics, gynaecology, paediatrics
and a special care baby unit.
• Was there a case for rationalisation and
improving the facilities at one hospital
only?
• Would closure disadvantage any
patients?

Data
• Locations of hospitals in study region
• Anonymised postcoded finished
consultant episodes for each of the
four specialities at each hospital
• Classified road network
• Speeds associated with each road
type

Data preparation
• Postcoded records allocated grid
references using Central Postcode
Directory (POSTZON)
• Vector data converted to raster
• Patient/specialty/hospital counts
produced for each raster
• Passage times computed for each
raster cell based on road
classification and speed estimates

Roads, urban areas, patient locations, hospitals in study area

Processing
• Travel time surfaces computed for
each hospital
• Each raster cell contains time
required to reach hospital in minutes
• Assumed walking speed in ‘non road’
areas

Travel time surface for hospital S – darker = shorter time

Travel time surface for hospital T

Time differences
• If we subtract the travel time surfaces areas
nearer one hospital will have positive
values for the difference, and areas nearer
the other will have negative ones
• Boundary between positive and negative
values is boundary of catchment for each
hospital.
• If you’re on the boundary (difference is
zero), it doesn’t matter which hospital you
choose

Roads, patient locations, hospital catchments

Indifference
• If we identify those rasters where the time
difference (ignoring the sign) is, say, 5 –
people in those areas are in the 5 minute
‘indifference’ zone.
• Patients might not be worried about a 5
minute increase in their travel time, they
might be worried about 30 minutes
• Indifference zones can be computed quite
easily

15-minute indifference zone between the two hospitals

Mean access time
• For each cell in the raster we can
compute the product of the number
of patients and the time required to
reach each hospital
• Dividing this by the number of
patients gives a patient weighted
average time for each hospital

Average travel times for the various specialties. There
isn’t much to chose between specialities with the
exception of paediatric episodes.

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

gather data
integrate data
process data
model data
display results

• All carried out using raster
operations

3: Schizophrenia in Cavan
• In 1996 Pringle, Waddington and
Youssef reported on evidence for the
causes of schizophrenia
• Ecological analysis of variation among
EDs
– Cases varied from 0-11 per ED
– Populations varied from 250-2750

• Poisson probabilities suggested that
one area had an unusually high number
of cases – these can be mapped

Male cases
superimposed on plot
of prevalence by ED

Male cases
superimposed on
rectified Landsat
image with ED
boundaries

4: GMS card holders: Maynooth
•Data on GMS – Medical Card holders
drawn under strict data protection
protocols from DOHC
• Anonymised address lists data matched
with GeoDirectory
•Address matching rate of around 77% in
Maynooth

• Interesting range of results around
average for the whole ED

•ED – single value 14.6 % (national average around 30%)
•Small Area – Range from 2% to 70% - noticeable internal variation and
good indicator of elderly or poor populations

•Strategic value in comparing datasets
•Important in policy analysis

Mapping invites comparison

Role of GIS
• Data integration
– Tally of cases in small areas requires
data on locations of cases and small
area boundaries
– ‘spatial join’ places small area codes
on the case records
– Counting is a standard database
operation

• Data display

Finally
• GIS encourages you to think about
space
• In a ‘conventional’ map of Ireland
areas (EDs, Counties…) are shown in
proportion to their physical size
• There are alternative ways of
displaying maps…

In a cartogram the areas are drawn
proportional to their populations –
this is a “density-equalised” map
projection

A map of the boundaries of counties and
electoral divisions – the areas are drawn
proportional to their physical size

By comparison, the more deprived
western area of Dublin, northern area
of Cork and south east of Limerick are
clearly apparent in the cartogram.

Haase’s deprivation index suggests that the
most deprived populations are in peripheral
rural areas in the north west.

Redrawing the data on the cartogram
reveals wide variation in the SMR, and also
uncovers the urban dimension which is
hidden in the conventional display

Patient data matched to Electoral Divisions –
hospital admissions for psychiatric problems – the
patchwork effect does not suggest any pattern

The cartogram reveals the urban nature
of the problem – those areas which are
more deprived are also those with
higher levels of illness/disability

Residents aged 15+ who are unable to work
through illness or disability: the map pattern
suggests that might be a largely rural
phenomenon

The roads, rail, and urban areas data have
been transformed into the same space as
the cartogram. Dublin and Cork seem
poorly provided with roads…

Other spatial information can be transformed
to the space of the density equalised
projection. Here is the familiar map of main
roads and towns.

Using a cartogram we see that the
incidence of lung cancer is randomly
spatially distributed

Lung cancer in males in New York State [Gastner & Newman: 2004]

Plotting locations of lung cancer cases, we
obtain a map which follows the underlying
population distribution – there appear to be
“clusters”

What about Ireland?
• Potentially hampered by challenge of
geocoding patient records
• Lack of small area population data
– EDs have an average population of 1150
– Wide variation in size (50-28000)
– Socially inhomogenous

Rural addresses
• A major challenge is non-uniqueness
of addresses in rural areas.
• For instance in Moy ED, 11 properties
share the address
‘Lackamore, Lahinch, Co. Clare’

What’s needed
• Postcoding should provide a
mechanism for geocoding patient data
with the granularity required for
planning the delivery of health care –
this has been the case in the UK for a
for long time
• Census data should be made available
for areas with small and similar
populations (cf: UK Output Areas)

Finally…
• GIS has an important role to play
in health studies
• Health data analysis is a rapidly
developing field
• However, in Ireland we are
hampered by data deficiencies –
now is the time for change

Maynooth: image in Google Earth

Central Dublin: image in Google Earth

Taoiseach’s Office: image in Google Earth

Downing Street, London: image in Google Earth

Maynooth: OSi orthophotograph added locally uisng KML

Sellafield: image in Google Earth

NCU – On Line Atlas of Cancer Mortality

The Philippine Institute
for Development
Studies makes an online atlas of socioeconomic indicators
available – contains
some health measures

The future
• Patient empowerment…

• GIS used to merge information from
regulatory agencies on 19196 landfill sites
• Sites buffered to 2km and postcodes
within buffer identified
• Postcoded live births (8.2x106), stillbirths
(43471) and congenital anomalies
(124,597) between 1982 and 1997
• Poisson regression modelling undertaken
• Evidence found for excess risk of
congenital anomaly and low birthweight

• Distance from each
census ward to
nearest hospital
computed
• Map shows number
of patients waiting
longer than 6
months for elective
inpatient care per
available and
unoccupied bed
within 60 minutes
travel time


Slide 8

GIS and Health
Martin Charlton1
Ronan Foley2
Dennis Pringle2
1National

Centre for Geocomputation
2Department of Geography
NUI Maynooth

Outline
• Preliminary observations on GIS
and health
• Examples of GIS in Health:
research and practice
• Mapping people not places

Patterns in Health Data
• Three main areas:
– Mapping of disease incidence
• Relative risk estimation

– Identification of disease clustering
• Surveillance

– Ecological analysis
• Relationship between incidence and
covariates
(Lawson et al, 2003)

Health Data
• Typically patient data is available
in two forms
• Individual – perhaps anonymised
to postcode/zip code level
• Aggregate – counts are available
for a set of zones
• The nature of the data affects the
nature of the analysis

Geographic Information Systems
• GIS is often confused with mapping
• A range of activities which includes
inter alia mapping and analysis
• Will typically involve several datasets
• May also involve several different
pieces of software

Mapping: EpiINFO
• This is a rather simple analysis and
mapping program
• It can be downloaded from the internet
• It doesn’t provide much in the way of
spatial data manipulation…
• … nor are its analytical capabilities very
advanced
• It is easy to use

Spatial Data Manipulation
• GIS isn’t merely concerned with
data display or query
• It’s useful in bringing together
different spatial datasets in the
first stages of an analysis
• This may well require a range of
different geometrical and
database operations

Analysis with Spatial Data
• Statistical analysis usually involves
some separate software.
• Spatial data have some properties
which mean that SPSS isn’t always
appropriate.
• There is now a range of specialist
software – much of it available for
download

GIS may be ideal for data manipulation, display and query,
but it’s not always ideal for analysis – in epidemiology a
variety of analytical software is available … WinBUGS,
GeoBUGS (MRC Biostatistics Unit, Cambridge)

Software to implement
particular methods – for
example scan statistics - can be
downloaded quickly and easily
from the Internet. Kulldorf’s
SaTScan system implements a
range of different scan
statistical models

GIS in Health in Practice
• Facility location – dialysis units
• Facility location – obs & gyn
• Data integration – schizophrenia
in Cavan
• Data manipulation and display –
GMS card holders in Maynooth

1: Provision of Dialysis Units
• There are 5 dialysis units for renal
patients in a region with a population
of about 3,000,000
• Major population centre has two units
about 5 miles apart
• Where would you locate an extra one?
• What would happen if one was
closed?

Location-allocation
• Location-allocation problems deal
with optimum location of facilities
• Attempt to minimise total
weighted travel time/distance
from users to facilities
• What data is needed?

Data requirements
• Digitised road network with classification
of road types
• Mean travel speeds on each type for
urban/non-urban roads
• Extents of urban areas
• Anonymised postcoded patient records
• Postcode->grid reference lookup
• Locations of hospitals in region (both with
and without dialysis units)

GIS processing
• Data integration
• Determine which segments of road network
are urban
• Determine travel times using road type
urban/non-urban speeds
• Tally numbers of patients at each node in
the road network
• Find nearest nodes in network to each
hospital
• Code hospitals as ‘existing’ or ‘potential’
units

Modelling/Analysis
• Carried out using standard GIS
functions
• Convenient ‘front end’ written
using system’s macro language
(save a lot of typing)
• Explore a range of alternatives

Hospital with
dialysis unit

Study region showing hospital locations and urban areas

Patients requiring dialysis (tallied to road nodes)

Classified road network – includes roads outside study area

•

Some alternative
scenarios:

(a) Optimal allocation
of patients
(b) Provide 1 extra
unit
(c) Provide 2 extra
units

(d) Close 1 unit in
city and reallocate
its patients

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

integrate data
process data
model and analyse
generate alternative scenarios
display the results

2: Facility Location
• Two hospitals both provided facilities
for obstetrics, gynaecology, paediatrics
and a special care baby unit.
• Was there a case for rationalisation and
improving the facilities at one hospital
only?
• Would closure disadvantage any
patients?

Data
• Locations of hospitals in study region
• Anonymised postcoded finished
consultant episodes for each of the
four specialities at each hospital
• Classified road network
• Speeds associated with each road
type

Data preparation
• Postcoded records allocated grid
references using Central Postcode
Directory (POSTZON)
• Vector data converted to raster
• Patient/specialty/hospital counts
produced for each raster
• Passage times computed for each
raster cell based on road
classification and speed estimates

Roads, urban areas, patient locations, hospitals in study area

Processing
• Travel time surfaces computed for
each hospital
• Each raster cell contains time
required to reach hospital in minutes
• Assumed walking speed in ‘non road’
areas

Travel time surface for hospital S – darker = shorter time

Travel time surface for hospital T

Time differences
• If we subtract the travel time surfaces areas
nearer one hospital will have positive
values for the difference, and areas nearer
the other will have negative ones
• Boundary between positive and negative
values is boundary of catchment for each
hospital.
• If you’re on the boundary (difference is
zero), it doesn’t matter which hospital you
choose

Roads, patient locations, hospital catchments

Indifference
• If we identify those rasters where the time
difference (ignoring the sign) is, say, 5 –
people in those areas are in the 5 minute
‘indifference’ zone.
• Patients might not be worried about a 5
minute increase in their travel time, they
might be worried about 30 minutes
• Indifference zones can be computed quite
easily

15-minute indifference zone between the two hospitals

Mean access time
• For each cell in the raster we can
compute the product of the number
of patients and the time required to
reach each hospital
• Dividing this by the number of
patients gives a patient weighted
average time for each hospital

Average travel times for the various specialties. There
isn’t much to chose between specialities with the
exception of paediatric episodes.

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

gather data
integrate data
process data
model data
display results

• All carried out using raster
operations

3: Schizophrenia in Cavan
• In 1996 Pringle, Waddington and
Youssef reported on evidence for the
causes of schizophrenia
• Ecological analysis of variation among
EDs
– Cases varied from 0-11 per ED
– Populations varied from 250-2750

• Poisson probabilities suggested that
one area had an unusually high number
of cases – these can be mapped

Male cases
superimposed on plot
of prevalence by ED

Male cases
superimposed on
rectified Landsat
image with ED
boundaries

4: GMS card holders: Maynooth
•Data on GMS – Medical Card holders
drawn under strict data protection
protocols from DOHC
• Anonymised address lists data matched
with GeoDirectory
•Address matching rate of around 77% in
Maynooth

• Interesting range of results around
average for the whole ED

•ED – single value 14.6 % (national average around 30%)
•Small Area – Range from 2% to 70% - noticeable internal variation and
good indicator of elderly or poor populations

•Strategic value in comparing datasets
•Important in policy analysis

Mapping invites comparison

Role of GIS
• Data integration
– Tally of cases in small areas requires
data on locations of cases and small
area boundaries
– ‘spatial join’ places small area codes
on the case records
– Counting is a standard database
operation

• Data display

Finally
• GIS encourages you to think about
space
• In a ‘conventional’ map of Ireland
areas (EDs, Counties…) are shown in
proportion to their physical size
• There are alternative ways of
displaying maps…

In a cartogram the areas are drawn
proportional to their populations –
this is a “density-equalised” map
projection

A map of the boundaries of counties and
electoral divisions – the areas are drawn
proportional to their physical size

By comparison, the more deprived
western area of Dublin, northern area
of Cork and south east of Limerick are
clearly apparent in the cartogram.

Haase’s deprivation index suggests that the
most deprived populations are in peripheral
rural areas in the north west.

Redrawing the data on the cartogram
reveals wide variation in the SMR, and also
uncovers the urban dimension which is
hidden in the conventional display

Patient data matched to Electoral Divisions –
hospital admissions for psychiatric problems – the
patchwork effect does not suggest any pattern

The cartogram reveals the urban nature
of the problem – those areas which are
more deprived are also those with
higher levels of illness/disability

Residents aged 15+ who are unable to work
through illness or disability: the map pattern
suggests that might be a largely rural
phenomenon

The roads, rail, and urban areas data have
been transformed into the same space as
the cartogram. Dublin and Cork seem
poorly provided with roads…

Other spatial information can be transformed
to the space of the density equalised
projection. Here is the familiar map of main
roads and towns.

Using a cartogram we see that the
incidence of lung cancer is randomly
spatially distributed

Lung cancer in males in New York State [Gastner & Newman: 2004]

Plotting locations of lung cancer cases, we
obtain a map which follows the underlying
population distribution – there appear to be
“clusters”

What about Ireland?
• Potentially hampered by challenge of
geocoding patient records
• Lack of small area population data
– EDs have an average population of 1150
– Wide variation in size (50-28000)
– Socially inhomogenous

Rural addresses
• A major challenge is non-uniqueness
of addresses in rural areas.
• For instance in Moy ED, 11 properties
share the address
‘Lackamore, Lahinch, Co. Clare’

What’s needed
• Postcoding should provide a
mechanism for geocoding patient data
with the granularity required for
planning the delivery of health care –
this has been the case in the UK for a
for long time
• Census data should be made available
for areas with small and similar
populations (cf: UK Output Areas)

Finally…
• GIS has an important role to play
in health studies
• Health data analysis is a rapidly
developing field
• However, in Ireland we are
hampered by data deficiencies –
now is the time for change

Maynooth: image in Google Earth

Central Dublin: image in Google Earth

Taoiseach’s Office: image in Google Earth

Downing Street, London: image in Google Earth

Maynooth: OSi orthophotograph added locally uisng KML

Sellafield: image in Google Earth

NCU – On Line Atlas of Cancer Mortality

The Philippine Institute
for Development
Studies makes an online atlas of socioeconomic indicators
available – contains
some health measures

The future
• Patient empowerment…

• GIS used to merge information from
regulatory agencies on 19196 landfill sites
• Sites buffered to 2km and postcodes
within buffer identified
• Postcoded live births (8.2x106), stillbirths
(43471) and congenital anomalies
(124,597) between 1982 and 1997
• Poisson regression modelling undertaken
• Evidence found for excess risk of
congenital anomaly and low birthweight

• Distance from each
census ward to
nearest hospital
computed
• Map shows number
of patients waiting
longer than 6
months for elective
inpatient care per
available and
unoccupied bed
within 60 minutes
travel time


Slide 9

GIS and Health
Martin Charlton1
Ronan Foley2
Dennis Pringle2
1National

Centre for Geocomputation
2Department of Geography
NUI Maynooth

Outline
• Preliminary observations on GIS
and health
• Examples of GIS in Health:
research and practice
• Mapping people not places

Patterns in Health Data
• Three main areas:
– Mapping of disease incidence
• Relative risk estimation

– Identification of disease clustering
• Surveillance

– Ecological analysis
• Relationship between incidence and
covariates
(Lawson et al, 2003)

Health Data
• Typically patient data is available
in two forms
• Individual – perhaps anonymised
to postcode/zip code level
• Aggregate – counts are available
for a set of zones
• The nature of the data affects the
nature of the analysis

Geographic Information Systems
• GIS is often confused with mapping
• A range of activities which includes
inter alia mapping and analysis
• Will typically involve several datasets
• May also involve several different
pieces of software

Mapping: EpiINFO
• This is a rather simple analysis and
mapping program
• It can be downloaded from the internet
• It doesn’t provide much in the way of
spatial data manipulation…
• … nor are its analytical capabilities very
advanced
• It is easy to use

Spatial Data Manipulation
• GIS isn’t merely concerned with
data display or query
• It’s useful in bringing together
different spatial datasets in the
first stages of an analysis
• This may well require a range of
different geometrical and
database operations

Analysis with Spatial Data
• Statistical analysis usually involves
some separate software.
• Spatial data have some properties
which mean that SPSS isn’t always
appropriate.
• There is now a range of specialist
software – much of it available for
download

GIS may be ideal for data manipulation, display and query,
but it’s not always ideal for analysis – in epidemiology a
variety of analytical software is available … WinBUGS,
GeoBUGS (MRC Biostatistics Unit, Cambridge)

Software to implement
particular methods – for
example scan statistics - can be
downloaded quickly and easily
from the Internet. Kulldorf’s
SaTScan system implements a
range of different scan
statistical models

GIS in Health in Practice
• Facility location – dialysis units
• Facility location – obs & gyn
• Data integration – schizophrenia
in Cavan
• Data manipulation and display –
GMS card holders in Maynooth

1: Provision of Dialysis Units
• There are 5 dialysis units for renal
patients in a region with a population
of about 3,000,000
• Major population centre has two units
about 5 miles apart
• Where would you locate an extra one?
• What would happen if one was
closed?

Location-allocation
• Location-allocation problems deal
with optimum location of facilities
• Attempt to minimise total
weighted travel time/distance
from users to facilities
• What data is needed?

Data requirements
• Digitised road network with classification
of road types
• Mean travel speeds on each type for
urban/non-urban roads
• Extents of urban areas
• Anonymised postcoded patient records
• Postcode->grid reference lookup
• Locations of hospitals in region (both with
and without dialysis units)

GIS processing
• Data integration
• Determine which segments of road network
are urban
• Determine travel times using road type
urban/non-urban speeds
• Tally numbers of patients at each node in
the road network
• Find nearest nodes in network to each
hospital
• Code hospitals as ‘existing’ or ‘potential’
units

Modelling/Analysis
• Carried out using standard GIS
functions
• Convenient ‘front end’ written
using system’s macro language
(save a lot of typing)
• Explore a range of alternatives

Hospital with
dialysis unit

Study region showing hospital locations and urban areas

Patients requiring dialysis (tallied to road nodes)

Classified road network – includes roads outside study area

•

Some alternative
scenarios:

(a) Optimal allocation
of patients
(b) Provide 1 extra
unit
(c) Provide 2 extra
units

(d) Close 1 unit in
city and reallocate
its patients

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

integrate data
process data
model and analyse
generate alternative scenarios
display the results

2: Facility Location
• Two hospitals both provided facilities
for obstetrics, gynaecology, paediatrics
and a special care baby unit.
• Was there a case for rationalisation and
improving the facilities at one hospital
only?
• Would closure disadvantage any
patients?

Data
• Locations of hospitals in study region
• Anonymised postcoded finished
consultant episodes for each of the
four specialities at each hospital
• Classified road network
• Speeds associated with each road
type

Data preparation
• Postcoded records allocated grid
references using Central Postcode
Directory (POSTZON)
• Vector data converted to raster
• Patient/specialty/hospital counts
produced for each raster
• Passage times computed for each
raster cell based on road
classification and speed estimates

Roads, urban areas, patient locations, hospitals in study area

Processing
• Travel time surfaces computed for
each hospital
• Each raster cell contains time
required to reach hospital in minutes
• Assumed walking speed in ‘non road’
areas

Travel time surface for hospital S – darker = shorter time

Travel time surface for hospital T

Time differences
• If we subtract the travel time surfaces areas
nearer one hospital will have positive
values for the difference, and areas nearer
the other will have negative ones
• Boundary between positive and negative
values is boundary of catchment for each
hospital.
• If you’re on the boundary (difference is
zero), it doesn’t matter which hospital you
choose

Roads, patient locations, hospital catchments

Indifference
• If we identify those rasters where the time
difference (ignoring the sign) is, say, 5 –
people in those areas are in the 5 minute
‘indifference’ zone.
• Patients might not be worried about a 5
minute increase in their travel time, they
might be worried about 30 minutes
• Indifference zones can be computed quite
easily

15-minute indifference zone between the two hospitals

Mean access time
• For each cell in the raster we can
compute the product of the number
of patients and the time required to
reach each hospital
• Dividing this by the number of
patients gives a patient weighted
average time for each hospital

Average travel times for the various specialties. There
isn’t much to chose between specialities with the
exception of paediatric episodes.

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

gather data
integrate data
process data
model data
display results

• All carried out using raster
operations

3: Schizophrenia in Cavan
• In 1996 Pringle, Waddington and
Youssef reported on evidence for the
causes of schizophrenia
• Ecological analysis of variation among
EDs
– Cases varied from 0-11 per ED
– Populations varied from 250-2750

• Poisson probabilities suggested that
one area had an unusually high number
of cases – these can be mapped

Male cases
superimposed on plot
of prevalence by ED

Male cases
superimposed on
rectified Landsat
image with ED
boundaries

4: GMS card holders: Maynooth
•Data on GMS – Medical Card holders
drawn under strict data protection
protocols from DOHC
• Anonymised address lists data matched
with GeoDirectory
•Address matching rate of around 77% in
Maynooth

• Interesting range of results around
average for the whole ED

•ED – single value 14.6 % (national average around 30%)
•Small Area – Range from 2% to 70% - noticeable internal variation and
good indicator of elderly or poor populations

•Strategic value in comparing datasets
•Important in policy analysis

Mapping invites comparison

Role of GIS
• Data integration
– Tally of cases in small areas requires
data on locations of cases and small
area boundaries
– ‘spatial join’ places small area codes
on the case records
– Counting is a standard database
operation

• Data display

Finally
• GIS encourages you to think about
space
• In a ‘conventional’ map of Ireland
areas (EDs, Counties…) are shown in
proportion to their physical size
• There are alternative ways of
displaying maps…

In a cartogram the areas are drawn
proportional to their populations –
this is a “density-equalised” map
projection

A map of the boundaries of counties and
electoral divisions – the areas are drawn
proportional to their physical size

By comparison, the more deprived
western area of Dublin, northern area
of Cork and south east of Limerick are
clearly apparent in the cartogram.

Haase’s deprivation index suggests that the
most deprived populations are in peripheral
rural areas in the north west.

Redrawing the data on the cartogram
reveals wide variation in the SMR, and also
uncovers the urban dimension which is
hidden in the conventional display

Patient data matched to Electoral Divisions –
hospital admissions for psychiatric problems – the
patchwork effect does not suggest any pattern

The cartogram reveals the urban nature
of the problem – those areas which are
more deprived are also those with
higher levels of illness/disability

Residents aged 15+ who are unable to work
through illness or disability: the map pattern
suggests that might be a largely rural
phenomenon

The roads, rail, and urban areas data have
been transformed into the same space as
the cartogram. Dublin and Cork seem
poorly provided with roads…

Other spatial information can be transformed
to the space of the density equalised
projection. Here is the familiar map of main
roads and towns.

Using a cartogram we see that the
incidence of lung cancer is randomly
spatially distributed

Lung cancer in males in New York State [Gastner & Newman: 2004]

Plotting locations of lung cancer cases, we
obtain a map which follows the underlying
population distribution – there appear to be
“clusters”

What about Ireland?
• Potentially hampered by challenge of
geocoding patient records
• Lack of small area population data
– EDs have an average population of 1150
– Wide variation in size (50-28000)
– Socially inhomogenous

Rural addresses
• A major challenge is non-uniqueness
of addresses in rural areas.
• For instance in Moy ED, 11 properties
share the address
‘Lackamore, Lahinch, Co. Clare’

What’s needed
• Postcoding should provide a
mechanism for geocoding patient data
with the granularity required for
planning the delivery of health care –
this has been the case in the UK for a
for long time
• Census data should be made available
for areas with small and similar
populations (cf: UK Output Areas)

Finally…
• GIS has an important role to play
in health studies
• Health data analysis is a rapidly
developing field
• However, in Ireland we are
hampered by data deficiencies –
now is the time for change

Maynooth: image in Google Earth

Central Dublin: image in Google Earth

Taoiseach’s Office: image in Google Earth

Downing Street, London: image in Google Earth

Maynooth: OSi orthophotograph added locally uisng KML

Sellafield: image in Google Earth

NCU – On Line Atlas of Cancer Mortality

The Philippine Institute
for Development
Studies makes an online atlas of socioeconomic indicators
available – contains
some health measures

The future
• Patient empowerment…

• GIS used to merge information from
regulatory agencies on 19196 landfill sites
• Sites buffered to 2km and postcodes
within buffer identified
• Postcoded live births (8.2x106), stillbirths
(43471) and congenital anomalies
(124,597) between 1982 and 1997
• Poisson regression modelling undertaken
• Evidence found for excess risk of
congenital anomaly and low birthweight

• Distance from each
census ward to
nearest hospital
computed
• Map shows number
of patients waiting
longer than 6
months for elective
inpatient care per
available and
unoccupied bed
within 60 minutes
travel time


Slide 10

GIS and Health
Martin Charlton1
Ronan Foley2
Dennis Pringle2
1National

Centre for Geocomputation
2Department of Geography
NUI Maynooth

Outline
• Preliminary observations on GIS
and health
• Examples of GIS in Health:
research and practice
• Mapping people not places

Patterns in Health Data
• Three main areas:
– Mapping of disease incidence
• Relative risk estimation

– Identification of disease clustering
• Surveillance

– Ecological analysis
• Relationship between incidence and
covariates
(Lawson et al, 2003)

Health Data
• Typically patient data is available
in two forms
• Individual – perhaps anonymised
to postcode/zip code level
• Aggregate – counts are available
for a set of zones
• The nature of the data affects the
nature of the analysis

Geographic Information Systems
• GIS is often confused with mapping
• A range of activities which includes
inter alia mapping and analysis
• Will typically involve several datasets
• May also involve several different
pieces of software

Mapping: EpiINFO
• This is a rather simple analysis and
mapping program
• It can be downloaded from the internet
• It doesn’t provide much in the way of
spatial data manipulation…
• … nor are its analytical capabilities very
advanced
• It is easy to use

Spatial Data Manipulation
• GIS isn’t merely concerned with
data display or query
• It’s useful in bringing together
different spatial datasets in the
first stages of an analysis
• This may well require a range of
different geometrical and
database operations

Analysis with Spatial Data
• Statistical analysis usually involves
some separate software.
• Spatial data have some properties
which mean that SPSS isn’t always
appropriate.
• There is now a range of specialist
software – much of it available for
download

GIS may be ideal for data manipulation, display and query,
but it’s not always ideal for analysis – in epidemiology a
variety of analytical software is available … WinBUGS,
GeoBUGS (MRC Biostatistics Unit, Cambridge)

Software to implement
particular methods – for
example scan statistics - can be
downloaded quickly and easily
from the Internet. Kulldorf’s
SaTScan system implements a
range of different scan
statistical models

GIS in Health in Practice
• Facility location – dialysis units
• Facility location – obs & gyn
• Data integration – schizophrenia
in Cavan
• Data manipulation and display –
GMS card holders in Maynooth

1: Provision of Dialysis Units
• There are 5 dialysis units for renal
patients in a region with a population
of about 3,000,000
• Major population centre has two units
about 5 miles apart
• Where would you locate an extra one?
• What would happen if one was
closed?

Location-allocation
• Location-allocation problems deal
with optimum location of facilities
• Attempt to minimise total
weighted travel time/distance
from users to facilities
• What data is needed?

Data requirements
• Digitised road network with classification
of road types
• Mean travel speeds on each type for
urban/non-urban roads
• Extents of urban areas
• Anonymised postcoded patient records
• Postcode->grid reference lookup
• Locations of hospitals in region (both with
and without dialysis units)

GIS processing
• Data integration
• Determine which segments of road network
are urban
• Determine travel times using road type
urban/non-urban speeds
• Tally numbers of patients at each node in
the road network
• Find nearest nodes in network to each
hospital
• Code hospitals as ‘existing’ or ‘potential’
units

Modelling/Analysis
• Carried out using standard GIS
functions
• Convenient ‘front end’ written
using system’s macro language
(save a lot of typing)
• Explore a range of alternatives

Hospital with
dialysis unit

Study region showing hospital locations and urban areas

Patients requiring dialysis (tallied to road nodes)

Classified road network – includes roads outside study area

•

Some alternative
scenarios:

(a) Optimal allocation
of patients
(b) Provide 1 extra
unit
(c) Provide 2 extra
units

(d) Close 1 unit in
city and reallocate
its patients

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

integrate data
process data
model and analyse
generate alternative scenarios
display the results

2: Facility Location
• Two hospitals both provided facilities
for obstetrics, gynaecology, paediatrics
and a special care baby unit.
• Was there a case for rationalisation and
improving the facilities at one hospital
only?
• Would closure disadvantage any
patients?

Data
• Locations of hospitals in study region
• Anonymised postcoded finished
consultant episodes for each of the
four specialities at each hospital
• Classified road network
• Speeds associated with each road
type

Data preparation
• Postcoded records allocated grid
references using Central Postcode
Directory (POSTZON)
• Vector data converted to raster
• Patient/specialty/hospital counts
produced for each raster
• Passage times computed for each
raster cell based on road
classification and speed estimates

Roads, urban areas, patient locations, hospitals in study area

Processing
• Travel time surfaces computed for
each hospital
• Each raster cell contains time
required to reach hospital in minutes
• Assumed walking speed in ‘non road’
areas

Travel time surface for hospital S – darker = shorter time

Travel time surface for hospital T

Time differences
• If we subtract the travel time surfaces areas
nearer one hospital will have positive
values for the difference, and areas nearer
the other will have negative ones
• Boundary between positive and negative
values is boundary of catchment for each
hospital.
• If you’re on the boundary (difference is
zero), it doesn’t matter which hospital you
choose

Roads, patient locations, hospital catchments

Indifference
• If we identify those rasters where the time
difference (ignoring the sign) is, say, 5 –
people in those areas are in the 5 minute
‘indifference’ zone.
• Patients might not be worried about a 5
minute increase in their travel time, they
might be worried about 30 minutes
• Indifference zones can be computed quite
easily

15-minute indifference zone between the two hospitals

Mean access time
• For each cell in the raster we can
compute the product of the number
of patients and the time required to
reach each hospital
• Dividing this by the number of
patients gives a patient weighted
average time for each hospital

Average travel times for the various specialties. There
isn’t much to chose between specialities with the
exception of paediatric episodes.

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

gather data
integrate data
process data
model data
display results

• All carried out using raster
operations

3: Schizophrenia in Cavan
• In 1996 Pringle, Waddington and
Youssef reported on evidence for the
causes of schizophrenia
• Ecological analysis of variation among
EDs
– Cases varied from 0-11 per ED
– Populations varied from 250-2750

• Poisson probabilities suggested that
one area had an unusually high number
of cases – these can be mapped

Male cases
superimposed on plot
of prevalence by ED

Male cases
superimposed on
rectified Landsat
image with ED
boundaries

4: GMS card holders: Maynooth
•Data on GMS – Medical Card holders
drawn under strict data protection
protocols from DOHC
• Anonymised address lists data matched
with GeoDirectory
•Address matching rate of around 77% in
Maynooth

• Interesting range of results around
average for the whole ED

•ED – single value 14.6 % (national average around 30%)
•Small Area – Range from 2% to 70% - noticeable internal variation and
good indicator of elderly or poor populations

•Strategic value in comparing datasets
•Important in policy analysis

Mapping invites comparison

Role of GIS
• Data integration
– Tally of cases in small areas requires
data on locations of cases and small
area boundaries
– ‘spatial join’ places small area codes
on the case records
– Counting is a standard database
operation

• Data display

Finally
• GIS encourages you to think about
space
• In a ‘conventional’ map of Ireland
areas (EDs, Counties…) are shown in
proportion to their physical size
• There are alternative ways of
displaying maps…

In a cartogram the areas are drawn
proportional to their populations –
this is a “density-equalised” map
projection

A map of the boundaries of counties and
electoral divisions – the areas are drawn
proportional to their physical size

By comparison, the more deprived
western area of Dublin, northern area
of Cork and south east of Limerick are
clearly apparent in the cartogram.

Haase’s deprivation index suggests that the
most deprived populations are in peripheral
rural areas in the north west.

Redrawing the data on the cartogram
reveals wide variation in the SMR, and also
uncovers the urban dimension which is
hidden in the conventional display

Patient data matched to Electoral Divisions –
hospital admissions for psychiatric problems – the
patchwork effect does not suggest any pattern

The cartogram reveals the urban nature
of the problem – those areas which are
more deprived are also those with
higher levels of illness/disability

Residents aged 15+ who are unable to work
through illness or disability: the map pattern
suggests that might be a largely rural
phenomenon

The roads, rail, and urban areas data have
been transformed into the same space as
the cartogram. Dublin and Cork seem
poorly provided with roads…

Other spatial information can be transformed
to the space of the density equalised
projection. Here is the familiar map of main
roads and towns.

Using a cartogram we see that the
incidence of lung cancer is randomly
spatially distributed

Lung cancer in males in New York State [Gastner & Newman: 2004]

Plotting locations of lung cancer cases, we
obtain a map which follows the underlying
population distribution – there appear to be
“clusters”

What about Ireland?
• Potentially hampered by challenge of
geocoding patient records
• Lack of small area population data
– EDs have an average population of 1150
– Wide variation in size (50-28000)
– Socially inhomogenous

Rural addresses
• A major challenge is non-uniqueness
of addresses in rural areas.
• For instance in Moy ED, 11 properties
share the address
‘Lackamore, Lahinch, Co. Clare’

What’s needed
• Postcoding should provide a
mechanism for geocoding patient data
with the granularity required for
planning the delivery of health care –
this has been the case in the UK for a
for long time
• Census data should be made available
for areas with small and similar
populations (cf: UK Output Areas)

Finally…
• GIS has an important role to play
in health studies
• Health data analysis is a rapidly
developing field
• However, in Ireland we are
hampered by data deficiencies –
now is the time for change

Maynooth: image in Google Earth

Central Dublin: image in Google Earth

Taoiseach’s Office: image in Google Earth

Downing Street, London: image in Google Earth

Maynooth: OSi orthophotograph added locally uisng KML

Sellafield: image in Google Earth

NCU – On Line Atlas of Cancer Mortality

The Philippine Institute
for Development
Studies makes an online atlas of socioeconomic indicators
available – contains
some health measures

The future
• Patient empowerment…

• GIS used to merge information from
regulatory agencies on 19196 landfill sites
• Sites buffered to 2km and postcodes
within buffer identified
• Postcoded live births (8.2x106), stillbirths
(43471) and congenital anomalies
(124,597) between 1982 and 1997
• Poisson regression modelling undertaken
• Evidence found for excess risk of
congenital anomaly and low birthweight

• Distance from each
census ward to
nearest hospital
computed
• Map shows number
of patients waiting
longer than 6
months for elective
inpatient care per
available and
unoccupied bed
within 60 minutes
travel time


Slide 11

GIS and Health
Martin Charlton1
Ronan Foley2
Dennis Pringle2
1National

Centre for Geocomputation
2Department of Geography
NUI Maynooth

Outline
• Preliminary observations on GIS
and health
• Examples of GIS in Health:
research and practice
• Mapping people not places

Patterns in Health Data
• Three main areas:
– Mapping of disease incidence
• Relative risk estimation

– Identification of disease clustering
• Surveillance

– Ecological analysis
• Relationship between incidence and
covariates
(Lawson et al, 2003)

Health Data
• Typically patient data is available
in two forms
• Individual – perhaps anonymised
to postcode/zip code level
• Aggregate – counts are available
for a set of zones
• The nature of the data affects the
nature of the analysis

Geographic Information Systems
• GIS is often confused with mapping
• A range of activities which includes
inter alia mapping and analysis
• Will typically involve several datasets
• May also involve several different
pieces of software

Mapping: EpiINFO
• This is a rather simple analysis and
mapping program
• It can be downloaded from the internet
• It doesn’t provide much in the way of
spatial data manipulation…
• … nor are its analytical capabilities very
advanced
• It is easy to use

Spatial Data Manipulation
• GIS isn’t merely concerned with
data display or query
• It’s useful in bringing together
different spatial datasets in the
first stages of an analysis
• This may well require a range of
different geometrical and
database operations

Analysis with Spatial Data
• Statistical analysis usually involves
some separate software.
• Spatial data have some properties
which mean that SPSS isn’t always
appropriate.
• There is now a range of specialist
software – much of it available for
download

GIS may be ideal for data manipulation, display and query,
but it’s not always ideal for analysis – in epidemiology a
variety of analytical software is available … WinBUGS,
GeoBUGS (MRC Biostatistics Unit, Cambridge)

Software to implement
particular methods – for
example scan statistics - can be
downloaded quickly and easily
from the Internet. Kulldorf’s
SaTScan system implements a
range of different scan
statistical models

GIS in Health in Practice
• Facility location – dialysis units
• Facility location – obs & gyn
• Data integration – schizophrenia
in Cavan
• Data manipulation and display –
GMS card holders in Maynooth

1: Provision of Dialysis Units
• There are 5 dialysis units for renal
patients in a region with a population
of about 3,000,000
• Major population centre has two units
about 5 miles apart
• Where would you locate an extra one?
• What would happen if one was
closed?

Location-allocation
• Location-allocation problems deal
with optimum location of facilities
• Attempt to minimise total
weighted travel time/distance
from users to facilities
• What data is needed?

Data requirements
• Digitised road network with classification
of road types
• Mean travel speeds on each type for
urban/non-urban roads
• Extents of urban areas
• Anonymised postcoded patient records
• Postcode->grid reference lookup
• Locations of hospitals in region (both with
and without dialysis units)

GIS processing
• Data integration
• Determine which segments of road network
are urban
• Determine travel times using road type
urban/non-urban speeds
• Tally numbers of patients at each node in
the road network
• Find nearest nodes in network to each
hospital
• Code hospitals as ‘existing’ or ‘potential’
units

Modelling/Analysis
• Carried out using standard GIS
functions
• Convenient ‘front end’ written
using system’s macro language
(save a lot of typing)
• Explore a range of alternatives

Hospital with
dialysis unit

Study region showing hospital locations and urban areas

Patients requiring dialysis (tallied to road nodes)

Classified road network – includes roads outside study area

•

Some alternative
scenarios:

(a) Optimal allocation
of patients
(b) Provide 1 extra
unit
(c) Provide 2 extra
units

(d) Close 1 unit in
city and reallocate
its patients

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

integrate data
process data
model and analyse
generate alternative scenarios
display the results

2: Facility Location
• Two hospitals both provided facilities
for obstetrics, gynaecology, paediatrics
and a special care baby unit.
• Was there a case for rationalisation and
improving the facilities at one hospital
only?
• Would closure disadvantage any
patients?

Data
• Locations of hospitals in study region
• Anonymised postcoded finished
consultant episodes for each of the
four specialities at each hospital
• Classified road network
• Speeds associated with each road
type

Data preparation
• Postcoded records allocated grid
references using Central Postcode
Directory (POSTZON)
• Vector data converted to raster
• Patient/specialty/hospital counts
produced for each raster
• Passage times computed for each
raster cell based on road
classification and speed estimates

Roads, urban areas, patient locations, hospitals in study area

Processing
• Travel time surfaces computed for
each hospital
• Each raster cell contains time
required to reach hospital in minutes
• Assumed walking speed in ‘non road’
areas

Travel time surface for hospital S – darker = shorter time

Travel time surface for hospital T

Time differences
• If we subtract the travel time surfaces areas
nearer one hospital will have positive
values for the difference, and areas nearer
the other will have negative ones
• Boundary between positive and negative
values is boundary of catchment for each
hospital.
• If you’re on the boundary (difference is
zero), it doesn’t matter which hospital you
choose

Roads, patient locations, hospital catchments

Indifference
• If we identify those rasters where the time
difference (ignoring the sign) is, say, 5 –
people in those areas are in the 5 minute
‘indifference’ zone.
• Patients might not be worried about a 5
minute increase in their travel time, they
might be worried about 30 minutes
• Indifference zones can be computed quite
easily

15-minute indifference zone between the two hospitals

Mean access time
• For each cell in the raster we can
compute the product of the number
of patients and the time required to
reach each hospital
• Dividing this by the number of
patients gives a patient weighted
average time for each hospital

Average travel times for the various specialties. There
isn’t much to chose between specialities with the
exception of paediatric episodes.

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

gather data
integrate data
process data
model data
display results

• All carried out using raster
operations

3: Schizophrenia in Cavan
• In 1996 Pringle, Waddington and
Youssef reported on evidence for the
causes of schizophrenia
• Ecological analysis of variation among
EDs
– Cases varied from 0-11 per ED
– Populations varied from 250-2750

• Poisson probabilities suggested that
one area had an unusually high number
of cases – these can be mapped

Male cases
superimposed on plot
of prevalence by ED

Male cases
superimposed on
rectified Landsat
image with ED
boundaries

4: GMS card holders: Maynooth
•Data on GMS – Medical Card holders
drawn under strict data protection
protocols from DOHC
• Anonymised address lists data matched
with GeoDirectory
•Address matching rate of around 77% in
Maynooth

• Interesting range of results around
average for the whole ED

•ED – single value 14.6 % (national average around 30%)
•Small Area – Range from 2% to 70% - noticeable internal variation and
good indicator of elderly or poor populations

•Strategic value in comparing datasets
•Important in policy analysis

Mapping invites comparison

Role of GIS
• Data integration
– Tally of cases in small areas requires
data on locations of cases and small
area boundaries
– ‘spatial join’ places small area codes
on the case records
– Counting is a standard database
operation

• Data display

Finally
• GIS encourages you to think about
space
• In a ‘conventional’ map of Ireland
areas (EDs, Counties…) are shown in
proportion to their physical size
• There are alternative ways of
displaying maps…

In a cartogram the areas are drawn
proportional to their populations –
this is a “density-equalised” map
projection

A map of the boundaries of counties and
electoral divisions – the areas are drawn
proportional to their physical size

By comparison, the more deprived
western area of Dublin, northern area
of Cork and south east of Limerick are
clearly apparent in the cartogram.

Haase’s deprivation index suggests that the
most deprived populations are in peripheral
rural areas in the north west.

Redrawing the data on the cartogram
reveals wide variation in the SMR, and also
uncovers the urban dimension which is
hidden in the conventional display

Patient data matched to Electoral Divisions –
hospital admissions for psychiatric problems – the
patchwork effect does not suggest any pattern

The cartogram reveals the urban nature
of the problem – those areas which are
more deprived are also those with
higher levels of illness/disability

Residents aged 15+ who are unable to work
through illness or disability: the map pattern
suggests that might be a largely rural
phenomenon

The roads, rail, and urban areas data have
been transformed into the same space as
the cartogram. Dublin and Cork seem
poorly provided with roads…

Other spatial information can be transformed
to the space of the density equalised
projection. Here is the familiar map of main
roads and towns.

Using a cartogram we see that the
incidence of lung cancer is randomly
spatially distributed

Lung cancer in males in New York State [Gastner & Newman: 2004]

Plotting locations of lung cancer cases, we
obtain a map which follows the underlying
population distribution – there appear to be
“clusters”

What about Ireland?
• Potentially hampered by challenge of
geocoding patient records
• Lack of small area population data
– EDs have an average population of 1150
– Wide variation in size (50-28000)
– Socially inhomogenous

Rural addresses
• A major challenge is non-uniqueness
of addresses in rural areas.
• For instance in Moy ED, 11 properties
share the address
‘Lackamore, Lahinch, Co. Clare’

What’s needed
• Postcoding should provide a
mechanism for geocoding patient data
with the granularity required for
planning the delivery of health care –
this has been the case in the UK for a
for long time
• Census data should be made available
for areas with small and similar
populations (cf: UK Output Areas)

Finally…
• GIS has an important role to play
in health studies
• Health data analysis is a rapidly
developing field
• However, in Ireland we are
hampered by data deficiencies –
now is the time for change

Maynooth: image in Google Earth

Central Dublin: image in Google Earth

Taoiseach’s Office: image in Google Earth

Downing Street, London: image in Google Earth

Maynooth: OSi orthophotograph added locally uisng KML

Sellafield: image in Google Earth

NCU – On Line Atlas of Cancer Mortality

The Philippine Institute
for Development
Studies makes an online atlas of socioeconomic indicators
available – contains
some health measures

The future
• Patient empowerment…

• GIS used to merge information from
regulatory agencies on 19196 landfill sites
• Sites buffered to 2km and postcodes
within buffer identified
• Postcoded live births (8.2x106), stillbirths
(43471) and congenital anomalies
(124,597) between 1982 and 1997
• Poisson regression modelling undertaken
• Evidence found for excess risk of
congenital anomaly and low birthweight

• Distance from each
census ward to
nearest hospital
computed
• Map shows number
of patients waiting
longer than 6
months for elective
inpatient care per
available and
unoccupied bed
within 60 minutes
travel time


Slide 12

GIS and Health
Martin Charlton1
Ronan Foley2
Dennis Pringle2
1National

Centre for Geocomputation
2Department of Geography
NUI Maynooth

Outline
• Preliminary observations on GIS
and health
• Examples of GIS in Health:
research and practice
• Mapping people not places

Patterns in Health Data
• Three main areas:
– Mapping of disease incidence
• Relative risk estimation

– Identification of disease clustering
• Surveillance

– Ecological analysis
• Relationship between incidence and
covariates
(Lawson et al, 2003)

Health Data
• Typically patient data is available
in two forms
• Individual – perhaps anonymised
to postcode/zip code level
• Aggregate – counts are available
for a set of zones
• The nature of the data affects the
nature of the analysis

Geographic Information Systems
• GIS is often confused with mapping
• A range of activities which includes
inter alia mapping and analysis
• Will typically involve several datasets
• May also involve several different
pieces of software

Mapping: EpiINFO
• This is a rather simple analysis and
mapping program
• It can be downloaded from the internet
• It doesn’t provide much in the way of
spatial data manipulation…
• … nor are its analytical capabilities very
advanced
• It is easy to use

Spatial Data Manipulation
• GIS isn’t merely concerned with
data display or query
• It’s useful in bringing together
different spatial datasets in the
first stages of an analysis
• This may well require a range of
different geometrical and
database operations

Analysis with Spatial Data
• Statistical analysis usually involves
some separate software.
• Spatial data have some properties
which mean that SPSS isn’t always
appropriate.
• There is now a range of specialist
software – much of it available for
download

GIS may be ideal for data manipulation, display and query,
but it’s not always ideal for analysis – in epidemiology a
variety of analytical software is available … WinBUGS,
GeoBUGS (MRC Biostatistics Unit, Cambridge)

Software to implement
particular methods – for
example scan statistics - can be
downloaded quickly and easily
from the Internet. Kulldorf’s
SaTScan system implements a
range of different scan
statistical models

GIS in Health in Practice
• Facility location – dialysis units
• Facility location – obs & gyn
• Data integration – schizophrenia
in Cavan
• Data manipulation and display –
GMS card holders in Maynooth

1: Provision of Dialysis Units
• There are 5 dialysis units for renal
patients in a region with a population
of about 3,000,000
• Major population centre has two units
about 5 miles apart
• Where would you locate an extra one?
• What would happen if one was
closed?

Location-allocation
• Location-allocation problems deal
with optimum location of facilities
• Attempt to minimise total
weighted travel time/distance
from users to facilities
• What data is needed?

Data requirements
• Digitised road network with classification
of road types
• Mean travel speeds on each type for
urban/non-urban roads
• Extents of urban areas
• Anonymised postcoded patient records
• Postcode->grid reference lookup
• Locations of hospitals in region (both with
and without dialysis units)

GIS processing
• Data integration
• Determine which segments of road network
are urban
• Determine travel times using road type
urban/non-urban speeds
• Tally numbers of patients at each node in
the road network
• Find nearest nodes in network to each
hospital
• Code hospitals as ‘existing’ or ‘potential’
units

Modelling/Analysis
• Carried out using standard GIS
functions
• Convenient ‘front end’ written
using system’s macro language
(save a lot of typing)
• Explore a range of alternatives

Hospital with
dialysis unit

Study region showing hospital locations and urban areas

Patients requiring dialysis (tallied to road nodes)

Classified road network – includes roads outside study area

•

Some alternative
scenarios:

(a) Optimal allocation
of patients
(b) Provide 1 extra
unit
(c) Provide 2 extra
units

(d) Close 1 unit in
city and reallocate
its patients

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

integrate data
process data
model and analyse
generate alternative scenarios
display the results

2: Facility Location
• Two hospitals both provided facilities
for obstetrics, gynaecology, paediatrics
and a special care baby unit.
• Was there a case for rationalisation and
improving the facilities at one hospital
only?
• Would closure disadvantage any
patients?

Data
• Locations of hospitals in study region
• Anonymised postcoded finished
consultant episodes for each of the
four specialities at each hospital
• Classified road network
• Speeds associated with each road
type

Data preparation
• Postcoded records allocated grid
references using Central Postcode
Directory (POSTZON)
• Vector data converted to raster
• Patient/specialty/hospital counts
produced for each raster
• Passage times computed for each
raster cell based on road
classification and speed estimates

Roads, urban areas, patient locations, hospitals in study area

Processing
• Travel time surfaces computed for
each hospital
• Each raster cell contains time
required to reach hospital in minutes
• Assumed walking speed in ‘non road’
areas

Travel time surface for hospital S – darker = shorter time

Travel time surface for hospital T

Time differences
• If we subtract the travel time surfaces areas
nearer one hospital will have positive
values for the difference, and areas nearer
the other will have negative ones
• Boundary between positive and negative
values is boundary of catchment for each
hospital.
• If you’re on the boundary (difference is
zero), it doesn’t matter which hospital you
choose

Roads, patient locations, hospital catchments

Indifference
• If we identify those rasters where the time
difference (ignoring the sign) is, say, 5 –
people in those areas are in the 5 minute
‘indifference’ zone.
• Patients might not be worried about a 5
minute increase in their travel time, they
might be worried about 30 minutes
• Indifference zones can be computed quite
easily

15-minute indifference zone between the two hospitals

Mean access time
• For each cell in the raster we can
compute the product of the number
of patients and the time required to
reach each hospital
• Dividing this by the number of
patients gives a patient weighted
average time for each hospital

Average travel times for the various specialties. There
isn’t much to chose between specialities with the
exception of paediatric episodes.

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

gather data
integrate data
process data
model data
display results

• All carried out using raster
operations

3: Schizophrenia in Cavan
• In 1996 Pringle, Waddington and
Youssef reported on evidence for the
causes of schizophrenia
• Ecological analysis of variation among
EDs
– Cases varied from 0-11 per ED
– Populations varied from 250-2750

• Poisson probabilities suggested that
one area had an unusually high number
of cases – these can be mapped

Male cases
superimposed on plot
of prevalence by ED

Male cases
superimposed on
rectified Landsat
image with ED
boundaries

4: GMS card holders: Maynooth
•Data on GMS – Medical Card holders
drawn under strict data protection
protocols from DOHC
• Anonymised address lists data matched
with GeoDirectory
•Address matching rate of around 77% in
Maynooth

• Interesting range of results around
average for the whole ED

•ED – single value 14.6 % (national average around 30%)
•Small Area – Range from 2% to 70% - noticeable internal variation and
good indicator of elderly or poor populations

•Strategic value in comparing datasets
•Important in policy analysis

Mapping invites comparison

Role of GIS
• Data integration
– Tally of cases in small areas requires
data on locations of cases and small
area boundaries
– ‘spatial join’ places small area codes
on the case records
– Counting is a standard database
operation

• Data display

Finally
• GIS encourages you to think about
space
• In a ‘conventional’ map of Ireland
areas (EDs, Counties…) are shown in
proportion to their physical size
• There are alternative ways of
displaying maps…

In a cartogram the areas are drawn
proportional to their populations –
this is a “density-equalised” map
projection

A map of the boundaries of counties and
electoral divisions – the areas are drawn
proportional to their physical size

By comparison, the more deprived
western area of Dublin, northern area
of Cork and south east of Limerick are
clearly apparent in the cartogram.

Haase’s deprivation index suggests that the
most deprived populations are in peripheral
rural areas in the north west.

Redrawing the data on the cartogram
reveals wide variation in the SMR, and also
uncovers the urban dimension which is
hidden in the conventional display

Patient data matched to Electoral Divisions –
hospital admissions for psychiatric problems – the
patchwork effect does not suggest any pattern

The cartogram reveals the urban nature
of the problem – those areas which are
more deprived are also those with
higher levels of illness/disability

Residents aged 15+ who are unable to work
through illness or disability: the map pattern
suggests that might be a largely rural
phenomenon

The roads, rail, and urban areas data have
been transformed into the same space as
the cartogram. Dublin and Cork seem
poorly provided with roads…

Other spatial information can be transformed
to the space of the density equalised
projection. Here is the familiar map of main
roads and towns.

Using a cartogram we see that the
incidence of lung cancer is randomly
spatially distributed

Lung cancer in males in New York State [Gastner & Newman: 2004]

Plotting locations of lung cancer cases, we
obtain a map which follows the underlying
population distribution – there appear to be
“clusters”

What about Ireland?
• Potentially hampered by challenge of
geocoding patient records
• Lack of small area population data
– EDs have an average population of 1150
– Wide variation in size (50-28000)
– Socially inhomogenous

Rural addresses
• A major challenge is non-uniqueness
of addresses in rural areas.
• For instance in Moy ED, 11 properties
share the address
‘Lackamore, Lahinch, Co. Clare’

What’s needed
• Postcoding should provide a
mechanism for geocoding patient data
with the granularity required for
planning the delivery of health care –
this has been the case in the UK for a
for long time
• Census data should be made available
for areas with small and similar
populations (cf: UK Output Areas)

Finally…
• GIS has an important role to play
in health studies
• Health data analysis is a rapidly
developing field
• However, in Ireland we are
hampered by data deficiencies –
now is the time for change

Maynooth: image in Google Earth

Central Dublin: image in Google Earth

Taoiseach’s Office: image in Google Earth

Downing Street, London: image in Google Earth

Maynooth: OSi orthophotograph added locally uisng KML

Sellafield: image in Google Earth

NCU – On Line Atlas of Cancer Mortality

The Philippine Institute
for Development
Studies makes an online atlas of socioeconomic indicators
available – contains
some health measures

The future
• Patient empowerment…

• GIS used to merge information from
regulatory agencies on 19196 landfill sites
• Sites buffered to 2km and postcodes
within buffer identified
• Postcoded live births (8.2x106), stillbirths
(43471) and congenital anomalies
(124,597) between 1982 and 1997
• Poisson regression modelling undertaken
• Evidence found for excess risk of
congenital anomaly and low birthweight

• Distance from each
census ward to
nearest hospital
computed
• Map shows number
of patients waiting
longer than 6
months for elective
inpatient care per
available and
unoccupied bed
within 60 minutes
travel time


Slide 13

GIS and Health
Martin Charlton1
Ronan Foley2
Dennis Pringle2
1National

Centre for Geocomputation
2Department of Geography
NUI Maynooth

Outline
• Preliminary observations on GIS
and health
• Examples of GIS in Health:
research and practice
• Mapping people not places

Patterns in Health Data
• Three main areas:
– Mapping of disease incidence
• Relative risk estimation

– Identification of disease clustering
• Surveillance

– Ecological analysis
• Relationship between incidence and
covariates
(Lawson et al, 2003)

Health Data
• Typically patient data is available
in two forms
• Individual – perhaps anonymised
to postcode/zip code level
• Aggregate – counts are available
for a set of zones
• The nature of the data affects the
nature of the analysis

Geographic Information Systems
• GIS is often confused with mapping
• A range of activities which includes
inter alia mapping and analysis
• Will typically involve several datasets
• May also involve several different
pieces of software

Mapping: EpiINFO
• This is a rather simple analysis and
mapping program
• It can be downloaded from the internet
• It doesn’t provide much in the way of
spatial data manipulation…
• … nor are its analytical capabilities very
advanced
• It is easy to use

Spatial Data Manipulation
• GIS isn’t merely concerned with
data display or query
• It’s useful in bringing together
different spatial datasets in the
first stages of an analysis
• This may well require a range of
different geometrical and
database operations

Analysis with Spatial Data
• Statistical analysis usually involves
some separate software.
• Spatial data have some properties
which mean that SPSS isn’t always
appropriate.
• There is now a range of specialist
software – much of it available for
download

GIS may be ideal for data manipulation, display and query,
but it’s not always ideal for analysis – in epidemiology a
variety of analytical software is available … WinBUGS,
GeoBUGS (MRC Biostatistics Unit, Cambridge)

Software to implement
particular methods – for
example scan statistics - can be
downloaded quickly and easily
from the Internet. Kulldorf’s
SaTScan system implements a
range of different scan
statistical models

GIS in Health in Practice
• Facility location – dialysis units
• Facility location – obs & gyn
• Data integration – schizophrenia
in Cavan
• Data manipulation and display –
GMS card holders in Maynooth

1: Provision of Dialysis Units
• There are 5 dialysis units for renal
patients in a region with a population
of about 3,000,000
• Major population centre has two units
about 5 miles apart
• Where would you locate an extra one?
• What would happen if one was
closed?

Location-allocation
• Location-allocation problems deal
with optimum location of facilities
• Attempt to minimise total
weighted travel time/distance
from users to facilities
• What data is needed?

Data requirements
• Digitised road network with classification
of road types
• Mean travel speeds on each type for
urban/non-urban roads
• Extents of urban areas
• Anonymised postcoded patient records
• Postcode->grid reference lookup
• Locations of hospitals in region (both with
and without dialysis units)

GIS processing
• Data integration
• Determine which segments of road network
are urban
• Determine travel times using road type
urban/non-urban speeds
• Tally numbers of patients at each node in
the road network
• Find nearest nodes in network to each
hospital
• Code hospitals as ‘existing’ or ‘potential’
units

Modelling/Analysis
• Carried out using standard GIS
functions
• Convenient ‘front end’ written
using system’s macro language
(save a lot of typing)
• Explore a range of alternatives

Hospital with
dialysis unit

Study region showing hospital locations and urban areas

Patients requiring dialysis (tallied to road nodes)

Classified road network – includes roads outside study area

•

Some alternative
scenarios:

(a) Optimal allocation
of patients
(b) Provide 1 extra
unit
(c) Provide 2 extra
units

(d) Close 1 unit in
city and reallocate
its patients

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

integrate data
process data
model and analyse
generate alternative scenarios
display the results

2: Facility Location
• Two hospitals both provided facilities
for obstetrics, gynaecology, paediatrics
and a special care baby unit.
• Was there a case for rationalisation and
improving the facilities at one hospital
only?
• Would closure disadvantage any
patients?

Data
• Locations of hospitals in study region
• Anonymised postcoded finished
consultant episodes for each of the
four specialities at each hospital
• Classified road network
• Speeds associated with each road
type

Data preparation
• Postcoded records allocated grid
references using Central Postcode
Directory (POSTZON)
• Vector data converted to raster
• Patient/specialty/hospital counts
produced for each raster
• Passage times computed for each
raster cell based on road
classification and speed estimates

Roads, urban areas, patient locations, hospitals in study area

Processing
• Travel time surfaces computed for
each hospital
• Each raster cell contains time
required to reach hospital in minutes
• Assumed walking speed in ‘non road’
areas

Travel time surface for hospital S – darker = shorter time

Travel time surface for hospital T

Time differences
• If we subtract the travel time surfaces areas
nearer one hospital will have positive
values for the difference, and areas nearer
the other will have negative ones
• Boundary between positive and negative
values is boundary of catchment for each
hospital.
• If you’re on the boundary (difference is
zero), it doesn’t matter which hospital you
choose

Roads, patient locations, hospital catchments

Indifference
• If we identify those rasters where the time
difference (ignoring the sign) is, say, 5 –
people in those areas are in the 5 minute
‘indifference’ zone.
• Patients might not be worried about a 5
minute increase in their travel time, they
might be worried about 30 minutes
• Indifference zones can be computed quite
easily

15-minute indifference zone between the two hospitals

Mean access time
• For each cell in the raster we can
compute the product of the number
of patients and the time required to
reach each hospital
• Dividing this by the number of
patients gives a patient weighted
average time for each hospital

Average travel times for the various specialties. There
isn’t much to chose between specialities with the
exception of paediatric episodes.

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

gather data
integrate data
process data
model data
display results

• All carried out using raster
operations

3: Schizophrenia in Cavan
• In 1996 Pringle, Waddington and
Youssef reported on evidence for the
causes of schizophrenia
• Ecological analysis of variation among
EDs
– Cases varied from 0-11 per ED
– Populations varied from 250-2750

• Poisson probabilities suggested that
one area had an unusually high number
of cases – these can be mapped

Male cases
superimposed on plot
of prevalence by ED

Male cases
superimposed on
rectified Landsat
image with ED
boundaries

4: GMS card holders: Maynooth
•Data on GMS – Medical Card holders
drawn under strict data protection
protocols from DOHC
• Anonymised address lists data matched
with GeoDirectory
•Address matching rate of around 77% in
Maynooth

• Interesting range of results around
average for the whole ED

•ED – single value 14.6 % (national average around 30%)
•Small Area – Range from 2% to 70% - noticeable internal variation and
good indicator of elderly or poor populations

•Strategic value in comparing datasets
•Important in policy analysis

Mapping invites comparison

Role of GIS
• Data integration
– Tally of cases in small areas requires
data on locations of cases and small
area boundaries
– ‘spatial join’ places small area codes
on the case records
– Counting is a standard database
operation

• Data display

Finally
• GIS encourages you to think about
space
• In a ‘conventional’ map of Ireland
areas (EDs, Counties…) are shown in
proportion to their physical size
• There are alternative ways of
displaying maps…

In a cartogram the areas are drawn
proportional to their populations –
this is a “density-equalised” map
projection

A map of the boundaries of counties and
electoral divisions – the areas are drawn
proportional to their physical size

By comparison, the more deprived
western area of Dublin, northern area
of Cork and south east of Limerick are
clearly apparent in the cartogram.

Haase’s deprivation index suggests that the
most deprived populations are in peripheral
rural areas in the north west.

Redrawing the data on the cartogram
reveals wide variation in the SMR, and also
uncovers the urban dimension which is
hidden in the conventional display

Patient data matched to Electoral Divisions –
hospital admissions for psychiatric problems – the
patchwork effect does not suggest any pattern

The cartogram reveals the urban nature
of the problem – those areas which are
more deprived are also those with
higher levels of illness/disability

Residents aged 15+ who are unable to work
through illness or disability: the map pattern
suggests that might be a largely rural
phenomenon

The roads, rail, and urban areas data have
been transformed into the same space as
the cartogram. Dublin and Cork seem
poorly provided with roads…

Other spatial information can be transformed
to the space of the density equalised
projection. Here is the familiar map of main
roads and towns.

Using a cartogram we see that the
incidence of lung cancer is randomly
spatially distributed

Lung cancer in males in New York State [Gastner & Newman: 2004]

Plotting locations of lung cancer cases, we
obtain a map which follows the underlying
population distribution – there appear to be
“clusters”

What about Ireland?
• Potentially hampered by challenge of
geocoding patient records
• Lack of small area population data
– EDs have an average population of 1150
– Wide variation in size (50-28000)
– Socially inhomogenous

Rural addresses
• A major challenge is non-uniqueness
of addresses in rural areas.
• For instance in Moy ED, 11 properties
share the address
‘Lackamore, Lahinch, Co. Clare’

What’s needed
• Postcoding should provide a
mechanism for geocoding patient data
with the granularity required for
planning the delivery of health care –
this has been the case in the UK for a
for long time
• Census data should be made available
for areas with small and similar
populations (cf: UK Output Areas)

Finally…
• GIS has an important role to play
in health studies
• Health data analysis is a rapidly
developing field
• However, in Ireland we are
hampered by data deficiencies –
now is the time for change

Maynooth: image in Google Earth

Central Dublin: image in Google Earth

Taoiseach’s Office: image in Google Earth

Downing Street, London: image in Google Earth

Maynooth: OSi orthophotograph added locally uisng KML

Sellafield: image in Google Earth

NCU – On Line Atlas of Cancer Mortality

The Philippine Institute
for Development
Studies makes an online atlas of socioeconomic indicators
available – contains
some health measures

The future
• Patient empowerment…

• GIS used to merge information from
regulatory agencies on 19196 landfill sites
• Sites buffered to 2km and postcodes
within buffer identified
• Postcoded live births (8.2x106), stillbirths
(43471) and congenital anomalies
(124,597) between 1982 and 1997
• Poisson regression modelling undertaken
• Evidence found for excess risk of
congenital anomaly and low birthweight

• Distance from each
census ward to
nearest hospital
computed
• Map shows number
of patients waiting
longer than 6
months for elective
inpatient care per
available and
unoccupied bed
within 60 minutes
travel time


Slide 14

GIS and Health
Martin Charlton1
Ronan Foley2
Dennis Pringle2
1National

Centre for Geocomputation
2Department of Geography
NUI Maynooth

Outline
• Preliminary observations on GIS
and health
• Examples of GIS in Health:
research and practice
• Mapping people not places

Patterns in Health Data
• Three main areas:
– Mapping of disease incidence
• Relative risk estimation

– Identification of disease clustering
• Surveillance

– Ecological analysis
• Relationship between incidence and
covariates
(Lawson et al, 2003)

Health Data
• Typically patient data is available
in two forms
• Individual – perhaps anonymised
to postcode/zip code level
• Aggregate – counts are available
for a set of zones
• The nature of the data affects the
nature of the analysis

Geographic Information Systems
• GIS is often confused with mapping
• A range of activities which includes
inter alia mapping and analysis
• Will typically involve several datasets
• May also involve several different
pieces of software

Mapping: EpiINFO
• This is a rather simple analysis and
mapping program
• It can be downloaded from the internet
• It doesn’t provide much in the way of
spatial data manipulation…
• … nor are its analytical capabilities very
advanced
• It is easy to use

Spatial Data Manipulation
• GIS isn’t merely concerned with
data display or query
• It’s useful in bringing together
different spatial datasets in the
first stages of an analysis
• This may well require a range of
different geometrical and
database operations

Analysis with Spatial Data
• Statistical analysis usually involves
some separate software.
• Spatial data have some properties
which mean that SPSS isn’t always
appropriate.
• There is now a range of specialist
software – much of it available for
download

GIS may be ideal for data manipulation, display and query,
but it’s not always ideal for analysis – in epidemiology a
variety of analytical software is available … WinBUGS,
GeoBUGS (MRC Biostatistics Unit, Cambridge)

Software to implement
particular methods – for
example scan statistics - can be
downloaded quickly and easily
from the Internet. Kulldorf’s
SaTScan system implements a
range of different scan
statistical models

GIS in Health in Practice
• Facility location – dialysis units
• Facility location – obs & gyn
• Data integration – schizophrenia
in Cavan
• Data manipulation and display –
GMS card holders in Maynooth

1: Provision of Dialysis Units
• There are 5 dialysis units for renal
patients in a region with a population
of about 3,000,000
• Major population centre has two units
about 5 miles apart
• Where would you locate an extra one?
• What would happen if one was
closed?

Location-allocation
• Location-allocation problems deal
with optimum location of facilities
• Attempt to minimise total
weighted travel time/distance
from users to facilities
• What data is needed?

Data requirements
• Digitised road network with classification
of road types
• Mean travel speeds on each type for
urban/non-urban roads
• Extents of urban areas
• Anonymised postcoded patient records
• Postcode->grid reference lookup
• Locations of hospitals in region (both with
and without dialysis units)

GIS processing
• Data integration
• Determine which segments of road network
are urban
• Determine travel times using road type
urban/non-urban speeds
• Tally numbers of patients at each node in
the road network
• Find nearest nodes in network to each
hospital
• Code hospitals as ‘existing’ or ‘potential’
units

Modelling/Analysis
• Carried out using standard GIS
functions
• Convenient ‘front end’ written
using system’s macro language
(save a lot of typing)
• Explore a range of alternatives

Hospital with
dialysis unit

Study region showing hospital locations and urban areas

Patients requiring dialysis (tallied to road nodes)

Classified road network – includes roads outside study area

•

Some alternative
scenarios:

(a) Optimal allocation
of patients
(b) Provide 1 extra
unit
(c) Provide 2 extra
units

(d) Close 1 unit in
city and reallocate
its patients

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

integrate data
process data
model and analyse
generate alternative scenarios
display the results

2: Facility Location
• Two hospitals both provided facilities
for obstetrics, gynaecology, paediatrics
and a special care baby unit.
• Was there a case for rationalisation and
improving the facilities at one hospital
only?
• Would closure disadvantage any
patients?

Data
• Locations of hospitals in study region
• Anonymised postcoded finished
consultant episodes for each of the
four specialities at each hospital
• Classified road network
• Speeds associated with each road
type

Data preparation
• Postcoded records allocated grid
references using Central Postcode
Directory (POSTZON)
• Vector data converted to raster
• Patient/specialty/hospital counts
produced for each raster
• Passage times computed for each
raster cell based on road
classification and speed estimates

Roads, urban areas, patient locations, hospitals in study area

Processing
• Travel time surfaces computed for
each hospital
• Each raster cell contains time
required to reach hospital in minutes
• Assumed walking speed in ‘non road’
areas

Travel time surface for hospital S – darker = shorter time

Travel time surface for hospital T

Time differences
• If we subtract the travel time surfaces areas
nearer one hospital will have positive
values for the difference, and areas nearer
the other will have negative ones
• Boundary between positive and negative
values is boundary of catchment for each
hospital.
• If you’re on the boundary (difference is
zero), it doesn’t matter which hospital you
choose

Roads, patient locations, hospital catchments

Indifference
• If we identify those rasters where the time
difference (ignoring the sign) is, say, 5 –
people in those areas are in the 5 minute
‘indifference’ zone.
• Patients might not be worried about a 5
minute increase in their travel time, they
might be worried about 30 minutes
• Indifference zones can be computed quite
easily

15-minute indifference zone between the two hospitals

Mean access time
• For each cell in the raster we can
compute the product of the number
of patients and the time required to
reach each hospital
• Dividing this by the number of
patients gives a patient weighted
average time for each hospital

Average travel times for the various specialties. There
isn’t much to chose between specialities with the
exception of paediatric episodes.

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

gather data
integrate data
process data
model data
display results

• All carried out using raster
operations

3: Schizophrenia in Cavan
• In 1996 Pringle, Waddington and
Youssef reported on evidence for the
causes of schizophrenia
• Ecological analysis of variation among
EDs
– Cases varied from 0-11 per ED
– Populations varied from 250-2750

• Poisson probabilities suggested that
one area had an unusually high number
of cases – these can be mapped

Male cases
superimposed on plot
of prevalence by ED

Male cases
superimposed on
rectified Landsat
image with ED
boundaries

4: GMS card holders: Maynooth
•Data on GMS – Medical Card holders
drawn under strict data protection
protocols from DOHC
• Anonymised address lists data matched
with GeoDirectory
•Address matching rate of around 77% in
Maynooth

• Interesting range of results around
average for the whole ED

•ED – single value 14.6 % (national average around 30%)
•Small Area – Range from 2% to 70% - noticeable internal variation and
good indicator of elderly or poor populations

•Strategic value in comparing datasets
•Important in policy analysis

Mapping invites comparison

Role of GIS
• Data integration
– Tally of cases in small areas requires
data on locations of cases and small
area boundaries
– ‘spatial join’ places small area codes
on the case records
– Counting is a standard database
operation

• Data display

Finally
• GIS encourages you to think about
space
• In a ‘conventional’ map of Ireland
areas (EDs, Counties…) are shown in
proportion to their physical size
• There are alternative ways of
displaying maps…

In a cartogram the areas are drawn
proportional to their populations –
this is a “density-equalised” map
projection

A map of the boundaries of counties and
electoral divisions – the areas are drawn
proportional to their physical size

By comparison, the more deprived
western area of Dublin, northern area
of Cork and south east of Limerick are
clearly apparent in the cartogram.

Haase’s deprivation index suggests that the
most deprived populations are in peripheral
rural areas in the north west.

Redrawing the data on the cartogram
reveals wide variation in the SMR, and also
uncovers the urban dimension which is
hidden in the conventional display

Patient data matched to Electoral Divisions –
hospital admissions for psychiatric problems – the
patchwork effect does not suggest any pattern

The cartogram reveals the urban nature
of the problem – those areas which are
more deprived are also those with
higher levels of illness/disability

Residents aged 15+ who are unable to work
through illness or disability: the map pattern
suggests that might be a largely rural
phenomenon

The roads, rail, and urban areas data have
been transformed into the same space as
the cartogram. Dublin and Cork seem
poorly provided with roads…

Other spatial information can be transformed
to the space of the density equalised
projection. Here is the familiar map of main
roads and towns.

Using a cartogram we see that the
incidence of lung cancer is randomly
spatially distributed

Lung cancer in males in New York State [Gastner & Newman: 2004]

Plotting locations of lung cancer cases, we
obtain a map which follows the underlying
population distribution – there appear to be
“clusters”

What about Ireland?
• Potentially hampered by challenge of
geocoding patient records
• Lack of small area population data
– EDs have an average population of 1150
– Wide variation in size (50-28000)
– Socially inhomogenous

Rural addresses
• A major challenge is non-uniqueness
of addresses in rural areas.
• For instance in Moy ED, 11 properties
share the address
‘Lackamore, Lahinch, Co. Clare’

What’s needed
• Postcoding should provide a
mechanism for geocoding patient data
with the granularity required for
planning the delivery of health care –
this has been the case in the UK for a
for long time
• Census data should be made available
for areas with small and similar
populations (cf: UK Output Areas)

Finally…
• GIS has an important role to play
in health studies
• Health data analysis is a rapidly
developing field
• However, in Ireland we are
hampered by data deficiencies –
now is the time for change

Maynooth: image in Google Earth

Central Dublin: image in Google Earth

Taoiseach’s Office: image in Google Earth

Downing Street, London: image in Google Earth

Maynooth: OSi orthophotograph added locally uisng KML

Sellafield: image in Google Earth

NCU – On Line Atlas of Cancer Mortality

The Philippine Institute
for Development
Studies makes an online atlas of socioeconomic indicators
available – contains
some health measures

The future
• Patient empowerment…

• GIS used to merge information from
regulatory agencies on 19196 landfill sites
• Sites buffered to 2km and postcodes
within buffer identified
• Postcoded live births (8.2x106), stillbirths
(43471) and congenital anomalies
(124,597) between 1982 and 1997
• Poisson regression modelling undertaken
• Evidence found for excess risk of
congenital anomaly and low birthweight

• Distance from each
census ward to
nearest hospital
computed
• Map shows number
of patients waiting
longer than 6
months for elective
inpatient care per
available and
unoccupied bed
within 60 minutes
travel time


Slide 15

GIS and Health
Martin Charlton1
Ronan Foley2
Dennis Pringle2
1National

Centre for Geocomputation
2Department of Geography
NUI Maynooth

Outline
• Preliminary observations on GIS
and health
• Examples of GIS in Health:
research and practice
• Mapping people not places

Patterns in Health Data
• Three main areas:
– Mapping of disease incidence
• Relative risk estimation

– Identification of disease clustering
• Surveillance

– Ecological analysis
• Relationship between incidence and
covariates
(Lawson et al, 2003)

Health Data
• Typically patient data is available
in two forms
• Individual – perhaps anonymised
to postcode/zip code level
• Aggregate – counts are available
for a set of zones
• The nature of the data affects the
nature of the analysis

Geographic Information Systems
• GIS is often confused with mapping
• A range of activities which includes
inter alia mapping and analysis
• Will typically involve several datasets
• May also involve several different
pieces of software

Mapping: EpiINFO
• This is a rather simple analysis and
mapping program
• It can be downloaded from the internet
• It doesn’t provide much in the way of
spatial data manipulation…
• … nor are its analytical capabilities very
advanced
• It is easy to use

Spatial Data Manipulation
• GIS isn’t merely concerned with
data display or query
• It’s useful in bringing together
different spatial datasets in the
first stages of an analysis
• This may well require a range of
different geometrical and
database operations

Analysis with Spatial Data
• Statistical analysis usually involves
some separate software.
• Spatial data have some properties
which mean that SPSS isn’t always
appropriate.
• There is now a range of specialist
software – much of it available for
download

GIS may be ideal for data manipulation, display and query,
but it’s not always ideal for analysis – in epidemiology a
variety of analytical software is available … WinBUGS,
GeoBUGS (MRC Biostatistics Unit, Cambridge)

Software to implement
particular methods – for
example scan statistics - can be
downloaded quickly and easily
from the Internet. Kulldorf’s
SaTScan system implements a
range of different scan
statistical models

GIS in Health in Practice
• Facility location – dialysis units
• Facility location – obs & gyn
• Data integration – schizophrenia
in Cavan
• Data manipulation and display –
GMS card holders in Maynooth

1: Provision of Dialysis Units
• There are 5 dialysis units for renal
patients in a region with a population
of about 3,000,000
• Major population centre has two units
about 5 miles apart
• Where would you locate an extra one?
• What would happen if one was
closed?

Location-allocation
• Location-allocation problems deal
with optimum location of facilities
• Attempt to minimise total
weighted travel time/distance
from users to facilities
• What data is needed?

Data requirements
• Digitised road network with classification
of road types
• Mean travel speeds on each type for
urban/non-urban roads
• Extents of urban areas
• Anonymised postcoded patient records
• Postcode->grid reference lookup
• Locations of hospitals in region (both with
and without dialysis units)

GIS processing
• Data integration
• Determine which segments of road network
are urban
• Determine travel times using road type
urban/non-urban speeds
• Tally numbers of patients at each node in
the road network
• Find nearest nodes in network to each
hospital
• Code hospitals as ‘existing’ or ‘potential’
units

Modelling/Analysis
• Carried out using standard GIS
functions
• Convenient ‘front end’ written
using system’s macro language
(save a lot of typing)
• Explore a range of alternatives

Hospital with
dialysis unit

Study region showing hospital locations and urban areas

Patients requiring dialysis (tallied to road nodes)

Classified road network – includes roads outside study area

•

Some alternative
scenarios:

(a) Optimal allocation
of patients
(b) Provide 1 extra
unit
(c) Provide 2 extra
units

(d) Close 1 unit in
city and reallocate
its patients

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

integrate data
process data
model and analyse
generate alternative scenarios
display the results

2: Facility Location
• Two hospitals both provided facilities
for obstetrics, gynaecology, paediatrics
and a special care baby unit.
• Was there a case for rationalisation and
improving the facilities at one hospital
only?
• Would closure disadvantage any
patients?

Data
• Locations of hospitals in study region
• Anonymised postcoded finished
consultant episodes for each of the
four specialities at each hospital
• Classified road network
• Speeds associated with each road
type

Data preparation
• Postcoded records allocated grid
references using Central Postcode
Directory (POSTZON)
• Vector data converted to raster
• Patient/specialty/hospital counts
produced for each raster
• Passage times computed for each
raster cell based on road
classification and speed estimates

Roads, urban areas, patient locations, hospitals in study area

Processing
• Travel time surfaces computed for
each hospital
• Each raster cell contains time
required to reach hospital in minutes
• Assumed walking speed in ‘non road’
areas

Travel time surface for hospital S – darker = shorter time

Travel time surface for hospital T

Time differences
• If we subtract the travel time surfaces areas
nearer one hospital will have positive
values for the difference, and areas nearer
the other will have negative ones
• Boundary between positive and negative
values is boundary of catchment for each
hospital.
• If you’re on the boundary (difference is
zero), it doesn’t matter which hospital you
choose

Roads, patient locations, hospital catchments

Indifference
• If we identify those rasters where the time
difference (ignoring the sign) is, say, 5 –
people in those areas are in the 5 minute
‘indifference’ zone.
• Patients might not be worried about a 5
minute increase in their travel time, they
might be worried about 30 minutes
• Indifference zones can be computed quite
easily

15-minute indifference zone between the two hospitals

Mean access time
• For each cell in the raster we can
compute the product of the number
of patients and the time required to
reach each hospital
• Dividing this by the number of
patients gives a patient weighted
average time for each hospital

Average travel times for the various specialties. There
isn’t much to chose between specialities with the
exception of paediatric episodes.

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

gather data
integrate data
process data
model data
display results

• All carried out using raster
operations

3: Schizophrenia in Cavan
• In 1996 Pringle, Waddington and
Youssef reported on evidence for the
causes of schizophrenia
• Ecological analysis of variation among
EDs
– Cases varied from 0-11 per ED
– Populations varied from 250-2750

• Poisson probabilities suggested that
one area had an unusually high number
of cases – these can be mapped

Male cases
superimposed on plot
of prevalence by ED

Male cases
superimposed on
rectified Landsat
image with ED
boundaries

4: GMS card holders: Maynooth
•Data on GMS – Medical Card holders
drawn under strict data protection
protocols from DOHC
• Anonymised address lists data matched
with GeoDirectory
•Address matching rate of around 77% in
Maynooth

• Interesting range of results around
average for the whole ED

•ED – single value 14.6 % (national average around 30%)
•Small Area – Range from 2% to 70% - noticeable internal variation and
good indicator of elderly or poor populations

•Strategic value in comparing datasets
•Important in policy analysis

Mapping invites comparison

Role of GIS
• Data integration
– Tally of cases in small areas requires
data on locations of cases and small
area boundaries
– ‘spatial join’ places small area codes
on the case records
– Counting is a standard database
operation

• Data display

Finally
• GIS encourages you to think about
space
• In a ‘conventional’ map of Ireland
areas (EDs, Counties…) are shown in
proportion to their physical size
• There are alternative ways of
displaying maps…

In a cartogram the areas are drawn
proportional to their populations –
this is a “density-equalised” map
projection

A map of the boundaries of counties and
electoral divisions – the areas are drawn
proportional to their physical size

By comparison, the more deprived
western area of Dublin, northern area
of Cork and south east of Limerick are
clearly apparent in the cartogram.

Haase’s deprivation index suggests that the
most deprived populations are in peripheral
rural areas in the north west.

Redrawing the data on the cartogram
reveals wide variation in the SMR, and also
uncovers the urban dimension which is
hidden in the conventional display

Patient data matched to Electoral Divisions –
hospital admissions for psychiatric problems – the
patchwork effect does not suggest any pattern

The cartogram reveals the urban nature
of the problem – those areas which are
more deprived are also those with
higher levels of illness/disability

Residents aged 15+ who are unable to work
through illness or disability: the map pattern
suggests that might be a largely rural
phenomenon

The roads, rail, and urban areas data have
been transformed into the same space as
the cartogram. Dublin and Cork seem
poorly provided with roads…

Other spatial information can be transformed
to the space of the density equalised
projection. Here is the familiar map of main
roads and towns.

Using a cartogram we see that the
incidence of lung cancer is randomly
spatially distributed

Lung cancer in males in New York State [Gastner & Newman: 2004]

Plotting locations of lung cancer cases, we
obtain a map which follows the underlying
population distribution – there appear to be
“clusters”

What about Ireland?
• Potentially hampered by challenge of
geocoding patient records
• Lack of small area population data
– EDs have an average population of 1150
– Wide variation in size (50-28000)
– Socially inhomogenous

Rural addresses
• A major challenge is non-uniqueness
of addresses in rural areas.
• For instance in Moy ED, 11 properties
share the address
‘Lackamore, Lahinch, Co. Clare’

What’s needed
• Postcoding should provide a
mechanism for geocoding patient data
with the granularity required for
planning the delivery of health care –
this has been the case in the UK for a
for long time
• Census data should be made available
for areas with small and similar
populations (cf: UK Output Areas)

Finally…
• GIS has an important role to play
in health studies
• Health data analysis is a rapidly
developing field
• However, in Ireland we are
hampered by data deficiencies –
now is the time for change

Maynooth: image in Google Earth

Central Dublin: image in Google Earth

Taoiseach’s Office: image in Google Earth

Downing Street, London: image in Google Earth

Maynooth: OSi orthophotograph added locally uisng KML

Sellafield: image in Google Earth

NCU – On Line Atlas of Cancer Mortality

The Philippine Institute
for Development
Studies makes an online atlas of socioeconomic indicators
available – contains
some health measures

The future
• Patient empowerment…

• GIS used to merge information from
regulatory agencies on 19196 landfill sites
• Sites buffered to 2km and postcodes
within buffer identified
• Postcoded live births (8.2x106), stillbirths
(43471) and congenital anomalies
(124,597) between 1982 and 1997
• Poisson regression modelling undertaken
• Evidence found for excess risk of
congenital anomaly and low birthweight

• Distance from each
census ward to
nearest hospital
computed
• Map shows number
of patients waiting
longer than 6
months for elective
inpatient care per
available and
unoccupied bed
within 60 minutes
travel time


Slide 16

GIS and Health
Martin Charlton1
Ronan Foley2
Dennis Pringle2
1National

Centre for Geocomputation
2Department of Geography
NUI Maynooth

Outline
• Preliminary observations on GIS
and health
• Examples of GIS in Health:
research and practice
• Mapping people not places

Patterns in Health Data
• Three main areas:
– Mapping of disease incidence
• Relative risk estimation

– Identification of disease clustering
• Surveillance

– Ecological analysis
• Relationship between incidence and
covariates
(Lawson et al, 2003)

Health Data
• Typically patient data is available
in two forms
• Individual – perhaps anonymised
to postcode/zip code level
• Aggregate – counts are available
for a set of zones
• The nature of the data affects the
nature of the analysis

Geographic Information Systems
• GIS is often confused with mapping
• A range of activities which includes
inter alia mapping and analysis
• Will typically involve several datasets
• May also involve several different
pieces of software

Mapping: EpiINFO
• This is a rather simple analysis and
mapping program
• It can be downloaded from the internet
• It doesn’t provide much in the way of
spatial data manipulation…
• … nor are its analytical capabilities very
advanced
• It is easy to use

Spatial Data Manipulation
• GIS isn’t merely concerned with
data display or query
• It’s useful in bringing together
different spatial datasets in the
first stages of an analysis
• This may well require a range of
different geometrical and
database operations

Analysis with Spatial Data
• Statistical analysis usually involves
some separate software.
• Spatial data have some properties
which mean that SPSS isn’t always
appropriate.
• There is now a range of specialist
software – much of it available for
download

GIS may be ideal for data manipulation, display and query,
but it’s not always ideal for analysis – in epidemiology a
variety of analytical software is available … WinBUGS,
GeoBUGS (MRC Biostatistics Unit, Cambridge)

Software to implement
particular methods – for
example scan statistics - can be
downloaded quickly and easily
from the Internet. Kulldorf’s
SaTScan system implements a
range of different scan
statistical models

GIS in Health in Practice
• Facility location – dialysis units
• Facility location – obs & gyn
• Data integration – schizophrenia
in Cavan
• Data manipulation and display –
GMS card holders in Maynooth

1: Provision of Dialysis Units
• There are 5 dialysis units for renal
patients in a region with a population
of about 3,000,000
• Major population centre has two units
about 5 miles apart
• Where would you locate an extra one?
• What would happen if one was
closed?

Location-allocation
• Location-allocation problems deal
with optimum location of facilities
• Attempt to minimise total
weighted travel time/distance
from users to facilities
• What data is needed?

Data requirements
• Digitised road network with classification
of road types
• Mean travel speeds on each type for
urban/non-urban roads
• Extents of urban areas
• Anonymised postcoded patient records
• Postcode->grid reference lookup
• Locations of hospitals in region (both with
and without dialysis units)

GIS processing
• Data integration
• Determine which segments of road network
are urban
• Determine travel times using road type
urban/non-urban speeds
• Tally numbers of patients at each node in
the road network
• Find nearest nodes in network to each
hospital
• Code hospitals as ‘existing’ or ‘potential’
units

Modelling/Analysis
• Carried out using standard GIS
functions
• Convenient ‘front end’ written
using system’s macro language
(save a lot of typing)
• Explore a range of alternatives

Hospital with
dialysis unit

Study region showing hospital locations and urban areas

Patients requiring dialysis (tallied to road nodes)

Classified road network – includes roads outside study area

•

Some alternative
scenarios:

(a) Optimal allocation
of patients
(b) Provide 1 extra
unit
(c) Provide 2 extra
units

(d) Close 1 unit in
city and reallocate
its patients

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

integrate data
process data
model and analyse
generate alternative scenarios
display the results

2: Facility Location
• Two hospitals both provided facilities
for obstetrics, gynaecology, paediatrics
and a special care baby unit.
• Was there a case for rationalisation and
improving the facilities at one hospital
only?
• Would closure disadvantage any
patients?

Data
• Locations of hospitals in study region
• Anonymised postcoded finished
consultant episodes for each of the
four specialities at each hospital
• Classified road network
• Speeds associated with each road
type

Data preparation
• Postcoded records allocated grid
references using Central Postcode
Directory (POSTZON)
• Vector data converted to raster
• Patient/specialty/hospital counts
produced for each raster
• Passage times computed for each
raster cell based on road
classification and speed estimates

Roads, urban areas, patient locations, hospitals in study area

Processing
• Travel time surfaces computed for
each hospital
• Each raster cell contains time
required to reach hospital in minutes
• Assumed walking speed in ‘non road’
areas

Travel time surface for hospital S – darker = shorter time

Travel time surface for hospital T

Time differences
• If we subtract the travel time surfaces areas
nearer one hospital will have positive
values for the difference, and areas nearer
the other will have negative ones
• Boundary between positive and negative
values is boundary of catchment for each
hospital.
• If you’re on the boundary (difference is
zero), it doesn’t matter which hospital you
choose

Roads, patient locations, hospital catchments

Indifference
• If we identify those rasters where the time
difference (ignoring the sign) is, say, 5 –
people in those areas are in the 5 minute
‘indifference’ zone.
• Patients might not be worried about a 5
minute increase in their travel time, they
might be worried about 30 minutes
• Indifference zones can be computed quite
easily

15-minute indifference zone between the two hospitals

Mean access time
• For each cell in the raster we can
compute the product of the number
of patients and the time required to
reach each hospital
• Dividing this by the number of
patients gives a patient weighted
average time for each hospital

Average travel times for the various specialties. There
isn’t much to chose between specialities with the
exception of paediatric episodes.

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

gather data
integrate data
process data
model data
display results

• All carried out using raster
operations

3: Schizophrenia in Cavan
• In 1996 Pringle, Waddington and
Youssef reported on evidence for the
causes of schizophrenia
• Ecological analysis of variation among
EDs
– Cases varied from 0-11 per ED
– Populations varied from 250-2750

• Poisson probabilities suggested that
one area had an unusually high number
of cases – these can be mapped

Male cases
superimposed on plot
of prevalence by ED

Male cases
superimposed on
rectified Landsat
image with ED
boundaries

4: GMS card holders: Maynooth
•Data on GMS – Medical Card holders
drawn under strict data protection
protocols from DOHC
• Anonymised address lists data matched
with GeoDirectory
•Address matching rate of around 77% in
Maynooth

• Interesting range of results around
average for the whole ED

•ED – single value 14.6 % (national average around 30%)
•Small Area – Range from 2% to 70% - noticeable internal variation and
good indicator of elderly or poor populations

•Strategic value in comparing datasets
•Important in policy analysis

Mapping invites comparison

Role of GIS
• Data integration
– Tally of cases in small areas requires
data on locations of cases and small
area boundaries
– ‘spatial join’ places small area codes
on the case records
– Counting is a standard database
operation

• Data display

Finally
• GIS encourages you to think about
space
• In a ‘conventional’ map of Ireland
areas (EDs, Counties…) are shown in
proportion to their physical size
• There are alternative ways of
displaying maps…

In a cartogram the areas are drawn
proportional to their populations –
this is a “density-equalised” map
projection

A map of the boundaries of counties and
electoral divisions – the areas are drawn
proportional to their physical size

By comparison, the more deprived
western area of Dublin, northern area
of Cork and south east of Limerick are
clearly apparent in the cartogram.

Haase’s deprivation index suggests that the
most deprived populations are in peripheral
rural areas in the north west.

Redrawing the data on the cartogram
reveals wide variation in the SMR, and also
uncovers the urban dimension which is
hidden in the conventional display

Patient data matched to Electoral Divisions –
hospital admissions for psychiatric problems – the
patchwork effect does not suggest any pattern

The cartogram reveals the urban nature
of the problem – those areas which are
more deprived are also those with
higher levels of illness/disability

Residents aged 15+ who are unable to work
through illness or disability: the map pattern
suggests that might be a largely rural
phenomenon

The roads, rail, and urban areas data have
been transformed into the same space as
the cartogram. Dublin and Cork seem
poorly provided with roads…

Other spatial information can be transformed
to the space of the density equalised
projection. Here is the familiar map of main
roads and towns.

Using a cartogram we see that the
incidence of lung cancer is randomly
spatially distributed

Lung cancer in males in New York State [Gastner & Newman: 2004]

Plotting locations of lung cancer cases, we
obtain a map which follows the underlying
population distribution – there appear to be
“clusters”

What about Ireland?
• Potentially hampered by challenge of
geocoding patient records
• Lack of small area population data
– EDs have an average population of 1150
– Wide variation in size (50-28000)
– Socially inhomogenous

Rural addresses
• A major challenge is non-uniqueness
of addresses in rural areas.
• For instance in Moy ED, 11 properties
share the address
‘Lackamore, Lahinch, Co. Clare’

What’s needed
• Postcoding should provide a
mechanism for geocoding patient data
with the granularity required for
planning the delivery of health care –
this has been the case in the UK for a
for long time
• Census data should be made available
for areas with small and similar
populations (cf: UK Output Areas)

Finally…
• GIS has an important role to play
in health studies
• Health data analysis is a rapidly
developing field
• However, in Ireland we are
hampered by data deficiencies –
now is the time for change

Maynooth: image in Google Earth

Central Dublin: image in Google Earth

Taoiseach’s Office: image in Google Earth

Downing Street, London: image in Google Earth

Maynooth: OSi orthophotograph added locally uisng KML

Sellafield: image in Google Earth

NCU – On Line Atlas of Cancer Mortality

The Philippine Institute
for Development
Studies makes an online atlas of socioeconomic indicators
available – contains
some health measures

The future
• Patient empowerment…

• GIS used to merge information from
regulatory agencies on 19196 landfill sites
• Sites buffered to 2km and postcodes
within buffer identified
• Postcoded live births (8.2x106), stillbirths
(43471) and congenital anomalies
(124,597) between 1982 and 1997
• Poisson regression modelling undertaken
• Evidence found for excess risk of
congenital anomaly and low birthweight

• Distance from each
census ward to
nearest hospital
computed
• Map shows number
of patients waiting
longer than 6
months for elective
inpatient care per
available and
unoccupied bed
within 60 minutes
travel time


Slide 17

GIS and Health
Martin Charlton1
Ronan Foley2
Dennis Pringle2
1National

Centre for Geocomputation
2Department of Geography
NUI Maynooth

Outline
• Preliminary observations on GIS
and health
• Examples of GIS in Health:
research and practice
• Mapping people not places

Patterns in Health Data
• Three main areas:
– Mapping of disease incidence
• Relative risk estimation

– Identification of disease clustering
• Surveillance

– Ecological analysis
• Relationship between incidence and
covariates
(Lawson et al, 2003)

Health Data
• Typically patient data is available
in two forms
• Individual – perhaps anonymised
to postcode/zip code level
• Aggregate – counts are available
for a set of zones
• The nature of the data affects the
nature of the analysis

Geographic Information Systems
• GIS is often confused with mapping
• A range of activities which includes
inter alia mapping and analysis
• Will typically involve several datasets
• May also involve several different
pieces of software

Mapping: EpiINFO
• This is a rather simple analysis and
mapping program
• It can be downloaded from the internet
• It doesn’t provide much in the way of
spatial data manipulation…
• … nor are its analytical capabilities very
advanced
• It is easy to use

Spatial Data Manipulation
• GIS isn’t merely concerned with
data display or query
• It’s useful in bringing together
different spatial datasets in the
first stages of an analysis
• This may well require a range of
different geometrical and
database operations

Analysis with Spatial Data
• Statistical analysis usually involves
some separate software.
• Spatial data have some properties
which mean that SPSS isn’t always
appropriate.
• There is now a range of specialist
software – much of it available for
download

GIS may be ideal for data manipulation, display and query,
but it’s not always ideal for analysis – in epidemiology a
variety of analytical software is available … WinBUGS,
GeoBUGS (MRC Biostatistics Unit, Cambridge)

Software to implement
particular methods – for
example scan statistics - can be
downloaded quickly and easily
from the Internet. Kulldorf’s
SaTScan system implements a
range of different scan
statistical models

GIS in Health in Practice
• Facility location – dialysis units
• Facility location – obs & gyn
• Data integration – schizophrenia
in Cavan
• Data manipulation and display –
GMS card holders in Maynooth

1: Provision of Dialysis Units
• There are 5 dialysis units for renal
patients in a region with a population
of about 3,000,000
• Major population centre has two units
about 5 miles apart
• Where would you locate an extra one?
• What would happen if one was
closed?

Location-allocation
• Location-allocation problems deal
with optimum location of facilities
• Attempt to minimise total
weighted travel time/distance
from users to facilities
• What data is needed?

Data requirements
• Digitised road network with classification
of road types
• Mean travel speeds on each type for
urban/non-urban roads
• Extents of urban areas
• Anonymised postcoded patient records
• Postcode->grid reference lookup
• Locations of hospitals in region (both with
and without dialysis units)

GIS processing
• Data integration
• Determine which segments of road network
are urban
• Determine travel times using road type
urban/non-urban speeds
• Tally numbers of patients at each node in
the road network
• Find nearest nodes in network to each
hospital
• Code hospitals as ‘existing’ or ‘potential’
units

Modelling/Analysis
• Carried out using standard GIS
functions
• Convenient ‘front end’ written
using system’s macro language
(save a lot of typing)
• Explore a range of alternatives

Hospital with
dialysis unit

Study region showing hospital locations and urban areas

Patients requiring dialysis (tallied to road nodes)

Classified road network – includes roads outside study area

•

Some alternative
scenarios:

(a) Optimal allocation
of patients
(b) Provide 1 extra
unit
(c) Provide 2 extra
units

(d) Close 1 unit in
city and reallocate
its patients

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

integrate data
process data
model and analyse
generate alternative scenarios
display the results

2: Facility Location
• Two hospitals both provided facilities
for obstetrics, gynaecology, paediatrics
and a special care baby unit.
• Was there a case for rationalisation and
improving the facilities at one hospital
only?
• Would closure disadvantage any
patients?

Data
• Locations of hospitals in study region
• Anonymised postcoded finished
consultant episodes for each of the
four specialities at each hospital
• Classified road network
• Speeds associated with each road
type

Data preparation
• Postcoded records allocated grid
references using Central Postcode
Directory (POSTZON)
• Vector data converted to raster
• Patient/specialty/hospital counts
produced for each raster
• Passage times computed for each
raster cell based on road
classification and speed estimates

Roads, urban areas, patient locations, hospitals in study area

Processing
• Travel time surfaces computed for
each hospital
• Each raster cell contains time
required to reach hospital in minutes
• Assumed walking speed in ‘non road’
areas

Travel time surface for hospital S – darker = shorter time

Travel time surface for hospital T

Time differences
• If we subtract the travel time surfaces areas
nearer one hospital will have positive
values for the difference, and areas nearer
the other will have negative ones
• Boundary between positive and negative
values is boundary of catchment for each
hospital.
• If you’re on the boundary (difference is
zero), it doesn’t matter which hospital you
choose

Roads, patient locations, hospital catchments

Indifference
• If we identify those rasters where the time
difference (ignoring the sign) is, say, 5 –
people in those areas are in the 5 minute
‘indifference’ zone.
• Patients might not be worried about a 5
minute increase in their travel time, they
might be worried about 30 minutes
• Indifference zones can be computed quite
easily

15-minute indifference zone between the two hospitals

Mean access time
• For each cell in the raster we can
compute the product of the number
of patients and the time required to
reach each hospital
• Dividing this by the number of
patients gives a patient weighted
average time for each hospital

Average travel times for the various specialties. There
isn’t much to chose between specialities with the
exception of paediatric episodes.

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

gather data
integrate data
process data
model data
display results

• All carried out using raster
operations

3: Schizophrenia in Cavan
• In 1996 Pringle, Waddington and
Youssef reported on evidence for the
causes of schizophrenia
• Ecological analysis of variation among
EDs
– Cases varied from 0-11 per ED
– Populations varied from 250-2750

• Poisson probabilities suggested that
one area had an unusually high number
of cases – these can be mapped

Male cases
superimposed on plot
of prevalence by ED

Male cases
superimposed on
rectified Landsat
image with ED
boundaries

4: GMS card holders: Maynooth
•Data on GMS – Medical Card holders
drawn under strict data protection
protocols from DOHC
• Anonymised address lists data matched
with GeoDirectory
•Address matching rate of around 77% in
Maynooth

• Interesting range of results around
average for the whole ED

•ED – single value 14.6 % (national average around 30%)
•Small Area – Range from 2% to 70% - noticeable internal variation and
good indicator of elderly or poor populations

•Strategic value in comparing datasets
•Important in policy analysis

Mapping invites comparison

Role of GIS
• Data integration
– Tally of cases in small areas requires
data on locations of cases and small
area boundaries
– ‘spatial join’ places small area codes
on the case records
– Counting is a standard database
operation

• Data display

Finally
• GIS encourages you to think about
space
• In a ‘conventional’ map of Ireland
areas (EDs, Counties…) are shown in
proportion to their physical size
• There are alternative ways of
displaying maps…

In a cartogram the areas are drawn
proportional to their populations –
this is a “density-equalised” map
projection

A map of the boundaries of counties and
electoral divisions – the areas are drawn
proportional to their physical size

By comparison, the more deprived
western area of Dublin, northern area
of Cork and south east of Limerick are
clearly apparent in the cartogram.

Haase’s deprivation index suggests that the
most deprived populations are in peripheral
rural areas in the north west.

Redrawing the data on the cartogram
reveals wide variation in the SMR, and also
uncovers the urban dimension which is
hidden in the conventional display

Patient data matched to Electoral Divisions –
hospital admissions for psychiatric problems – the
patchwork effect does not suggest any pattern

The cartogram reveals the urban nature
of the problem – those areas which are
more deprived are also those with
higher levels of illness/disability

Residents aged 15+ who are unable to work
through illness or disability: the map pattern
suggests that might be a largely rural
phenomenon

The roads, rail, and urban areas data have
been transformed into the same space as
the cartogram. Dublin and Cork seem
poorly provided with roads…

Other spatial information can be transformed
to the space of the density equalised
projection. Here is the familiar map of main
roads and towns.

Using a cartogram we see that the
incidence of lung cancer is randomly
spatially distributed

Lung cancer in males in New York State [Gastner & Newman: 2004]

Plotting locations of lung cancer cases, we
obtain a map which follows the underlying
population distribution – there appear to be
“clusters”

What about Ireland?
• Potentially hampered by challenge of
geocoding patient records
• Lack of small area population data
– EDs have an average population of 1150
– Wide variation in size (50-28000)
– Socially inhomogenous

Rural addresses
• A major challenge is non-uniqueness
of addresses in rural areas.
• For instance in Moy ED, 11 properties
share the address
‘Lackamore, Lahinch, Co. Clare’

What’s needed
• Postcoding should provide a
mechanism for geocoding patient data
with the granularity required for
planning the delivery of health care –
this has been the case in the UK for a
for long time
• Census data should be made available
for areas with small and similar
populations (cf: UK Output Areas)

Finally…
• GIS has an important role to play
in health studies
• Health data analysis is a rapidly
developing field
• However, in Ireland we are
hampered by data deficiencies –
now is the time for change

Maynooth: image in Google Earth

Central Dublin: image in Google Earth

Taoiseach’s Office: image in Google Earth

Downing Street, London: image in Google Earth

Maynooth: OSi orthophotograph added locally uisng KML

Sellafield: image in Google Earth

NCU – On Line Atlas of Cancer Mortality

The Philippine Institute
for Development
Studies makes an online atlas of socioeconomic indicators
available – contains
some health measures

The future
• Patient empowerment…

• GIS used to merge information from
regulatory agencies on 19196 landfill sites
• Sites buffered to 2km and postcodes
within buffer identified
• Postcoded live births (8.2x106), stillbirths
(43471) and congenital anomalies
(124,597) between 1982 and 1997
• Poisson regression modelling undertaken
• Evidence found for excess risk of
congenital anomaly and low birthweight

• Distance from each
census ward to
nearest hospital
computed
• Map shows number
of patients waiting
longer than 6
months for elective
inpatient care per
available and
unoccupied bed
within 60 minutes
travel time


Slide 18

GIS and Health
Martin Charlton1
Ronan Foley2
Dennis Pringle2
1National

Centre for Geocomputation
2Department of Geography
NUI Maynooth

Outline
• Preliminary observations on GIS
and health
• Examples of GIS in Health:
research and practice
• Mapping people not places

Patterns in Health Data
• Three main areas:
– Mapping of disease incidence
• Relative risk estimation

– Identification of disease clustering
• Surveillance

– Ecological analysis
• Relationship between incidence and
covariates
(Lawson et al, 2003)

Health Data
• Typically patient data is available
in two forms
• Individual – perhaps anonymised
to postcode/zip code level
• Aggregate – counts are available
for a set of zones
• The nature of the data affects the
nature of the analysis

Geographic Information Systems
• GIS is often confused with mapping
• A range of activities which includes
inter alia mapping and analysis
• Will typically involve several datasets
• May also involve several different
pieces of software

Mapping: EpiINFO
• This is a rather simple analysis and
mapping program
• It can be downloaded from the internet
• It doesn’t provide much in the way of
spatial data manipulation…
• … nor are its analytical capabilities very
advanced
• It is easy to use

Spatial Data Manipulation
• GIS isn’t merely concerned with
data display or query
• It’s useful in bringing together
different spatial datasets in the
first stages of an analysis
• This may well require a range of
different geometrical and
database operations

Analysis with Spatial Data
• Statistical analysis usually involves
some separate software.
• Spatial data have some properties
which mean that SPSS isn’t always
appropriate.
• There is now a range of specialist
software – much of it available for
download

GIS may be ideal for data manipulation, display and query,
but it’s not always ideal for analysis – in epidemiology a
variety of analytical software is available … WinBUGS,
GeoBUGS (MRC Biostatistics Unit, Cambridge)

Software to implement
particular methods – for
example scan statistics - can be
downloaded quickly and easily
from the Internet. Kulldorf’s
SaTScan system implements a
range of different scan
statistical models

GIS in Health in Practice
• Facility location – dialysis units
• Facility location – obs & gyn
• Data integration – schizophrenia
in Cavan
• Data manipulation and display –
GMS card holders in Maynooth

1: Provision of Dialysis Units
• There are 5 dialysis units for renal
patients in a region with a population
of about 3,000,000
• Major population centre has two units
about 5 miles apart
• Where would you locate an extra one?
• What would happen if one was
closed?

Location-allocation
• Location-allocation problems deal
with optimum location of facilities
• Attempt to minimise total
weighted travel time/distance
from users to facilities
• What data is needed?

Data requirements
• Digitised road network with classification
of road types
• Mean travel speeds on each type for
urban/non-urban roads
• Extents of urban areas
• Anonymised postcoded patient records
• Postcode->grid reference lookup
• Locations of hospitals in region (both with
and without dialysis units)

GIS processing
• Data integration
• Determine which segments of road network
are urban
• Determine travel times using road type
urban/non-urban speeds
• Tally numbers of patients at each node in
the road network
• Find nearest nodes in network to each
hospital
• Code hospitals as ‘existing’ or ‘potential’
units

Modelling/Analysis
• Carried out using standard GIS
functions
• Convenient ‘front end’ written
using system’s macro language
(save a lot of typing)
• Explore a range of alternatives

Hospital with
dialysis unit

Study region showing hospital locations and urban areas

Patients requiring dialysis (tallied to road nodes)

Classified road network – includes roads outside study area

•

Some alternative
scenarios:

(a) Optimal allocation
of patients
(b) Provide 1 extra
unit
(c) Provide 2 extra
units

(d) Close 1 unit in
city and reallocate
its patients

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

integrate data
process data
model and analyse
generate alternative scenarios
display the results

2: Facility Location
• Two hospitals both provided facilities
for obstetrics, gynaecology, paediatrics
and a special care baby unit.
• Was there a case for rationalisation and
improving the facilities at one hospital
only?
• Would closure disadvantage any
patients?

Data
• Locations of hospitals in study region
• Anonymised postcoded finished
consultant episodes for each of the
four specialities at each hospital
• Classified road network
• Speeds associated with each road
type

Data preparation
• Postcoded records allocated grid
references using Central Postcode
Directory (POSTZON)
• Vector data converted to raster
• Patient/specialty/hospital counts
produced for each raster
• Passage times computed for each
raster cell based on road
classification and speed estimates

Roads, urban areas, patient locations, hospitals in study area

Processing
• Travel time surfaces computed for
each hospital
• Each raster cell contains time
required to reach hospital in minutes
• Assumed walking speed in ‘non road’
areas

Travel time surface for hospital S – darker = shorter time

Travel time surface for hospital T

Time differences
• If we subtract the travel time surfaces areas
nearer one hospital will have positive
values for the difference, and areas nearer
the other will have negative ones
• Boundary between positive and negative
values is boundary of catchment for each
hospital.
• If you’re on the boundary (difference is
zero), it doesn’t matter which hospital you
choose

Roads, patient locations, hospital catchments

Indifference
• If we identify those rasters where the time
difference (ignoring the sign) is, say, 5 –
people in those areas are in the 5 minute
‘indifference’ zone.
• Patients might not be worried about a 5
minute increase in their travel time, they
might be worried about 30 minutes
• Indifference zones can be computed quite
easily

15-minute indifference zone between the two hospitals

Mean access time
• For each cell in the raster we can
compute the product of the number
of patients and the time required to
reach each hospital
• Dividing this by the number of
patients gives a patient weighted
average time for each hospital

Average travel times for the various specialties. There
isn’t much to chose between specialities with the
exception of paediatric episodes.

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

gather data
integrate data
process data
model data
display results

• All carried out using raster
operations

3: Schizophrenia in Cavan
• In 1996 Pringle, Waddington and
Youssef reported on evidence for the
causes of schizophrenia
• Ecological analysis of variation among
EDs
– Cases varied from 0-11 per ED
– Populations varied from 250-2750

• Poisson probabilities suggested that
one area had an unusually high number
of cases – these can be mapped

Male cases
superimposed on plot
of prevalence by ED

Male cases
superimposed on
rectified Landsat
image with ED
boundaries

4: GMS card holders: Maynooth
•Data on GMS – Medical Card holders
drawn under strict data protection
protocols from DOHC
• Anonymised address lists data matched
with GeoDirectory
•Address matching rate of around 77% in
Maynooth

• Interesting range of results around
average for the whole ED

•ED – single value 14.6 % (national average around 30%)
•Small Area – Range from 2% to 70% - noticeable internal variation and
good indicator of elderly or poor populations

•Strategic value in comparing datasets
•Important in policy analysis

Mapping invites comparison

Role of GIS
• Data integration
– Tally of cases in small areas requires
data on locations of cases and small
area boundaries
– ‘spatial join’ places small area codes
on the case records
– Counting is a standard database
operation

• Data display

Finally
• GIS encourages you to think about
space
• In a ‘conventional’ map of Ireland
areas (EDs, Counties…) are shown in
proportion to their physical size
• There are alternative ways of
displaying maps…

In a cartogram the areas are drawn
proportional to their populations –
this is a “density-equalised” map
projection

A map of the boundaries of counties and
electoral divisions – the areas are drawn
proportional to their physical size

By comparison, the more deprived
western area of Dublin, northern area
of Cork and south east of Limerick are
clearly apparent in the cartogram.

Haase’s deprivation index suggests that the
most deprived populations are in peripheral
rural areas in the north west.

Redrawing the data on the cartogram
reveals wide variation in the SMR, and also
uncovers the urban dimension which is
hidden in the conventional display

Patient data matched to Electoral Divisions –
hospital admissions for psychiatric problems – the
patchwork effect does not suggest any pattern

The cartogram reveals the urban nature
of the problem – those areas which are
more deprived are also those with
higher levels of illness/disability

Residents aged 15+ who are unable to work
through illness or disability: the map pattern
suggests that might be a largely rural
phenomenon

The roads, rail, and urban areas data have
been transformed into the same space as
the cartogram. Dublin and Cork seem
poorly provided with roads…

Other spatial information can be transformed
to the space of the density equalised
projection. Here is the familiar map of main
roads and towns.

Using a cartogram we see that the
incidence of lung cancer is randomly
spatially distributed

Lung cancer in males in New York State [Gastner & Newman: 2004]

Plotting locations of lung cancer cases, we
obtain a map which follows the underlying
population distribution – there appear to be
“clusters”

What about Ireland?
• Potentially hampered by challenge of
geocoding patient records
• Lack of small area population data
– EDs have an average population of 1150
– Wide variation in size (50-28000)
– Socially inhomogenous

Rural addresses
• A major challenge is non-uniqueness
of addresses in rural areas.
• For instance in Moy ED, 11 properties
share the address
‘Lackamore, Lahinch, Co. Clare’

What’s needed
• Postcoding should provide a
mechanism for geocoding patient data
with the granularity required for
planning the delivery of health care –
this has been the case in the UK for a
for long time
• Census data should be made available
for areas with small and similar
populations (cf: UK Output Areas)

Finally…
• GIS has an important role to play
in health studies
• Health data analysis is a rapidly
developing field
• However, in Ireland we are
hampered by data deficiencies –
now is the time for change

Maynooth: image in Google Earth

Central Dublin: image in Google Earth

Taoiseach’s Office: image in Google Earth

Downing Street, London: image in Google Earth

Maynooth: OSi orthophotograph added locally uisng KML

Sellafield: image in Google Earth

NCU – On Line Atlas of Cancer Mortality

The Philippine Institute
for Development
Studies makes an online atlas of socioeconomic indicators
available – contains
some health measures

The future
• Patient empowerment…

• GIS used to merge information from
regulatory agencies on 19196 landfill sites
• Sites buffered to 2km and postcodes
within buffer identified
• Postcoded live births (8.2x106), stillbirths
(43471) and congenital anomalies
(124,597) between 1982 and 1997
• Poisson regression modelling undertaken
• Evidence found for excess risk of
congenital anomaly and low birthweight

• Distance from each
census ward to
nearest hospital
computed
• Map shows number
of patients waiting
longer than 6
months for elective
inpatient care per
available and
unoccupied bed
within 60 minutes
travel time


Slide 19

GIS and Health
Martin Charlton1
Ronan Foley2
Dennis Pringle2
1National

Centre for Geocomputation
2Department of Geography
NUI Maynooth

Outline
• Preliminary observations on GIS
and health
• Examples of GIS in Health:
research and practice
• Mapping people not places

Patterns in Health Data
• Three main areas:
– Mapping of disease incidence
• Relative risk estimation

– Identification of disease clustering
• Surveillance

– Ecological analysis
• Relationship between incidence and
covariates
(Lawson et al, 2003)

Health Data
• Typically patient data is available
in two forms
• Individual – perhaps anonymised
to postcode/zip code level
• Aggregate – counts are available
for a set of zones
• The nature of the data affects the
nature of the analysis

Geographic Information Systems
• GIS is often confused with mapping
• A range of activities which includes
inter alia mapping and analysis
• Will typically involve several datasets
• May also involve several different
pieces of software

Mapping: EpiINFO
• This is a rather simple analysis and
mapping program
• It can be downloaded from the internet
• It doesn’t provide much in the way of
spatial data manipulation…
• … nor are its analytical capabilities very
advanced
• It is easy to use

Spatial Data Manipulation
• GIS isn’t merely concerned with
data display or query
• It’s useful in bringing together
different spatial datasets in the
first stages of an analysis
• This may well require a range of
different geometrical and
database operations

Analysis with Spatial Data
• Statistical analysis usually involves
some separate software.
• Spatial data have some properties
which mean that SPSS isn’t always
appropriate.
• There is now a range of specialist
software – much of it available for
download

GIS may be ideal for data manipulation, display and query,
but it’s not always ideal for analysis – in epidemiology a
variety of analytical software is available … WinBUGS,
GeoBUGS (MRC Biostatistics Unit, Cambridge)

Software to implement
particular methods – for
example scan statistics - can be
downloaded quickly and easily
from the Internet. Kulldorf’s
SaTScan system implements a
range of different scan
statistical models

GIS in Health in Practice
• Facility location – dialysis units
• Facility location – obs & gyn
• Data integration – schizophrenia
in Cavan
• Data manipulation and display –
GMS card holders in Maynooth

1: Provision of Dialysis Units
• There are 5 dialysis units for renal
patients in a region with a population
of about 3,000,000
• Major population centre has two units
about 5 miles apart
• Where would you locate an extra one?
• What would happen if one was
closed?

Location-allocation
• Location-allocation problems deal
with optimum location of facilities
• Attempt to minimise total
weighted travel time/distance
from users to facilities
• What data is needed?

Data requirements
• Digitised road network with classification
of road types
• Mean travel speeds on each type for
urban/non-urban roads
• Extents of urban areas
• Anonymised postcoded patient records
• Postcode->grid reference lookup
• Locations of hospitals in region (both with
and without dialysis units)

GIS processing
• Data integration
• Determine which segments of road network
are urban
• Determine travel times using road type
urban/non-urban speeds
• Tally numbers of patients at each node in
the road network
• Find nearest nodes in network to each
hospital
• Code hospitals as ‘existing’ or ‘potential’
units

Modelling/Analysis
• Carried out using standard GIS
functions
• Convenient ‘front end’ written
using system’s macro language
(save a lot of typing)
• Explore a range of alternatives

Hospital with
dialysis unit

Study region showing hospital locations and urban areas

Patients requiring dialysis (tallied to road nodes)

Classified road network – includes roads outside study area

•

Some alternative
scenarios:

(a) Optimal allocation
of patients
(b) Provide 1 extra
unit
(c) Provide 2 extra
units

(d) Close 1 unit in
city and reallocate
its patients

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

integrate data
process data
model and analyse
generate alternative scenarios
display the results

2: Facility Location
• Two hospitals both provided facilities
for obstetrics, gynaecology, paediatrics
and a special care baby unit.
• Was there a case for rationalisation and
improving the facilities at one hospital
only?
• Would closure disadvantage any
patients?

Data
• Locations of hospitals in study region
• Anonymised postcoded finished
consultant episodes for each of the
four specialities at each hospital
• Classified road network
• Speeds associated with each road
type

Data preparation
• Postcoded records allocated grid
references using Central Postcode
Directory (POSTZON)
• Vector data converted to raster
• Patient/specialty/hospital counts
produced for each raster
• Passage times computed for each
raster cell based on road
classification and speed estimates

Roads, urban areas, patient locations, hospitals in study area

Processing
• Travel time surfaces computed for
each hospital
• Each raster cell contains time
required to reach hospital in minutes
• Assumed walking speed in ‘non road’
areas

Travel time surface for hospital S – darker = shorter time

Travel time surface for hospital T

Time differences
• If we subtract the travel time surfaces areas
nearer one hospital will have positive
values for the difference, and areas nearer
the other will have negative ones
• Boundary between positive and negative
values is boundary of catchment for each
hospital.
• If you’re on the boundary (difference is
zero), it doesn’t matter which hospital you
choose

Roads, patient locations, hospital catchments

Indifference
• If we identify those rasters where the time
difference (ignoring the sign) is, say, 5 –
people in those areas are in the 5 minute
‘indifference’ zone.
• Patients might not be worried about a 5
minute increase in their travel time, they
might be worried about 30 minutes
• Indifference zones can be computed quite
easily

15-minute indifference zone between the two hospitals

Mean access time
• For each cell in the raster we can
compute the product of the number
of patients and the time required to
reach each hospital
• Dividing this by the number of
patients gives a patient weighted
average time for each hospital

Average travel times for the various specialties. There
isn’t much to chose between specialities with the
exception of paediatric episodes.

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

gather data
integrate data
process data
model data
display results

• All carried out using raster
operations

3: Schizophrenia in Cavan
• In 1996 Pringle, Waddington and
Youssef reported on evidence for the
causes of schizophrenia
• Ecological analysis of variation among
EDs
– Cases varied from 0-11 per ED
– Populations varied from 250-2750

• Poisson probabilities suggested that
one area had an unusually high number
of cases – these can be mapped

Male cases
superimposed on plot
of prevalence by ED

Male cases
superimposed on
rectified Landsat
image with ED
boundaries

4: GMS card holders: Maynooth
•Data on GMS – Medical Card holders
drawn under strict data protection
protocols from DOHC
• Anonymised address lists data matched
with GeoDirectory
•Address matching rate of around 77% in
Maynooth

• Interesting range of results around
average for the whole ED

•ED – single value 14.6 % (national average around 30%)
•Small Area – Range from 2% to 70% - noticeable internal variation and
good indicator of elderly or poor populations

•Strategic value in comparing datasets
•Important in policy analysis

Mapping invites comparison

Role of GIS
• Data integration
– Tally of cases in small areas requires
data on locations of cases and small
area boundaries
– ‘spatial join’ places small area codes
on the case records
– Counting is a standard database
operation

• Data display

Finally
• GIS encourages you to think about
space
• In a ‘conventional’ map of Ireland
areas (EDs, Counties…) are shown in
proportion to their physical size
• There are alternative ways of
displaying maps…

In a cartogram the areas are drawn
proportional to their populations –
this is a “density-equalised” map
projection

A map of the boundaries of counties and
electoral divisions – the areas are drawn
proportional to their physical size

By comparison, the more deprived
western area of Dublin, northern area
of Cork and south east of Limerick are
clearly apparent in the cartogram.

Haase’s deprivation index suggests that the
most deprived populations are in peripheral
rural areas in the north west.

Redrawing the data on the cartogram
reveals wide variation in the SMR, and also
uncovers the urban dimension which is
hidden in the conventional display

Patient data matched to Electoral Divisions –
hospital admissions for psychiatric problems – the
patchwork effect does not suggest any pattern

The cartogram reveals the urban nature
of the problem – those areas which are
more deprived are also those with
higher levels of illness/disability

Residents aged 15+ who are unable to work
through illness or disability: the map pattern
suggests that might be a largely rural
phenomenon

The roads, rail, and urban areas data have
been transformed into the same space as
the cartogram. Dublin and Cork seem
poorly provided with roads…

Other spatial information can be transformed
to the space of the density equalised
projection. Here is the familiar map of main
roads and towns.

Using a cartogram we see that the
incidence of lung cancer is randomly
spatially distributed

Lung cancer in males in New York State [Gastner & Newman: 2004]

Plotting locations of lung cancer cases, we
obtain a map which follows the underlying
population distribution – there appear to be
“clusters”

What about Ireland?
• Potentially hampered by challenge of
geocoding patient records
• Lack of small area population data
– EDs have an average population of 1150
– Wide variation in size (50-28000)
– Socially inhomogenous

Rural addresses
• A major challenge is non-uniqueness
of addresses in rural areas.
• For instance in Moy ED, 11 properties
share the address
‘Lackamore, Lahinch, Co. Clare’

What’s needed
• Postcoding should provide a
mechanism for geocoding patient data
with the granularity required for
planning the delivery of health care –
this has been the case in the UK for a
for long time
• Census data should be made available
for areas with small and similar
populations (cf: UK Output Areas)

Finally…
• GIS has an important role to play
in health studies
• Health data analysis is a rapidly
developing field
• However, in Ireland we are
hampered by data deficiencies –
now is the time for change

Maynooth: image in Google Earth

Central Dublin: image in Google Earth

Taoiseach’s Office: image in Google Earth

Downing Street, London: image in Google Earth

Maynooth: OSi orthophotograph added locally uisng KML

Sellafield: image in Google Earth

NCU – On Line Atlas of Cancer Mortality

The Philippine Institute
for Development
Studies makes an online atlas of socioeconomic indicators
available – contains
some health measures

The future
• Patient empowerment…

• GIS used to merge information from
regulatory agencies on 19196 landfill sites
• Sites buffered to 2km and postcodes
within buffer identified
• Postcoded live births (8.2x106), stillbirths
(43471) and congenital anomalies
(124,597) between 1982 and 1997
• Poisson regression modelling undertaken
• Evidence found for excess risk of
congenital anomaly and low birthweight

• Distance from each
census ward to
nearest hospital
computed
• Map shows number
of patients waiting
longer than 6
months for elective
inpatient care per
available and
unoccupied bed
within 60 minutes
travel time


Slide 20

GIS and Health
Martin Charlton1
Ronan Foley2
Dennis Pringle2
1National

Centre for Geocomputation
2Department of Geography
NUI Maynooth

Outline
• Preliminary observations on GIS
and health
• Examples of GIS in Health:
research and practice
• Mapping people not places

Patterns in Health Data
• Three main areas:
– Mapping of disease incidence
• Relative risk estimation

– Identification of disease clustering
• Surveillance

– Ecological analysis
• Relationship between incidence and
covariates
(Lawson et al, 2003)

Health Data
• Typically patient data is available
in two forms
• Individual – perhaps anonymised
to postcode/zip code level
• Aggregate – counts are available
for a set of zones
• The nature of the data affects the
nature of the analysis

Geographic Information Systems
• GIS is often confused with mapping
• A range of activities which includes
inter alia mapping and analysis
• Will typically involve several datasets
• May also involve several different
pieces of software

Mapping: EpiINFO
• This is a rather simple analysis and
mapping program
• It can be downloaded from the internet
• It doesn’t provide much in the way of
spatial data manipulation…
• … nor are its analytical capabilities very
advanced
• It is easy to use

Spatial Data Manipulation
• GIS isn’t merely concerned with
data display or query
• It’s useful in bringing together
different spatial datasets in the
first stages of an analysis
• This may well require a range of
different geometrical and
database operations

Analysis with Spatial Data
• Statistical analysis usually involves
some separate software.
• Spatial data have some properties
which mean that SPSS isn’t always
appropriate.
• There is now a range of specialist
software – much of it available for
download

GIS may be ideal for data manipulation, display and query,
but it’s not always ideal for analysis – in epidemiology a
variety of analytical software is available … WinBUGS,
GeoBUGS (MRC Biostatistics Unit, Cambridge)

Software to implement
particular methods – for
example scan statistics - can be
downloaded quickly and easily
from the Internet. Kulldorf’s
SaTScan system implements a
range of different scan
statistical models

GIS in Health in Practice
• Facility location – dialysis units
• Facility location – obs & gyn
• Data integration – schizophrenia
in Cavan
• Data manipulation and display –
GMS card holders in Maynooth

1: Provision of Dialysis Units
• There are 5 dialysis units for renal
patients in a region with a population
of about 3,000,000
• Major population centre has two units
about 5 miles apart
• Where would you locate an extra one?
• What would happen if one was
closed?

Location-allocation
• Location-allocation problems deal
with optimum location of facilities
• Attempt to minimise total
weighted travel time/distance
from users to facilities
• What data is needed?

Data requirements
• Digitised road network with classification
of road types
• Mean travel speeds on each type for
urban/non-urban roads
• Extents of urban areas
• Anonymised postcoded patient records
• Postcode->grid reference lookup
• Locations of hospitals in region (both with
and without dialysis units)

GIS processing
• Data integration
• Determine which segments of road network
are urban
• Determine travel times using road type
urban/non-urban speeds
• Tally numbers of patients at each node in
the road network
• Find nearest nodes in network to each
hospital
• Code hospitals as ‘existing’ or ‘potential’
units

Modelling/Analysis
• Carried out using standard GIS
functions
• Convenient ‘front end’ written
using system’s macro language
(save a lot of typing)
• Explore a range of alternatives

Hospital with
dialysis unit

Study region showing hospital locations and urban areas

Patients requiring dialysis (tallied to road nodes)

Classified road network – includes roads outside study area

•

Some alternative
scenarios:

(a) Optimal allocation
of patients
(b) Provide 1 extra
unit
(c) Provide 2 extra
units

(d) Close 1 unit in
city and reallocate
its patients

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

integrate data
process data
model and analyse
generate alternative scenarios
display the results

2: Facility Location
• Two hospitals both provided facilities
for obstetrics, gynaecology, paediatrics
and a special care baby unit.
• Was there a case for rationalisation and
improving the facilities at one hospital
only?
• Would closure disadvantage any
patients?

Data
• Locations of hospitals in study region
• Anonymised postcoded finished
consultant episodes for each of the
four specialities at each hospital
• Classified road network
• Speeds associated with each road
type

Data preparation
• Postcoded records allocated grid
references using Central Postcode
Directory (POSTZON)
• Vector data converted to raster
• Patient/specialty/hospital counts
produced for each raster
• Passage times computed for each
raster cell based on road
classification and speed estimates

Roads, urban areas, patient locations, hospitals in study area

Processing
• Travel time surfaces computed for
each hospital
• Each raster cell contains time
required to reach hospital in minutes
• Assumed walking speed in ‘non road’
areas

Travel time surface for hospital S – darker = shorter time

Travel time surface for hospital T

Time differences
• If we subtract the travel time surfaces areas
nearer one hospital will have positive
values for the difference, and areas nearer
the other will have negative ones
• Boundary between positive and negative
values is boundary of catchment for each
hospital.
• If you’re on the boundary (difference is
zero), it doesn’t matter which hospital you
choose

Roads, patient locations, hospital catchments

Indifference
• If we identify those rasters where the time
difference (ignoring the sign) is, say, 5 –
people in those areas are in the 5 minute
‘indifference’ zone.
• Patients might not be worried about a 5
minute increase in their travel time, they
might be worried about 30 minutes
• Indifference zones can be computed quite
easily

15-minute indifference zone between the two hospitals

Mean access time
• For each cell in the raster we can
compute the product of the number
of patients and the time required to
reach each hospital
• Dividing this by the number of
patients gives a patient weighted
average time for each hospital

Average travel times for the various specialties. There
isn’t much to chose between specialities with the
exception of paediatric episodes.

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

gather data
integrate data
process data
model data
display results

• All carried out using raster
operations

3: Schizophrenia in Cavan
• In 1996 Pringle, Waddington and
Youssef reported on evidence for the
causes of schizophrenia
• Ecological analysis of variation among
EDs
– Cases varied from 0-11 per ED
– Populations varied from 250-2750

• Poisson probabilities suggested that
one area had an unusually high number
of cases – these can be mapped

Male cases
superimposed on plot
of prevalence by ED

Male cases
superimposed on
rectified Landsat
image with ED
boundaries

4: GMS card holders: Maynooth
•Data on GMS – Medical Card holders
drawn under strict data protection
protocols from DOHC
• Anonymised address lists data matched
with GeoDirectory
•Address matching rate of around 77% in
Maynooth

• Interesting range of results around
average for the whole ED

•ED – single value 14.6 % (national average around 30%)
•Small Area – Range from 2% to 70% - noticeable internal variation and
good indicator of elderly or poor populations

•Strategic value in comparing datasets
•Important in policy analysis

Mapping invites comparison

Role of GIS
• Data integration
– Tally of cases in small areas requires
data on locations of cases and small
area boundaries
– ‘spatial join’ places small area codes
on the case records
– Counting is a standard database
operation

• Data display

Finally
• GIS encourages you to think about
space
• In a ‘conventional’ map of Ireland
areas (EDs, Counties…) are shown in
proportion to their physical size
• There are alternative ways of
displaying maps…

In a cartogram the areas are drawn
proportional to their populations –
this is a “density-equalised” map
projection

A map of the boundaries of counties and
electoral divisions – the areas are drawn
proportional to their physical size

By comparison, the more deprived
western area of Dublin, northern area
of Cork and south east of Limerick are
clearly apparent in the cartogram.

Haase’s deprivation index suggests that the
most deprived populations are in peripheral
rural areas in the north west.

Redrawing the data on the cartogram
reveals wide variation in the SMR, and also
uncovers the urban dimension which is
hidden in the conventional display

Patient data matched to Electoral Divisions –
hospital admissions for psychiatric problems – the
patchwork effect does not suggest any pattern

The cartogram reveals the urban nature
of the problem – those areas which are
more deprived are also those with
higher levels of illness/disability

Residents aged 15+ who are unable to work
through illness or disability: the map pattern
suggests that might be a largely rural
phenomenon

The roads, rail, and urban areas data have
been transformed into the same space as
the cartogram. Dublin and Cork seem
poorly provided with roads…

Other spatial information can be transformed
to the space of the density equalised
projection. Here is the familiar map of main
roads and towns.

Using a cartogram we see that the
incidence of lung cancer is randomly
spatially distributed

Lung cancer in males in New York State [Gastner & Newman: 2004]

Plotting locations of lung cancer cases, we
obtain a map which follows the underlying
population distribution – there appear to be
“clusters”

What about Ireland?
• Potentially hampered by challenge of
geocoding patient records
• Lack of small area population data
– EDs have an average population of 1150
– Wide variation in size (50-28000)
– Socially inhomogenous

Rural addresses
• A major challenge is non-uniqueness
of addresses in rural areas.
• For instance in Moy ED, 11 properties
share the address
‘Lackamore, Lahinch, Co. Clare’

What’s needed
• Postcoding should provide a
mechanism for geocoding patient data
with the granularity required for
planning the delivery of health care –
this has been the case in the UK for a
for long time
• Census data should be made available
for areas with small and similar
populations (cf: UK Output Areas)

Finally…
• GIS has an important role to play
in health studies
• Health data analysis is a rapidly
developing field
• However, in Ireland we are
hampered by data deficiencies –
now is the time for change

Maynooth: image in Google Earth

Central Dublin: image in Google Earth

Taoiseach’s Office: image in Google Earth

Downing Street, London: image in Google Earth

Maynooth: OSi orthophotograph added locally uisng KML

Sellafield: image in Google Earth

NCU – On Line Atlas of Cancer Mortality

The Philippine Institute
for Development
Studies makes an online atlas of socioeconomic indicators
available – contains
some health measures

The future
• Patient empowerment…

• GIS used to merge information from
regulatory agencies on 19196 landfill sites
• Sites buffered to 2km and postcodes
within buffer identified
• Postcoded live births (8.2x106), stillbirths
(43471) and congenital anomalies
(124,597) between 1982 and 1997
• Poisson regression modelling undertaken
• Evidence found for excess risk of
congenital anomaly and low birthweight

• Distance from each
census ward to
nearest hospital
computed
• Map shows number
of patients waiting
longer than 6
months for elective
inpatient care per
available and
unoccupied bed
within 60 minutes
travel time


Slide 21

GIS and Health
Martin Charlton1
Ronan Foley2
Dennis Pringle2
1National

Centre for Geocomputation
2Department of Geography
NUI Maynooth

Outline
• Preliminary observations on GIS
and health
• Examples of GIS in Health:
research and practice
• Mapping people not places

Patterns in Health Data
• Three main areas:
– Mapping of disease incidence
• Relative risk estimation

– Identification of disease clustering
• Surveillance

– Ecological analysis
• Relationship between incidence and
covariates
(Lawson et al, 2003)

Health Data
• Typically patient data is available
in two forms
• Individual – perhaps anonymised
to postcode/zip code level
• Aggregate – counts are available
for a set of zones
• The nature of the data affects the
nature of the analysis

Geographic Information Systems
• GIS is often confused with mapping
• A range of activities which includes
inter alia mapping and analysis
• Will typically involve several datasets
• May also involve several different
pieces of software

Mapping: EpiINFO
• This is a rather simple analysis and
mapping program
• It can be downloaded from the internet
• It doesn’t provide much in the way of
spatial data manipulation…
• … nor are its analytical capabilities very
advanced
• It is easy to use

Spatial Data Manipulation
• GIS isn’t merely concerned with
data display or query
• It’s useful in bringing together
different spatial datasets in the
first stages of an analysis
• This may well require a range of
different geometrical and
database operations

Analysis with Spatial Data
• Statistical analysis usually involves
some separate software.
• Spatial data have some properties
which mean that SPSS isn’t always
appropriate.
• There is now a range of specialist
software – much of it available for
download

GIS may be ideal for data manipulation, display and query,
but it’s not always ideal for analysis – in epidemiology a
variety of analytical software is available … WinBUGS,
GeoBUGS (MRC Biostatistics Unit, Cambridge)

Software to implement
particular methods – for
example scan statistics - can be
downloaded quickly and easily
from the Internet. Kulldorf’s
SaTScan system implements a
range of different scan
statistical models

GIS in Health in Practice
• Facility location – dialysis units
• Facility location – obs & gyn
• Data integration – schizophrenia
in Cavan
• Data manipulation and display –
GMS card holders in Maynooth

1: Provision of Dialysis Units
• There are 5 dialysis units for renal
patients in a region with a population
of about 3,000,000
• Major population centre has two units
about 5 miles apart
• Where would you locate an extra one?
• What would happen if one was
closed?

Location-allocation
• Location-allocation problems deal
with optimum location of facilities
• Attempt to minimise total
weighted travel time/distance
from users to facilities
• What data is needed?

Data requirements
• Digitised road network with classification
of road types
• Mean travel speeds on each type for
urban/non-urban roads
• Extents of urban areas
• Anonymised postcoded patient records
• Postcode->grid reference lookup
• Locations of hospitals in region (both with
and without dialysis units)

GIS processing
• Data integration
• Determine which segments of road network
are urban
• Determine travel times using road type
urban/non-urban speeds
• Tally numbers of patients at each node in
the road network
• Find nearest nodes in network to each
hospital
• Code hospitals as ‘existing’ or ‘potential’
units

Modelling/Analysis
• Carried out using standard GIS
functions
• Convenient ‘front end’ written
using system’s macro language
(save a lot of typing)
• Explore a range of alternatives

Hospital with
dialysis unit

Study region showing hospital locations and urban areas

Patients requiring dialysis (tallied to road nodes)

Classified road network – includes roads outside study area

•

Some alternative
scenarios:

(a) Optimal allocation
of patients
(b) Provide 1 extra
unit
(c) Provide 2 extra
units

(d) Close 1 unit in
city and reallocate
its patients

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

integrate data
process data
model and analyse
generate alternative scenarios
display the results

2: Facility Location
• Two hospitals both provided facilities
for obstetrics, gynaecology, paediatrics
and a special care baby unit.
• Was there a case for rationalisation and
improving the facilities at one hospital
only?
• Would closure disadvantage any
patients?

Data
• Locations of hospitals in study region
• Anonymised postcoded finished
consultant episodes for each of the
four specialities at each hospital
• Classified road network
• Speeds associated with each road
type

Data preparation
• Postcoded records allocated grid
references using Central Postcode
Directory (POSTZON)
• Vector data converted to raster
• Patient/specialty/hospital counts
produced for each raster
• Passage times computed for each
raster cell based on road
classification and speed estimates

Roads, urban areas, patient locations, hospitals in study area

Processing
• Travel time surfaces computed for
each hospital
• Each raster cell contains time
required to reach hospital in minutes
• Assumed walking speed in ‘non road’
areas

Travel time surface for hospital S – darker = shorter time

Travel time surface for hospital T

Time differences
• If we subtract the travel time surfaces areas
nearer one hospital will have positive
values for the difference, and areas nearer
the other will have negative ones
• Boundary between positive and negative
values is boundary of catchment for each
hospital.
• If you’re on the boundary (difference is
zero), it doesn’t matter which hospital you
choose

Roads, patient locations, hospital catchments

Indifference
• If we identify those rasters where the time
difference (ignoring the sign) is, say, 5 –
people in those areas are in the 5 minute
‘indifference’ zone.
• Patients might not be worried about a 5
minute increase in their travel time, they
might be worried about 30 minutes
• Indifference zones can be computed quite
easily

15-minute indifference zone between the two hospitals

Mean access time
• For each cell in the raster we can
compute the product of the number
of patients and the time required to
reach each hospital
• Dividing this by the number of
patients gives a patient weighted
average time for each hospital

Average travel times for the various specialties. There
isn’t much to chose between specialities with the
exception of paediatric episodes.

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

gather data
integrate data
process data
model data
display results

• All carried out using raster
operations

3: Schizophrenia in Cavan
• In 1996 Pringle, Waddington and
Youssef reported on evidence for the
causes of schizophrenia
• Ecological analysis of variation among
EDs
– Cases varied from 0-11 per ED
– Populations varied from 250-2750

• Poisson probabilities suggested that
one area had an unusually high number
of cases – these can be mapped

Male cases
superimposed on plot
of prevalence by ED

Male cases
superimposed on
rectified Landsat
image with ED
boundaries

4: GMS card holders: Maynooth
•Data on GMS – Medical Card holders
drawn under strict data protection
protocols from DOHC
• Anonymised address lists data matched
with GeoDirectory
•Address matching rate of around 77% in
Maynooth

• Interesting range of results around
average for the whole ED

•ED – single value 14.6 % (national average around 30%)
•Small Area – Range from 2% to 70% - noticeable internal variation and
good indicator of elderly or poor populations

•Strategic value in comparing datasets
•Important in policy analysis

Mapping invites comparison

Role of GIS
• Data integration
– Tally of cases in small areas requires
data on locations of cases and small
area boundaries
– ‘spatial join’ places small area codes
on the case records
– Counting is a standard database
operation

• Data display

Finally
• GIS encourages you to think about
space
• In a ‘conventional’ map of Ireland
areas (EDs, Counties…) are shown in
proportion to their physical size
• There are alternative ways of
displaying maps…

In a cartogram the areas are drawn
proportional to their populations –
this is a “density-equalised” map
projection

A map of the boundaries of counties and
electoral divisions – the areas are drawn
proportional to their physical size

By comparison, the more deprived
western area of Dublin, northern area
of Cork and south east of Limerick are
clearly apparent in the cartogram.

Haase’s deprivation index suggests that the
most deprived populations are in peripheral
rural areas in the north west.

Redrawing the data on the cartogram
reveals wide variation in the SMR, and also
uncovers the urban dimension which is
hidden in the conventional display

Patient data matched to Electoral Divisions –
hospital admissions for psychiatric problems – the
patchwork effect does not suggest any pattern

The cartogram reveals the urban nature
of the problem – those areas which are
more deprived are also those with
higher levels of illness/disability

Residents aged 15+ who are unable to work
through illness or disability: the map pattern
suggests that might be a largely rural
phenomenon

The roads, rail, and urban areas data have
been transformed into the same space as
the cartogram. Dublin and Cork seem
poorly provided with roads…

Other spatial information can be transformed
to the space of the density equalised
projection. Here is the familiar map of main
roads and towns.

Using a cartogram we see that the
incidence of lung cancer is randomly
spatially distributed

Lung cancer in males in New York State [Gastner & Newman: 2004]

Plotting locations of lung cancer cases, we
obtain a map which follows the underlying
population distribution – there appear to be
“clusters”

What about Ireland?
• Potentially hampered by challenge of
geocoding patient records
• Lack of small area population data
– EDs have an average population of 1150
– Wide variation in size (50-28000)
– Socially inhomogenous

Rural addresses
• A major challenge is non-uniqueness
of addresses in rural areas.
• For instance in Moy ED, 11 properties
share the address
‘Lackamore, Lahinch, Co. Clare’

What’s needed
• Postcoding should provide a
mechanism for geocoding patient data
with the granularity required for
planning the delivery of health care –
this has been the case in the UK for a
for long time
• Census data should be made available
for areas with small and similar
populations (cf: UK Output Areas)

Finally…
• GIS has an important role to play
in health studies
• Health data analysis is a rapidly
developing field
• However, in Ireland we are
hampered by data deficiencies –
now is the time for change

Maynooth: image in Google Earth

Central Dublin: image in Google Earth

Taoiseach’s Office: image in Google Earth

Downing Street, London: image in Google Earth

Maynooth: OSi orthophotograph added locally uisng KML

Sellafield: image in Google Earth

NCU – On Line Atlas of Cancer Mortality

The Philippine Institute
for Development
Studies makes an online atlas of socioeconomic indicators
available – contains
some health measures

The future
• Patient empowerment…

• GIS used to merge information from
regulatory agencies on 19196 landfill sites
• Sites buffered to 2km and postcodes
within buffer identified
• Postcoded live births (8.2x106), stillbirths
(43471) and congenital anomalies
(124,597) between 1982 and 1997
• Poisson regression modelling undertaken
• Evidence found for excess risk of
congenital anomaly and low birthweight

• Distance from each
census ward to
nearest hospital
computed
• Map shows number
of patients waiting
longer than 6
months for elective
inpatient care per
available and
unoccupied bed
within 60 minutes
travel time


Slide 22

GIS and Health
Martin Charlton1
Ronan Foley2
Dennis Pringle2
1National

Centre for Geocomputation
2Department of Geography
NUI Maynooth

Outline
• Preliminary observations on GIS
and health
• Examples of GIS in Health:
research and practice
• Mapping people not places

Patterns in Health Data
• Three main areas:
– Mapping of disease incidence
• Relative risk estimation

– Identification of disease clustering
• Surveillance

– Ecological analysis
• Relationship between incidence and
covariates
(Lawson et al, 2003)

Health Data
• Typically patient data is available
in two forms
• Individual – perhaps anonymised
to postcode/zip code level
• Aggregate – counts are available
for a set of zones
• The nature of the data affects the
nature of the analysis

Geographic Information Systems
• GIS is often confused with mapping
• A range of activities which includes
inter alia mapping and analysis
• Will typically involve several datasets
• May also involve several different
pieces of software

Mapping: EpiINFO
• This is a rather simple analysis and
mapping program
• It can be downloaded from the internet
• It doesn’t provide much in the way of
spatial data manipulation…
• … nor are its analytical capabilities very
advanced
• It is easy to use

Spatial Data Manipulation
• GIS isn’t merely concerned with
data display or query
• It’s useful in bringing together
different spatial datasets in the
first stages of an analysis
• This may well require a range of
different geometrical and
database operations

Analysis with Spatial Data
• Statistical analysis usually involves
some separate software.
• Spatial data have some properties
which mean that SPSS isn’t always
appropriate.
• There is now a range of specialist
software – much of it available for
download

GIS may be ideal for data manipulation, display and query,
but it’s not always ideal for analysis – in epidemiology a
variety of analytical software is available … WinBUGS,
GeoBUGS (MRC Biostatistics Unit, Cambridge)

Software to implement
particular methods – for
example scan statistics - can be
downloaded quickly and easily
from the Internet. Kulldorf’s
SaTScan system implements a
range of different scan
statistical models

GIS in Health in Practice
• Facility location – dialysis units
• Facility location – obs & gyn
• Data integration – schizophrenia
in Cavan
• Data manipulation and display –
GMS card holders in Maynooth

1: Provision of Dialysis Units
• There are 5 dialysis units for renal
patients in a region with a population
of about 3,000,000
• Major population centre has two units
about 5 miles apart
• Where would you locate an extra one?
• What would happen if one was
closed?

Location-allocation
• Location-allocation problems deal
with optimum location of facilities
• Attempt to minimise total
weighted travel time/distance
from users to facilities
• What data is needed?

Data requirements
• Digitised road network with classification
of road types
• Mean travel speeds on each type for
urban/non-urban roads
• Extents of urban areas
• Anonymised postcoded patient records
• Postcode->grid reference lookup
• Locations of hospitals in region (both with
and without dialysis units)

GIS processing
• Data integration
• Determine which segments of road network
are urban
• Determine travel times using road type
urban/non-urban speeds
• Tally numbers of patients at each node in
the road network
• Find nearest nodes in network to each
hospital
• Code hospitals as ‘existing’ or ‘potential’
units

Modelling/Analysis
• Carried out using standard GIS
functions
• Convenient ‘front end’ written
using system’s macro language
(save a lot of typing)
• Explore a range of alternatives

Hospital with
dialysis unit

Study region showing hospital locations and urban areas

Patients requiring dialysis (tallied to road nodes)

Classified road network – includes roads outside study area

•

Some alternative
scenarios:

(a) Optimal allocation
of patients
(b) Provide 1 extra
unit
(c) Provide 2 extra
units

(d) Close 1 unit in
city and reallocate
its patients

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

integrate data
process data
model and analyse
generate alternative scenarios
display the results

2: Facility Location
• Two hospitals both provided facilities
for obstetrics, gynaecology, paediatrics
and a special care baby unit.
• Was there a case for rationalisation and
improving the facilities at one hospital
only?
• Would closure disadvantage any
patients?

Data
• Locations of hospitals in study region
• Anonymised postcoded finished
consultant episodes for each of the
four specialities at each hospital
• Classified road network
• Speeds associated with each road
type

Data preparation
• Postcoded records allocated grid
references using Central Postcode
Directory (POSTZON)
• Vector data converted to raster
• Patient/specialty/hospital counts
produced for each raster
• Passage times computed for each
raster cell based on road
classification and speed estimates

Roads, urban areas, patient locations, hospitals in study area

Processing
• Travel time surfaces computed for
each hospital
• Each raster cell contains time
required to reach hospital in minutes
• Assumed walking speed in ‘non road’
areas

Travel time surface for hospital S – darker = shorter time

Travel time surface for hospital T

Time differences
• If we subtract the travel time surfaces areas
nearer one hospital will have positive
values for the difference, and areas nearer
the other will have negative ones
• Boundary between positive and negative
values is boundary of catchment for each
hospital.
• If you’re on the boundary (difference is
zero), it doesn’t matter which hospital you
choose

Roads, patient locations, hospital catchments

Indifference
• If we identify those rasters where the time
difference (ignoring the sign) is, say, 5 –
people in those areas are in the 5 minute
‘indifference’ zone.
• Patients might not be worried about a 5
minute increase in their travel time, they
might be worried about 30 minutes
• Indifference zones can be computed quite
easily

15-minute indifference zone between the two hospitals

Mean access time
• For each cell in the raster we can
compute the product of the number
of patients and the time required to
reach each hospital
• Dividing this by the number of
patients gives a patient weighted
average time for each hospital

Average travel times for the various specialties. There
isn’t much to chose between specialities with the
exception of paediatric episodes.

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

gather data
integrate data
process data
model data
display results

• All carried out using raster
operations

3: Schizophrenia in Cavan
• In 1996 Pringle, Waddington and
Youssef reported on evidence for the
causes of schizophrenia
• Ecological analysis of variation among
EDs
– Cases varied from 0-11 per ED
– Populations varied from 250-2750

• Poisson probabilities suggested that
one area had an unusually high number
of cases – these can be mapped

Male cases
superimposed on plot
of prevalence by ED

Male cases
superimposed on
rectified Landsat
image with ED
boundaries

4: GMS card holders: Maynooth
•Data on GMS – Medical Card holders
drawn under strict data protection
protocols from DOHC
• Anonymised address lists data matched
with GeoDirectory
•Address matching rate of around 77% in
Maynooth

• Interesting range of results around
average for the whole ED

•ED – single value 14.6 % (national average around 30%)
•Small Area – Range from 2% to 70% - noticeable internal variation and
good indicator of elderly or poor populations

•Strategic value in comparing datasets
•Important in policy analysis

Mapping invites comparison

Role of GIS
• Data integration
– Tally of cases in small areas requires
data on locations of cases and small
area boundaries
– ‘spatial join’ places small area codes
on the case records
– Counting is a standard database
operation

• Data display

Finally
• GIS encourages you to think about
space
• In a ‘conventional’ map of Ireland
areas (EDs, Counties…) are shown in
proportion to their physical size
• There are alternative ways of
displaying maps…

In a cartogram the areas are drawn
proportional to their populations –
this is a “density-equalised” map
projection

A map of the boundaries of counties and
electoral divisions – the areas are drawn
proportional to their physical size

By comparison, the more deprived
western area of Dublin, northern area
of Cork and south east of Limerick are
clearly apparent in the cartogram.

Haase’s deprivation index suggests that the
most deprived populations are in peripheral
rural areas in the north west.

Redrawing the data on the cartogram
reveals wide variation in the SMR, and also
uncovers the urban dimension which is
hidden in the conventional display

Patient data matched to Electoral Divisions –
hospital admissions for psychiatric problems – the
patchwork effect does not suggest any pattern

The cartogram reveals the urban nature
of the problem – those areas which are
more deprived are also those with
higher levels of illness/disability

Residents aged 15+ who are unable to work
through illness or disability: the map pattern
suggests that might be a largely rural
phenomenon

The roads, rail, and urban areas data have
been transformed into the same space as
the cartogram. Dublin and Cork seem
poorly provided with roads…

Other spatial information can be transformed
to the space of the density equalised
projection. Here is the familiar map of main
roads and towns.

Using a cartogram we see that the
incidence of lung cancer is randomly
spatially distributed

Lung cancer in males in New York State [Gastner & Newman: 2004]

Plotting locations of lung cancer cases, we
obtain a map which follows the underlying
population distribution – there appear to be
“clusters”

What about Ireland?
• Potentially hampered by challenge of
geocoding patient records
• Lack of small area population data
– EDs have an average population of 1150
– Wide variation in size (50-28000)
– Socially inhomogenous

Rural addresses
• A major challenge is non-uniqueness
of addresses in rural areas.
• For instance in Moy ED, 11 properties
share the address
‘Lackamore, Lahinch, Co. Clare’

What’s needed
• Postcoding should provide a
mechanism for geocoding patient data
with the granularity required for
planning the delivery of health care –
this has been the case in the UK for a
for long time
• Census data should be made available
for areas with small and similar
populations (cf: UK Output Areas)

Finally…
• GIS has an important role to play
in health studies
• Health data analysis is a rapidly
developing field
• However, in Ireland we are
hampered by data deficiencies –
now is the time for change

Maynooth: image in Google Earth

Central Dublin: image in Google Earth

Taoiseach’s Office: image in Google Earth

Downing Street, London: image in Google Earth

Maynooth: OSi orthophotograph added locally uisng KML

Sellafield: image in Google Earth

NCU – On Line Atlas of Cancer Mortality

The Philippine Institute
for Development
Studies makes an online atlas of socioeconomic indicators
available – contains
some health measures

The future
• Patient empowerment…

• GIS used to merge information from
regulatory agencies on 19196 landfill sites
• Sites buffered to 2km and postcodes
within buffer identified
• Postcoded live births (8.2x106), stillbirths
(43471) and congenital anomalies
(124,597) between 1982 and 1997
• Poisson regression modelling undertaken
• Evidence found for excess risk of
congenital anomaly and low birthweight

• Distance from each
census ward to
nearest hospital
computed
• Map shows number
of patients waiting
longer than 6
months for elective
inpatient care per
available and
unoccupied bed
within 60 minutes
travel time


Slide 23

GIS and Health
Martin Charlton1
Ronan Foley2
Dennis Pringle2
1National

Centre for Geocomputation
2Department of Geography
NUI Maynooth

Outline
• Preliminary observations on GIS
and health
• Examples of GIS in Health:
research and practice
• Mapping people not places

Patterns in Health Data
• Three main areas:
– Mapping of disease incidence
• Relative risk estimation

– Identification of disease clustering
• Surveillance

– Ecological analysis
• Relationship between incidence and
covariates
(Lawson et al, 2003)

Health Data
• Typically patient data is available
in two forms
• Individual – perhaps anonymised
to postcode/zip code level
• Aggregate – counts are available
for a set of zones
• The nature of the data affects the
nature of the analysis

Geographic Information Systems
• GIS is often confused with mapping
• A range of activities which includes
inter alia mapping and analysis
• Will typically involve several datasets
• May also involve several different
pieces of software

Mapping: EpiINFO
• This is a rather simple analysis and
mapping program
• It can be downloaded from the internet
• It doesn’t provide much in the way of
spatial data manipulation…
• … nor are its analytical capabilities very
advanced
• It is easy to use

Spatial Data Manipulation
• GIS isn’t merely concerned with
data display or query
• It’s useful in bringing together
different spatial datasets in the
first stages of an analysis
• This may well require a range of
different geometrical and
database operations

Analysis with Spatial Data
• Statistical analysis usually involves
some separate software.
• Spatial data have some properties
which mean that SPSS isn’t always
appropriate.
• There is now a range of specialist
software – much of it available for
download

GIS may be ideal for data manipulation, display and query,
but it’s not always ideal for analysis – in epidemiology a
variety of analytical software is available … WinBUGS,
GeoBUGS (MRC Biostatistics Unit, Cambridge)

Software to implement
particular methods – for
example scan statistics - can be
downloaded quickly and easily
from the Internet. Kulldorf’s
SaTScan system implements a
range of different scan
statistical models

GIS in Health in Practice
• Facility location – dialysis units
• Facility location – obs & gyn
• Data integration – schizophrenia
in Cavan
• Data manipulation and display –
GMS card holders in Maynooth

1: Provision of Dialysis Units
• There are 5 dialysis units for renal
patients in a region with a population
of about 3,000,000
• Major population centre has two units
about 5 miles apart
• Where would you locate an extra one?
• What would happen if one was
closed?

Location-allocation
• Location-allocation problems deal
with optimum location of facilities
• Attempt to minimise total
weighted travel time/distance
from users to facilities
• What data is needed?

Data requirements
• Digitised road network with classification
of road types
• Mean travel speeds on each type for
urban/non-urban roads
• Extents of urban areas
• Anonymised postcoded patient records
• Postcode->grid reference lookup
• Locations of hospitals in region (both with
and without dialysis units)

GIS processing
• Data integration
• Determine which segments of road network
are urban
• Determine travel times using road type
urban/non-urban speeds
• Tally numbers of patients at each node in
the road network
• Find nearest nodes in network to each
hospital
• Code hospitals as ‘existing’ or ‘potential’
units

Modelling/Analysis
• Carried out using standard GIS
functions
• Convenient ‘front end’ written
using system’s macro language
(save a lot of typing)
• Explore a range of alternatives

Hospital with
dialysis unit

Study region showing hospital locations and urban areas

Patients requiring dialysis (tallied to road nodes)

Classified road network – includes roads outside study area

•

Some alternative
scenarios:

(a) Optimal allocation
of patients
(b) Provide 1 extra
unit
(c) Provide 2 extra
units

(d) Close 1 unit in
city and reallocate
its patients

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

integrate data
process data
model and analyse
generate alternative scenarios
display the results

2: Facility Location
• Two hospitals both provided facilities
for obstetrics, gynaecology, paediatrics
and a special care baby unit.
• Was there a case for rationalisation and
improving the facilities at one hospital
only?
• Would closure disadvantage any
patients?

Data
• Locations of hospitals in study region
• Anonymised postcoded finished
consultant episodes for each of the
four specialities at each hospital
• Classified road network
• Speeds associated with each road
type

Data preparation
• Postcoded records allocated grid
references using Central Postcode
Directory (POSTZON)
• Vector data converted to raster
• Patient/specialty/hospital counts
produced for each raster
• Passage times computed for each
raster cell based on road
classification and speed estimates

Roads, urban areas, patient locations, hospitals in study area

Processing
• Travel time surfaces computed for
each hospital
• Each raster cell contains time
required to reach hospital in minutes
• Assumed walking speed in ‘non road’
areas

Travel time surface for hospital S – darker = shorter time

Travel time surface for hospital T

Time differences
• If we subtract the travel time surfaces areas
nearer one hospital will have positive
values for the difference, and areas nearer
the other will have negative ones
• Boundary between positive and negative
values is boundary of catchment for each
hospital.
• If you’re on the boundary (difference is
zero), it doesn’t matter which hospital you
choose

Roads, patient locations, hospital catchments

Indifference
• If we identify those rasters where the time
difference (ignoring the sign) is, say, 5 –
people in those areas are in the 5 minute
‘indifference’ zone.
• Patients might not be worried about a 5
minute increase in their travel time, they
might be worried about 30 minutes
• Indifference zones can be computed quite
easily

15-minute indifference zone between the two hospitals

Mean access time
• For each cell in the raster we can
compute the product of the number
of patients and the time required to
reach each hospital
• Dividing this by the number of
patients gives a patient weighted
average time for each hospital

Average travel times for the various specialties. There
isn’t much to chose between specialities with the
exception of paediatric episodes.

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

gather data
integrate data
process data
model data
display results

• All carried out using raster
operations

3: Schizophrenia in Cavan
• In 1996 Pringle, Waddington and
Youssef reported on evidence for the
causes of schizophrenia
• Ecological analysis of variation among
EDs
– Cases varied from 0-11 per ED
– Populations varied from 250-2750

• Poisson probabilities suggested that
one area had an unusually high number
of cases – these can be mapped

Male cases
superimposed on plot
of prevalence by ED

Male cases
superimposed on
rectified Landsat
image with ED
boundaries

4: GMS card holders: Maynooth
•Data on GMS – Medical Card holders
drawn under strict data protection
protocols from DOHC
• Anonymised address lists data matched
with GeoDirectory
•Address matching rate of around 77% in
Maynooth

• Interesting range of results around
average for the whole ED

•ED – single value 14.6 % (national average around 30%)
•Small Area – Range from 2% to 70% - noticeable internal variation and
good indicator of elderly or poor populations

•Strategic value in comparing datasets
•Important in policy analysis

Mapping invites comparison

Role of GIS
• Data integration
– Tally of cases in small areas requires
data on locations of cases and small
area boundaries
– ‘spatial join’ places small area codes
on the case records
– Counting is a standard database
operation

• Data display

Finally
• GIS encourages you to think about
space
• In a ‘conventional’ map of Ireland
areas (EDs, Counties…) are shown in
proportion to their physical size
• There are alternative ways of
displaying maps…

In a cartogram the areas are drawn
proportional to their populations –
this is a “density-equalised” map
projection

A map of the boundaries of counties and
electoral divisions – the areas are drawn
proportional to their physical size

By comparison, the more deprived
western area of Dublin, northern area
of Cork and south east of Limerick are
clearly apparent in the cartogram.

Haase’s deprivation index suggests that the
most deprived populations are in peripheral
rural areas in the north west.

Redrawing the data on the cartogram
reveals wide variation in the SMR, and also
uncovers the urban dimension which is
hidden in the conventional display

Patient data matched to Electoral Divisions –
hospital admissions for psychiatric problems – the
patchwork effect does not suggest any pattern

The cartogram reveals the urban nature
of the problem – those areas which are
more deprived are also those with
higher levels of illness/disability

Residents aged 15+ who are unable to work
through illness or disability: the map pattern
suggests that might be a largely rural
phenomenon

The roads, rail, and urban areas data have
been transformed into the same space as
the cartogram. Dublin and Cork seem
poorly provided with roads…

Other spatial information can be transformed
to the space of the density equalised
projection. Here is the familiar map of main
roads and towns.

Using a cartogram we see that the
incidence of lung cancer is randomly
spatially distributed

Lung cancer in males in New York State [Gastner & Newman: 2004]

Plotting locations of lung cancer cases, we
obtain a map which follows the underlying
population distribution – there appear to be
“clusters”

What about Ireland?
• Potentially hampered by challenge of
geocoding patient records
• Lack of small area population data
– EDs have an average population of 1150
– Wide variation in size (50-28000)
– Socially inhomogenous

Rural addresses
• A major challenge is non-uniqueness
of addresses in rural areas.
• For instance in Moy ED, 11 properties
share the address
‘Lackamore, Lahinch, Co. Clare’

What’s needed
• Postcoding should provide a
mechanism for geocoding patient data
with the granularity required for
planning the delivery of health care –
this has been the case in the UK for a
for long time
• Census data should be made available
for areas with small and similar
populations (cf: UK Output Areas)

Finally…
• GIS has an important role to play
in health studies
• Health data analysis is a rapidly
developing field
• However, in Ireland we are
hampered by data deficiencies –
now is the time for change

Maynooth: image in Google Earth

Central Dublin: image in Google Earth

Taoiseach’s Office: image in Google Earth

Downing Street, London: image in Google Earth

Maynooth: OSi orthophotograph added locally uisng KML

Sellafield: image in Google Earth

NCU – On Line Atlas of Cancer Mortality

The Philippine Institute
for Development
Studies makes an online atlas of socioeconomic indicators
available – contains
some health measures

The future
• Patient empowerment…

• GIS used to merge information from
regulatory agencies on 19196 landfill sites
• Sites buffered to 2km and postcodes
within buffer identified
• Postcoded live births (8.2x106), stillbirths
(43471) and congenital anomalies
(124,597) between 1982 and 1997
• Poisson regression modelling undertaken
• Evidence found for excess risk of
congenital anomaly and low birthweight

• Distance from each
census ward to
nearest hospital
computed
• Map shows number
of patients waiting
longer than 6
months for elective
inpatient care per
available and
unoccupied bed
within 60 minutes
travel time


Slide 24

GIS and Health
Martin Charlton1
Ronan Foley2
Dennis Pringle2
1National

Centre for Geocomputation
2Department of Geography
NUI Maynooth

Outline
• Preliminary observations on GIS
and health
• Examples of GIS in Health:
research and practice
• Mapping people not places

Patterns in Health Data
• Three main areas:
– Mapping of disease incidence
• Relative risk estimation

– Identification of disease clustering
• Surveillance

– Ecological analysis
• Relationship between incidence and
covariates
(Lawson et al, 2003)

Health Data
• Typically patient data is available
in two forms
• Individual – perhaps anonymised
to postcode/zip code level
• Aggregate – counts are available
for a set of zones
• The nature of the data affects the
nature of the analysis

Geographic Information Systems
• GIS is often confused with mapping
• A range of activities which includes
inter alia mapping and analysis
• Will typically involve several datasets
• May also involve several different
pieces of software

Mapping: EpiINFO
• This is a rather simple analysis and
mapping program
• It can be downloaded from the internet
• It doesn’t provide much in the way of
spatial data manipulation…
• … nor are its analytical capabilities very
advanced
• It is easy to use

Spatial Data Manipulation
• GIS isn’t merely concerned with
data display or query
• It’s useful in bringing together
different spatial datasets in the
first stages of an analysis
• This may well require a range of
different geometrical and
database operations

Analysis with Spatial Data
• Statistical analysis usually involves
some separate software.
• Spatial data have some properties
which mean that SPSS isn’t always
appropriate.
• There is now a range of specialist
software – much of it available for
download

GIS may be ideal for data manipulation, display and query,
but it’s not always ideal for analysis – in epidemiology a
variety of analytical software is available … WinBUGS,
GeoBUGS (MRC Biostatistics Unit, Cambridge)

Software to implement
particular methods – for
example scan statistics - can be
downloaded quickly and easily
from the Internet. Kulldorf’s
SaTScan system implements a
range of different scan
statistical models

GIS in Health in Practice
• Facility location – dialysis units
• Facility location – obs & gyn
• Data integration – schizophrenia
in Cavan
• Data manipulation and display –
GMS card holders in Maynooth

1: Provision of Dialysis Units
• There are 5 dialysis units for renal
patients in a region with a population
of about 3,000,000
• Major population centre has two units
about 5 miles apart
• Where would you locate an extra one?
• What would happen if one was
closed?

Location-allocation
• Location-allocation problems deal
with optimum location of facilities
• Attempt to minimise total
weighted travel time/distance
from users to facilities
• What data is needed?

Data requirements
• Digitised road network with classification
of road types
• Mean travel speeds on each type for
urban/non-urban roads
• Extents of urban areas
• Anonymised postcoded patient records
• Postcode->grid reference lookup
• Locations of hospitals in region (both with
and without dialysis units)

GIS processing
• Data integration
• Determine which segments of road network
are urban
• Determine travel times using road type
urban/non-urban speeds
• Tally numbers of patients at each node in
the road network
• Find nearest nodes in network to each
hospital
• Code hospitals as ‘existing’ or ‘potential’
units

Modelling/Analysis
• Carried out using standard GIS
functions
• Convenient ‘front end’ written
using system’s macro language
(save a lot of typing)
• Explore a range of alternatives

Hospital with
dialysis unit

Study region showing hospital locations and urban areas

Patients requiring dialysis (tallied to road nodes)

Classified road network – includes roads outside study area

•

Some alternative
scenarios:

(a) Optimal allocation
of patients
(b) Provide 1 extra
unit
(c) Provide 2 extra
units

(d) Close 1 unit in
city and reallocate
its patients

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

integrate data
process data
model and analyse
generate alternative scenarios
display the results

2: Facility Location
• Two hospitals both provided facilities
for obstetrics, gynaecology, paediatrics
and a special care baby unit.
• Was there a case for rationalisation and
improving the facilities at one hospital
only?
• Would closure disadvantage any
patients?

Data
• Locations of hospitals in study region
• Anonymised postcoded finished
consultant episodes for each of the
four specialities at each hospital
• Classified road network
• Speeds associated with each road
type

Data preparation
• Postcoded records allocated grid
references using Central Postcode
Directory (POSTZON)
• Vector data converted to raster
• Patient/specialty/hospital counts
produced for each raster
• Passage times computed for each
raster cell based on road
classification and speed estimates

Roads, urban areas, patient locations, hospitals in study area

Processing
• Travel time surfaces computed for
each hospital
• Each raster cell contains time
required to reach hospital in minutes
• Assumed walking speed in ‘non road’
areas

Travel time surface for hospital S – darker = shorter time

Travel time surface for hospital T

Time differences
• If we subtract the travel time surfaces areas
nearer one hospital will have positive
values for the difference, and areas nearer
the other will have negative ones
• Boundary between positive and negative
values is boundary of catchment for each
hospital.
• If you’re on the boundary (difference is
zero), it doesn’t matter which hospital you
choose

Roads, patient locations, hospital catchments

Indifference
• If we identify those rasters where the time
difference (ignoring the sign) is, say, 5 –
people in those areas are in the 5 minute
‘indifference’ zone.
• Patients might not be worried about a 5
minute increase in their travel time, they
might be worried about 30 minutes
• Indifference zones can be computed quite
easily

15-minute indifference zone between the two hospitals

Mean access time
• For each cell in the raster we can
compute the product of the number
of patients and the time required to
reach each hospital
• Dividing this by the number of
patients gives a patient weighted
average time for each hospital

Average travel times for the various specialties. There
isn’t much to chose between specialities with the
exception of paediatric episodes.

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

gather data
integrate data
process data
model data
display results

• All carried out using raster
operations

3: Schizophrenia in Cavan
• In 1996 Pringle, Waddington and
Youssef reported on evidence for the
causes of schizophrenia
• Ecological analysis of variation among
EDs
– Cases varied from 0-11 per ED
– Populations varied from 250-2750

• Poisson probabilities suggested that
one area had an unusually high number
of cases – these can be mapped

Male cases
superimposed on plot
of prevalence by ED

Male cases
superimposed on
rectified Landsat
image with ED
boundaries

4: GMS card holders: Maynooth
•Data on GMS – Medical Card holders
drawn under strict data protection
protocols from DOHC
• Anonymised address lists data matched
with GeoDirectory
•Address matching rate of around 77% in
Maynooth

• Interesting range of results around
average for the whole ED

•ED – single value 14.6 % (national average around 30%)
•Small Area – Range from 2% to 70% - noticeable internal variation and
good indicator of elderly or poor populations

•Strategic value in comparing datasets
•Important in policy analysis

Mapping invites comparison

Role of GIS
• Data integration
– Tally of cases in small areas requires
data on locations of cases and small
area boundaries
– ‘spatial join’ places small area codes
on the case records
– Counting is a standard database
operation

• Data display

Finally
• GIS encourages you to think about
space
• In a ‘conventional’ map of Ireland
areas (EDs, Counties…) are shown in
proportion to their physical size
• There are alternative ways of
displaying maps…

In a cartogram the areas are drawn
proportional to their populations –
this is a “density-equalised” map
projection

A map of the boundaries of counties and
electoral divisions – the areas are drawn
proportional to their physical size

By comparison, the more deprived
western area of Dublin, northern area
of Cork and south east of Limerick are
clearly apparent in the cartogram.

Haase’s deprivation index suggests that the
most deprived populations are in peripheral
rural areas in the north west.

Redrawing the data on the cartogram
reveals wide variation in the SMR, and also
uncovers the urban dimension which is
hidden in the conventional display

Patient data matched to Electoral Divisions –
hospital admissions for psychiatric problems – the
patchwork effect does not suggest any pattern

The cartogram reveals the urban nature
of the problem – those areas which are
more deprived are also those with
higher levels of illness/disability

Residents aged 15+ who are unable to work
through illness or disability: the map pattern
suggests that might be a largely rural
phenomenon

The roads, rail, and urban areas data have
been transformed into the same space as
the cartogram. Dublin and Cork seem
poorly provided with roads…

Other spatial information can be transformed
to the space of the density equalised
projection. Here is the familiar map of main
roads and towns.

Using a cartogram we see that the
incidence of lung cancer is randomly
spatially distributed

Lung cancer in males in New York State [Gastner & Newman: 2004]

Plotting locations of lung cancer cases, we
obtain a map which follows the underlying
population distribution – there appear to be
“clusters”

What about Ireland?
• Potentially hampered by challenge of
geocoding patient records
• Lack of small area population data
– EDs have an average population of 1150
– Wide variation in size (50-28000)
– Socially inhomogenous

Rural addresses
• A major challenge is non-uniqueness
of addresses in rural areas.
• For instance in Moy ED, 11 properties
share the address
‘Lackamore, Lahinch, Co. Clare’

What’s needed
• Postcoding should provide a
mechanism for geocoding patient data
with the granularity required for
planning the delivery of health care –
this has been the case in the UK for a
for long time
• Census data should be made available
for areas with small and similar
populations (cf: UK Output Areas)

Finally…
• GIS has an important role to play
in health studies
• Health data analysis is a rapidly
developing field
• However, in Ireland we are
hampered by data deficiencies –
now is the time for change

Maynooth: image in Google Earth

Central Dublin: image in Google Earth

Taoiseach’s Office: image in Google Earth

Downing Street, London: image in Google Earth

Maynooth: OSi orthophotograph added locally uisng KML

Sellafield: image in Google Earth

NCU – On Line Atlas of Cancer Mortality

The Philippine Institute
for Development
Studies makes an online atlas of socioeconomic indicators
available – contains
some health measures

The future
• Patient empowerment…

• GIS used to merge information from
regulatory agencies on 19196 landfill sites
• Sites buffered to 2km and postcodes
within buffer identified
• Postcoded live births (8.2x106), stillbirths
(43471) and congenital anomalies
(124,597) between 1982 and 1997
• Poisson regression modelling undertaken
• Evidence found for excess risk of
congenital anomaly and low birthweight

• Distance from each
census ward to
nearest hospital
computed
• Map shows number
of patients waiting
longer than 6
months for elective
inpatient care per
available and
unoccupied bed
within 60 minutes
travel time


Slide 25

GIS and Health
Martin Charlton1
Ronan Foley2
Dennis Pringle2
1National

Centre for Geocomputation
2Department of Geography
NUI Maynooth

Outline
• Preliminary observations on GIS
and health
• Examples of GIS in Health:
research and practice
• Mapping people not places

Patterns in Health Data
• Three main areas:
– Mapping of disease incidence
• Relative risk estimation

– Identification of disease clustering
• Surveillance

– Ecological analysis
• Relationship between incidence and
covariates
(Lawson et al, 2003)

Health Data
• Typically patient data is available
in two forms
• Individual – perhaps anonymised
to postcode/zip code level
• Aggregate – counts are available
for a set of zones
• The nature of the data affects the
nature of the analysis

Geographic Information Systems
• GIS is often confused with mapping
• A range of activities which includes
inter alia mapping and analysis
• Will typically involve several datasets
• May also involve several different
pieces of software

Mapping: EpiINFO
• This is a rather simple analysis and
mapping program
• It can be downloaded from the internet
• It doesn’t provide much in the way of
spatial data manipulation…
• … nor are its analytical capabilities very
advanced
• It is easy to use

Spatial Data Manipulation
• GIS isn’t merely concerned with
data display or query
• It’s useful in bringing together
different spatial datasets in the
first stages of an analysis
• This may well require a range of
different geometrical and
database operations

Analysis with Spatial Data
• Statistical analysis usually involves
some separate software.
• Spatial data have some properties
which mean that SPSS isn’t always
appropriate.
• There is now a range of specialist
software – much of it available for
download

GIS may be ideal for data manipulation, display and query,
but it’s not always ideal for analysis – in epidemiology a
variety of analytical software is available … WinBUGS,
GeoBUGS (MRC Biostatistics Unit, Cambridge)

Software to implement
particular methods – for
example scan statistics - can be
downloaded quickly and easily
from the Internet. Kulldorf’s
SaTScan system implements a
range of different scan
statistical models

GIS in Health in Practice
• Facility location – dialysis units
• Facility location – obs & gyn
• Data integration – schizophrenia
in Cavan
• Data manipulation and display –
GMS card holders in Maynooth

1: Provision of Dialysis Units
• There are 5 dialysis units for renal
patients in a region with a population
of about 3,000,000
• Major population centre has two units
about 5 miles apart
• Where would you locate an extra one?
• What would happen if one was
closed?

Location-allocation
• Location-allocation problems deal
with optimum location of facilities
• Attempt to minimise total
weighted travel time/distance
from users to facilities
• What data is needed?

Data requirements
• Digitised road network with classification
of road types
• Mean travel speeds on each type for
urban/non-urban roads
• Extents of urban areas
• Anonymised postcoded patient records
• Postcode->grid reference lookup
• Locations of hospitals in region (both with
and without dialysis units)

GIS processing
• Data integration
• Determine which segments of road network
are urban
• Determine travel times using road type
urban/non-urban speeds
• Tally numbers of patients at each node in
the road network
• Find nearest nodes in network to each
hospital
• Code hospitals as ‘existing’ or ‘potential’
units

Modelling/Analysis
• Carried out using standard GIS
functions
• Convenient ‘front end’ written
using system’s macro language
(save a lot of typing)
• Explore a range of alternatives

Hospital with
dialysis unit

Study region showing hospital locations and urban areas

Patients requiring dialysis (tallied to road nodes)

Classified road network – includes roads outside study area

•

Some alternative
scenarios:

(a) Optimal allocation
of patients
(b) Provide 1 extra
unit
(c) Provide 2 extra
units

(d) Close 1 unit in
city and reallocate
its patients

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

integrate data
process data
model and analyse
generate alternative scenarios
display the results

2: Facility Location
• Two hospitals both provided facilities
for obstetrics, gynaecology, paediatrics
and a special care baby unit.
• Was there a case for rationalisation and
improving the facilities at one hospital
only?
• Would closure disadvantage any
patients?

Data
• Locations of hospitals in study region
• Anonymised postcoded finished
consultant episodes for each of the
four specialities at each hospital
• Classified road network
• Speeds associated with each road
type

Data preparation
• Postcoded records allocated grid
references using Central Postcode
Directory (POSTZON)
• Vector data converted to raster
• Patient/specialty/hospital counts
produced for each raster
• Passage times computed for each
raster cell based on road
classification and speed estimates

Roads, urban areas, patient locations, hospitals in study area

Processing
• Travel time surfaces computed for
each hospital
• Each raster cell contains time
required to reach hospital in minutes
• Assumed walking speed in ‘non road’
areas

Travel time surface for hospital S – darker = shorter time

Travel time surface for hospital T

Time differences
• If we subtract the travel time surfaces areas
nearer one hospital will have positive
values for the difference, and areas nearer
the other will have negative ones
• Boundary between positive and negative
values is boundary of catchment for each
hospital.
• If you’re on the boundary (difference is
zero), it doesn’t matter which hospital you
choose

Roads, patient locations, hospital catchments

Indifference
• If we identify those rasters where the time
difference (ignoring the sign) is, say, 5 –
people in those areas are in the 5 minute
‘indifference’ zone.
• Patients might not be worried about a 5
minute increase in their travel time, they
might be worried about 30 minutes
• Indifference zones can be computed quite
easily

15-minute indifference zone between the two hospitals

Mean access time
• For each cell in the raster we can
compute the product of the number
of patients and the time required to
reach each hospital
• Dividing this by the number of
patients gives a patient weighted
average time for each hospital

Average travel times for the various specialties. There
isn’t much to chose between specialities with the
exception of paediatric episodes.

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

gather data
integrate data
process data
model data
display results

• All carried out using raster
operations

3: Schizophrenia in Cavan
• In 1996 Pringle, Waddington and
Youssef reported on evidence for the
causes of schizophrenia
• Ecological analysis of variation among
EDs
– Cases varied from 0-11 per ED
– Populations varied from 250-2750

• Poisson probabilities suggested that
one area had an unusually high number
of cases – these can be mapped

Male cases
superimposed on plot
of prevalence by ED

Male cases
superimposed on
rectified Landsat
image with ED
boundaries

4: GMS card holders: Maynooth
•Data on GMS – Medical Card holders
drawn under strict data protection
protocols from DOHC
• Anonymised address lists data matched
with GeoDirectory
•Address matching rate of around 77% in
Maynooth

• Interesting range of results around
average for the whole ED

•ED – single value 14.6 % (national average around 30%)
•Small Area – Range from 2% to 70% - noticeable internal variation and
good indicator of elderly or poor populations

•Strategic value in comparing datasets
•Important in policy analysis

Mapping invites comparison

Role of GIS
• Data integration
– Tally of cases in small areas requires
data on locations of cases and small
area boundaries
– ‘spatial join’ places small area codes
on the case records
– Counting is a standard database
operation

• Data display

Finally
• GIS encourages you to think about
space
• In a ‘conventional’ map of Ireland
areas (EDs, Counties…) are shown in
proportion to their physical size
• There are alternative ways of
displaying maps…

In a cartogram the areas are drawn
proportional to their populations –
this is a “density-equalised” map
projection

A map of the boundaries of counties and
electoral divisions – the areas are drawn
proportional to their physical size

By comparison, the more deprived
western area of Dublin, northern area
of Cork and south east of Limerick are
clearly apparent in the cartogram.

Haase’s deprivation index suggests that the
most deprived populations are in peripheral
rural areas in the north west.

Redrawing the data on the cartogram
reveals wide variation in the SMR, and also
uncovers the urban dimension which is
hidden in the conventional display

Patient data matched to Electoral Divisions –
hospital admissions for psychiatric problems – the
patchwork effect does not suggest any pattern

The cartogram reveals the urban nature
of the problem – those areas which are
more deprived are also those with
higher levels of illness/disability

Residents aged 15+ who are unable to work
through illness or disability: the map pattern
suggests that might be a largely rural
phenomenon

The roads, rail, and urban areas data have
been transformed into the same space as
the cartogram. Dublin and Cork seem
poorly provided with roads…

Other spatial information can be transformed
to the space of the density equalised
projection. Here is the familiar map of main
roads and towns.

Using a cartogram we see that the
incidence of lung cancer is randomly
spatially distributed

Lung cancer in males in New York State [Gastner & Newman: 2004]

Plotting locations of lung cancer cases, we
obtain a map which follows the underlying
population distribution – there appear to be
“clusters”

What about Ireland?
• Potentially hampered by challenge of
geocoding patient records
• Lack of small area population data
– EDs have an average population of 1150
– Wide variation in size (50-28000)
– Socially inhomogenous

Rural addresses
• A major challenge is non-uniqueness
of addresses in rural areas.
• For instance in Moy ED, 11 properties
share the address
‘Lackamore, Lahinch, Co. Clare’

What’s needed
• Postcoding should provide a
mechanism for geocoding patient data
with the granularity required for
planning the delivery of health care –
this has been the case in the UK for a
for long time
• Census data should be made available
for areas with small and similar
populations (cf: UK Output Areas)

Finally…
• GIS has an important role to play
in health studies
• Health data analysis is a rapidly
developing field
• However, in Ireland we are
hampered by data deficiencies –
now is the time for change

Maynooth: image in Google Earth

Central Dublin: image in Google Earth

Taoiseach’s Office: image in Google Earth

Downing Street, London: image in Google Earth

Maynooth: OSi orthophotograph added locally uisng KML

Sellafield: image in Google Earth

NCU – On Line Atlas of Cancer Mortality

The Philippine Institute
for Development
Studies makes an online atlas of socioeconomic indicators
available – contains
some health measures

The future
• Patient empowerment…

• GIS used to merge information from
regulatory agencies on 19196 landfill sites
• Sites buffered to 2km and postcodes
within buffer identified
• Postcoded live births (8.2x106), stillbirths
(43471) and congenital anomalies
(124,597) between 1982 and 1997
• Poisson regression modelling undertaken
• Evidence found for excess risk of
congenital anomaly and low birthweight

• Distance from each
census ward to
nearest hospital
computed
• Map shows number
of patients waiting
longer than 6
months for elective
inpatient care per
available and
unoccupied bed
within 60 minutes
travel time


Slide 26

GIS and Health
Martin Charlton1
Ronan Foley2
Dennis Pringle2
1National

Centre for Geocomputation
2Department of Geography
NUI Maynooth

Outline
• Preliminary observations on GIS
and health
• Examples of GIS in Health:
research and practice
• Mapping people not places

Patterns in Health Data
• Three main areas:
– Mapping of disease incidence
• Relative risk estimation

– Identification of disease clustering
• Surveillance

– Ecological analysis
• Relationship between incidence and
covariates
(Lawson et al, 2003)

Health Data
• Typically patient data is available
in two forms
• Individual – perhaps anonymised
to postcode/zip code level
• Aggregate – counts are available
for a set of zones
• The nature of the data affects the
nature of the analysis

Geographic Information Systems
• GIS is often confused with mapping
• A range of activities which includes
inter alia mapping and analysis
• Will typically involve several datasets
• May also involve several different
pieces of software

Mapping: EpiINFO
• This is a rather simple analysis and
mapping program
• It can be downloaded from the internet
• It doesn’t provide much in the way of
spatial data manipulation…
• … nor are its analytical capabilities very
advanced
• It is easy to use

Spatial Data Manipulation
• GIS isn’t merely concerned with
data display or query
• It’s useful in bringing together
different spatial datasets in the
first stages of an analysis
• This may well require a range of
different geometrical and
database operations

Analysis with Spatial Data
• Statistical analysis usually involves
some separate software.
• Spatial data have some properties
which mean that SPSS isn’t always
appropriate.
• There is now a range of specialist
software – much of it available for
download

GIS may be ideal for data manipulation, display and query,
but it’s not always ideal for analysis – in epidemiology a
variety of analytical software is available … WinBUGS,
GeoBUGS (MRC Biostatistics Unit, Cambridge)

Software to implement
particular methods – for
example scan statistics - can be
downloaded quickly and easily
from the Internet. Kulldorf’s
SaTScan system implements a
range of different scan
statistical models

GIS in Health in Practice
• Facility location – dialysis units
• Facility location – obs & gyn
• Data integration – schizophrenia
in Cavan
• Data manipulation and display –
GMS card holders in Maynooth

1: Provision of Dialysis Units
• There are 5 dialysis units for renal
patients in a region with a population
of about 3,000,000
• Major population centre has two units
about 5 miles apart
• Where would you locate an extra one?
• What would happen if one was
closed?

Location-allocation
• Location-allocation problems deal
with optimum location of facilities
• Attempt to minimise total
weighted travel time/distance
from users to facilities
• What data is needed?

Data requirements
• Digitised road network with classification
of road types
• Mean travel speeds on each type for
urban/non-urban roads
• Extents of urban areas
• Anonymised postcoded patient records
• Postcode->grid reference lookup
• Locations of hospitals in region (both with
and without dialysis units)

GIS processing
• Data integration
• Determine which segments of road network
are urban
• Determine travel times using road type
urban/non-urban speeds
• Tally numbers of patients at each node in
the road network
• Find nearest nodes in network to each
hospital
• Code hospitals as ‘existing’ or ‘potential’
units

Modelling/Analysis
• Carried out using standard GIS
functions
• Convenient ‘front end’ written
using system’s macro language
(save a lot of typing)
• Explore a range of alternatives

Hospital with
dialysis unit

Study region showing hospital locations and urban areas

Patients requiring dialysis (tallied to road nodes)

Classified road network – includes roads outside study area

•

Some alternative
scenarios:

(a) Optimal allocation
of patients
(b) Provide 1 extra
unit
(c) Provide 2 extra
units

(d) Close 1 unit in
city and reallocate
its patients

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

integrate data
process data
model and analyse
generate alternative scenarios
display the results

2: Facility Location
• Two hospitals both provided facilities
for obstetrics, gynaecology, paediatrics
and a special care baby unit.
• Was there a case for rationalisation and
improving the facilities at one hospital
only?
• Would closure disadvantage any
patients?

Data
• Locations of hospitals in study region
• Anonymised postcoded finished
consultant episodes for each of the
four specialities at each hospital
• Classified road network
• Speeds associated with each road
type

Data preparation
• Postcoded records allocated grid
references using Central Postcode
Directory (POSTZON)
• Vector data converted to raster
• Patient/specialty/hospital counts
produced for each raster
• Passage times computed for each
raster cell based on road
classification and speed estimates

Roads, urban areas, patient locations, hospitals in study area

Processing
• Travel time surfaces computed for
each hospital
• Each raster cell contains time
required to reach hospital in minutes
• Assumed walking speed in ‘non road’
areas

Travel time surface for hospital S – darker = shorter time

Travel time surface for hospital T

Time differences
• If we subtract the travel time surfaces areas
nearer one hospital will have positive
values for the difference, and areas nearer
the other will have negative ones
• Boundary between positive and negative
values is boundary of catchment for each
hospital.
• If you’re on the boundary (difference is
zero), it doesn’t matter which hospital you
choose

Roads, patient locations, hospital catchments

Indifference
• If we identify those rasters where the time
difference (ignoring the sign) is, say, 5 –
people in those areas are in the 5 minute
‘indifference’ zone.
• Patients might not be worried about a 5
minute increase in their travel time, they
might be worried about 30 minutes
• Indifference zones can be computed quite
easily

15-minute indifference zone between the two hospitals

Mean access time
• For each cell in the raster we can
compute the product of the number
of patients and the time required to
reach each hospital
• Dividing this by the number of
patients gives a patient weighted
average time for each hospital

Average travel times for the various specialties. There
isn’t much to chose between specialities with the
exception of paediatric episodes.

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

gather data
integrate data
process data
model data
display results

• All carried out using raster
operations

3: Schizophrenia in Cavan
• In 1996 Pringle, Waddington and
Youssef reported on evidence for the
causes of schizophrenia
• Ecological analysis of variation among
EDs
– Cases varied from 0-11 per ED
– Populations varied from 250-2750

• Poisson probabilities suggested that
one area had an unusually high number
of cases – these can be mapped

Male cases
superimposed on plot
of prevalence by ED

Male cases
superimposed on
rectified Landsat
image with ED
boundaries

4: GMS card holders: Maynooth
•Data on GMS – Medical Card holders
drawn under strict data protection
protocols from DOHC
• Anonymised address lists data matched
with GeoDirectory
•Address matching rate of around 77% in
Maynooth

• Interesting range of results around
average for the whole ED

•ED – single value 14.6 % (national average around 30%)
•Small Area – Range from 2% to 70% - noticeable internal variation and
good indicator of elderly or poor populations

•Strategic value in comparing datasets
•Important in policy analysis

Mapping invites comparison

Role of GIS
• Data integration
– Tally of cases in small areas requires
data on locations of cases and small
area boundaries
– ‘spatial join’ places small area codes
on the case records
– Counting is a standard database
operation

• Data display

Finally
• GIS encourages you to think about
space
• In a ‘conventional’ map of Ireland
areas (EDs, Counties…) are shown in
proportion to their physical size
• There are alternative ways of
displaying maps…

In a cartogram the areas are drawn
proportional to their populations –
this is a “density-equalised” map
projection

A map of the boundaries of counties and
electoral divisions – the areas are drawn
proportional to their physical size

By comparison, the more deprived
western area of Dublin, northern area
of Cork and south east of Limerick are
clearly apparent in the cartogram.

Haase’s deprivation index suggests that the
most deprived populations are in peripheral
rural areas in the north west.

Redrawing the data on the cartogram
reveals wide variation in the SMR, and also
uncovers the urban dimension which is
hidden in the conventional display

Patient data matched to Electoral Divisions –
hospital admissions for psychiatric problems – the
patchwork effect does not suggest any pattern

The cartogram reveals the urban nature
of the problem – those areas which are
more deprived are also those with
higher levels of illness/disability

Residents aged 15+ who are unable to work
through illness or disability: the map pattern
suggests that might be a largely rural
phenomenon

The roads, rail, and urban areas data have
been transformed into the same space as
the cartogram. Dublin and Cork seem
poorly provided with roads…

Other spatial information can be transformed
to the space of the density equalised
projection. Here is the familiar map of main
roads and towns.

Using a cartogram we see that the
incidence of lung cancer is randomly
spatially distributed

Lung cancer in males in New York State [Gastner & Newman: 2004]

Plotting locations of lung cancer cases, we
obtain a map which follows the underlying
population distribution – there appear to be
“clusters”

What about Ireland?
• Potentially hampered by challenge of
geocoding patient records
• Lack of small area population data
– EDs have an average population of 1150
– Wide variation in size (50-28000)
– Socially inhomogenous

Rural addresses
• A major challenge is non-uniqueness
of addresses in rural areas.
• For instance in Moy ED, 11 properties
share the address
‘Lackamore, Lahinch, Co. Clare’

What’s needed
• Postcoding should provide a
mechanism for geocoding patient data
with the granularity required for
planning the delivery of health care –
this has been the case in the UK for a
for long time
• Census data should be made available
for areas with small and similar
populations (cf: UK Output Areas)

Finally…
• GIS has an important role to play
in health studies
• Health data analysis is a rapidly
developing field
• However, in Ireland we are
hampered by data deficiencies –
now is the time for change

Maynooth: image in Google Earth

Central Dublin: image in Google Earth

Taoiseach’s Office: image in Google Earth

Downing Street, London: image in Google Earth

Maynooth: OSi orthophotograph added locally uisng KML

Sellafield: image in Google Earth

NCU – On Line Atlas of Cancer Mortality

The Philippine Institute
for Development
Studies makes an online atlas of socioeconomic indicators
available – contains
some health measures

The future
• Patient empowerment…

• GIS used to merge information from
regulatory agencies on 19196 landfill sites
• Sites buffered to 2km and postcodes
within buffer identified
• Postcoded live births (8.2x106), stillbirths
(43471) and congenital anomalies
(124,597) between 1982 and 1997
• Poisson regression modelling undertaken
• Evidence found for excess risk of
congenital anomaly and low birthweight

• Distance from each
census ward to
nearest hospital
computed
• Map shows number
of patients waiting
longer than 6
months for elective
inpatient care per
available and
unoccupied bed
within 60 minutes
travel time


Slide 27

GIS and Health
Martin Charlton1
Ronan Foley2
Dennis Pringle2
1National

Centre for Geocomputation
2Department of Geography
NUI Maynooth

Outline
• Preliminary observations on GIS
and health
• Examples of GIS in Health:
research and practice
• Mapping people not places

Patterns in Health Data
• Three main areas:
– Mapping of disease incidence
• Relative risk estimation

– Identification of disease clustering
• Surveillance

– Ecological analysis
• Relationship between incidence and
covariates
(Lawson et al, 2003)

Health Data
• Typically patient data is available
in two forms
• Individual – perhaps anonymised
to postcode/zip code level
• Aggregate – counts are available
for a set of zones
• The nature of the data affects the
nature of the analysis

Geographic Information Systems
• GIS is often confused with mapping
• A range of activities which includes
inter alia mapping and analysis
• Will typically involve several datasets
• May also involve several different
pieces of software

Mapping: EpiINFO
• This is a rather simple analysis and
mapping program
• It can be downloaded from the internet
• It doesn’t provide much in the way of
spatial data manipulation…
• … nor are its analytical capabilities very
advanced
• It is easy to use

Spatial Data Manipulation
• GIS isn’t merely concerned with
data display or query
• It’s useful in bringing together
different spatial datasets in the
first stages of an analysis
• This may well require a range of
different geometrical and
database operations

Analysis with Spatial Data
• Statistical analysis usually involves
some separate software.
• Spatial data have some properties
which mean that SPSS isn’t always
appropriate.
• There is now a range of specialist
software – much of it available for
download

GIS may be ideal for data manipulation, display and query,
but it’s not always ideal for analysis – in epidemiology a
variety of analytical software is available … WinBUGS,
GeoBUGS (MRC Biostatistics Unit, Cambridge)

Software to implement
particular methods – for
example scan statistics - can be
downloaded quickly and easily
from the Internet. Kulldorf’s
SaTScan system implements a
range of different scan
statistical models

GIS in Health in Practice
• Facility location – dialysis units
• Facility location – obs & gyn
• Data integration – schizophrenia
in Cavan
• Data manipulation and display –
GMS card holders in Maynooth

1: Provision of Dialysis Units
• There are 5 dialysis units for renal
patients in a region with a population
of about 3,000,000
• Major population centre has two units
about 5 miles apart
• Where would you locate an extra one?
• What would happen if one was
closed?

Location-allocation
• Location-allocation problems deal
with optimum location of facilities
• Attempt to minimise total
weighted travel time/distance
from users to facilities
• What data is needed?

Data requirements
• Digitised road network with classification
of road types
• Mean travel speeds on each type for
urban/non-urban roads
• Extents of urban areas
• Anonymised postcoded patient records
• Postcode->grid reference lookup
• Locations of hospitals in region (both with
and without dialysis units)

GIS processing
• Data integration
• Determine which segments of road network
are urban
• Determine travel times using road type
urban/non-urban speeds
• Tally numbers of patients at each node in
the road network
• Find nearest nodes in network to each
hospital
• Code hospitals as ‘existing’ or ‘potential’
units

Modelling/Analysis
• Carried out using standard GIS
functions
• Convenient ‘front end’ written
using system’s macro language
(save a lot of typing)
• Explore a range of alternatives

Hospital with
dialysis unit

Study region showing hospital locations and urban areas

Patients requiring dialysis (tallied to road nodes)

Classified road network – includes roads outside study area

•

Some alternative
scenarios:

(a) Optimal allocation
of patients
(b) Provide 1 extra
unit
(c) Provide 2 extra
units

(d) Close 1 unit in
city and reallocate
its patients

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

integrate data
process data
model and analyse
generate alternative scenarios
display the results

2: Facility Location
• Two hospitals both provided facilities
for obstetrics, gynaecology, paediatrics
and a special care baby unit.
• Was there a case for rationalisation and
improving the facilities at one hospital
only?
• Would closure disadvantage any
patients?

Data
• Locations of hospitals in study region
• Anonymised postcoded finished
consultant episodes for each of the
four specialities at each hospital
• Classified road network
• Speeds associated with each road
type

Data preparation
• Postcoded records allocated grid
references using Central Postcode
Directory (POSTZON)
• Vector data converted to raster
• Patient/specialty/hospital counts
produced for each raster
• Passage times computed for each
raster cell based on road
classification and speed estimates

Roads, urban areas, patient locations, hospitals in study area

Processing
• Travel time surfaces computed for
each hospital
• Each raster cell contains time
required to reach hospital in minutes
• Assumed walking speed in ‘non road’
areas

Travel time surface for hospital S – darker = shorter time

Travel time surface for hospital T

Time differences
• If we subtract the travel time surfaces areas
nearer one hospital will have positive
values for the difference, and areas nearer
the other will have negative ones
• Boundary between positive and negative
values is boundary of catchment for each
hospital.
• If you’re on the boundary (difference is
zero), it doesn’t matter which hospital you
choose

Roads, patient locations, hospital catchments

Indifference
• If we identify those rasters where the time
difference (ignoring the sign) is, say, 5 –
people in those areas are in the 5 minute
‘indifference’ zone.
• Patients might not be worried about a 5
minute increase in their travel time, they
might be worried about 30 minutes
• Indifference zones can be computed quite
easily

15-minute indifference zone between the two hospitals

Mean access time
• For each cell in the raster we can
compute the product of the number
of patients and the time required to
reach each hospital
• Dividing this by the number of
patients gives a patient weighted
average time for each hospital

Average travel times for the various specialties. There
isn’t much to chose between specialities with the
exception of paediatric episodes.

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

gather data
integrate data
process data
model data
display results

• All carried out using raster
operations

3: Schizophrenia in Cavan
• In 1996 Pringle, Waddington and
Youssef reported on evidence for the
causes of schizophrenia
• Ecological analysis of variation among
EDs
– Cases varied from 0-11 per ED
– Populations varied from 250-2750

• Poisson probabilities suggested that
one area had an unusually high number
of cases – these can be mapped

Male cases
superimposed on plot
of prevalence by ED

Male cases
superimposed on
rectified Landsat
image with ED
boundaries

4: GMS card holders: Maynooth
•Data on GMS – Medical Card holders
drawn under strict data protection
protocols from DOHC
• Anonymised address lists data matched
with GeoDirectory
•Address matching rate of around 77% in
Maynooth

• Interesting range of results around
average for the whole ED

•ED – single value 14.6 % (national average around 30%)
•Small Area – Range from 2% to 70% - noticeable internal variation and
good indicator of elderly or poor populations

•Strategic value in comparing datasets
•Important in policy analysis

Mapping invites comparison

Role of GIS
• Data integration
– Tally of cases in small areas requires
data on locations of cases and small
area boundaries
– ‘spatial join’ places small area codes
on the case records
– Counting is a standard database
operation

• Data display

Finally
• GIS encourages you to think about
space
• In a ‘conventional’ map of Ireland
areas (EDs, Counties…) are shown in
proportion to their physical size
• There are alternative ways of
displaying maps…

In a cartogram the areas are drawn
proportional to their populations –
this is a “density-equalised” map
projection

A map of the boundaries of counties and
electoral divisions – the areas are drawn
proportional to their physical size

By comparison, the more deprived
western area of Dublin, northern area
of Cork and south east of Limerick are
clearly apparent in the cartogram.

Haase’s deprivation index suggests that the
most deprived populations are in peripheral
rural areas in the north west.

Redrawing the data on the cartogram
reveals wide variation in the SMR, and also
uncovers the urban dimension which is
hidden in the conventional display

Patient data matched to Electoral Divisions –
hospital admissions for psychiatric problems – the
patchwork effect does not suggest any pattern

The cartogram reveals the urban nature
of the problem – those areas which are
more deprived are also those with
higher levels of illness/disability

Residents aged 15+ who are unable to work
through illness or disability: the map pattern
suggests that might be a largely rural
phenomenon

The roads, rail, and urban areas data have
been transformed into the same space as
the cartogram. Dublin and Cork seem
poorly provided with roads…

Other spatial information can be transformed
to the space of the density equalised
projection. Here is the familiar map of main
roads and towns.

Using a cartogram we see that the
incidence of lung cancer is randomly
spatially distributed

Lung cancer in males in New York State [Gastner & Newman: 2004]

Plotting locations of lung cancer cases, we
obtain a map which follows the underlying
population distribution – there appear to be
“clusters”

What about Ireland?
• Potentially hampered by challenge of
geocoding patient records
• Lack of small area population data
– EDs have an average population of 1150
– Wide variation in size (50-28000)
– Socially inhomogenous

Rural addresses
• A major challenge is non-uniqueness
of addresses in rural areas.
• For instance in Moy ED, 11 properties
share the address
‘Lackamore, Lahinch, Co. Clare’

What’s needed
• Postcoding should provide a
mechanism for geocoding patient data
with the granularity required for
planning the delivery of health care –
this has been the case in the UK for a
for long time
• Census data should be made available
for areas with small and similar
populations (cf: UK Output Areas)

Finally…
• GIS has an important role to play
in health studies
• Health data analysis is a rapidly
developing field
• However, in Ireland we are
hampered by data deficiencies –
now is the time for change

Maynooth: image in Google Earth

Central Dublin: image in Google Earth

Taoiseach’s Office: image in Google Earth

Downing Street, London: image in Google Earth

Maynooth: OSi orthophotograph added locally uisng KML

Sellafield: image in Google Earth

NCU – On Line Atlas of Cancer Mortality

The Philippine Institute
for Development
Studies makes an online atlas of socioeconomic indicators
available – contains
some health measures

The future
• Patient empowerment…

• GIS used to merge information from
regulatory agencies on 19196 landfill sites
• Sites buffered to 2km and postcodes
within buffer identified
• Postcoded live births (8.2x106), stillbirths
(43471) and congenital anomalies
(124,597) between 1982 and 1997
• Poisson regression modelling undertaken
• Evidence found for excess risk of
congenital anomaly and low birthweight

• Distance from each
census ward to
nearest hospital
computed
• Map shows number
of patients waiting
longer than 6
months for elective
inpatient care per
available and
unoccupied bed
within 60 minutes
travel time


Slide 28

GIS and Health
Martin Charlton1
Ronan Foley2
Dennis Pringle2
1National

Centre for Geocomputation
2Department of Geography
NUI Maynooth

Outline
• Preliminary observations on GIS
and health
• Examples of GIS in Health:
research and practice
• Mapping people not places

Patterns in Health Data
• Three main areas:
– Mapping of disease incidence
• Relative risk estimation

– Identification of disease clustering
• Surveillance

– Ecological analysis
• Relationship between incidence and
covariates
(Lawson et al, 2003)

Health Data
• Typically patient data is available
in two forms
• Individual – perhaps anonymised
to postcode/zip code level
• Aggregate – counts are available
for a set of zones
• The nature of the data affects the
nature of the analysis

Geographic Information Systems
• GIS is often confused with mapping
• A range of activities which includes
inter alia mapping and analysis
• Will typically involve several datasets
• May also involve several different
pieces of software

Mapping: EpiINFO
• This is a rather simple analysis and
mapping program
• It can be downloaded from the internet
• It doesn’t provide much in the way of
spatial data manipulation…
• … nor are its analytical capabilities very
advanced
• It is easy to use

Spatial Data Manipulation
• GIS isn’t merely concerned with
data display or query
• It’s useful in bringing together
different spatial datasets in the
first stages of an analysis
• This may well require a range of
different geometrical and
database operations

Analysis with Spatial Data
• Statistical analysis usually involves
some separate software.
• Spatial data have some properties
which mean that SPSS isn’t always
appropriate.
• There is now a range of specialist
software – much of it available for
download

GIS may be ideal for data manipulation, display and query,
but it’s not always ideal for analysis – in epidemiology a
variety of analytical software is available … WinBUGS,
GeoBUGS (MRC Biostatistics Unit, Cambridge)

Software to implement
particular methods – for
example scan statistics - can be
downloaded quickly and easily
from the Internet. Kulldorf’s
SaTScan system implements a
range of different scan
statistical models

GIS in Health in Practice
• Facility location – dialysis units
• Facility location – obs & gyn
• Data integration – schizophrenia
in Cavan
• Data manipulation and display –
GMS card holders in Maynooth

1: Provision of Dialysis Units
• There are 5 dialysis units for renal
patients in a region with a population
of about 3,000,000
• Major population centre has two units
about 5 miles apart
• Where would you locate an extra one?
• What would happen if one was
closed?

Location-allocation
• Location-allocation problems deal
with optimum location of facilities
• Attempt to minimise total
weighted travel time/distance
from users to facilities
• What data is needed?

Data requirements
• Digitised road network with classification
of road types
• Mean travel speeds on each type for
urban/non-urban roads
• Extents of urban areas
• Anonymised postcoded patient records
• Postcode->grid reference lookup
• Locations of hospitals in region (both with
and without dialysis units)

GIS processing
• Data integration
• Determine which segments of road network
are urban
• Determine travel times using road type
urban/non-urban speeds
• Tally numbers of patients at each node in
the road network
• Find nearest nodes in network to each
hospital
• Code hospitals as ‘existing’ or ‘potential’
units

Modelling/Analysis
• Carried out using standard GIS
functions
• Convenient ‘front end’ written
using system’s macro language
(save a lot of typing)
• Explore a range of alternatives

Hospital with
dialysis unit

Study region showing hospital locations and urban areas

Patients requiring dialysis (tallied to road nodes)

Classified road network – includes roads outside study area

•

Some alternative
scenarios:

(a) Optimal allocation
of patients
(b) Provide 1 extra
unit
(c) Provide 2 extra
units

(d) Close 1 unit in
city and reallocate
its patients

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

integrate data
process data
model and analyse
generate alternative scenarios
display the results

2: Facility Location
• Two hospitals both provided facilities
for obstetrics, gynaecology, paediatrics
and a special care baby unit.
• Was there a case for rationalisation and
improving the facilities at one hospital
only?
• Would closure disadvantage any
patients?

Data
• Locations of hospitals in study region
• Anonymised postcoded finished
consultant episodes for each of the
four specialities at each hospital
• Classified road network
• Speeds associated with each road
type

Data preparation
• Postcoded records allocated grid
references using Central Postcode
Directory (POSTZON)
• Vector data converted to raster
• Patient/specialty/hospital counts
produced for each raster
• Passage times computed for each
raster cell based on road
classification and speed estimates

Roads, urban areas, patient locations, hospitals in study area

Processing
• Travel time surfaces computed for
each hospital
• Each raster cell contains time
required to reach hospital in minutes
• Assumed walking speed in ‘non road’
areas

Travel time surface for hospital S – darker = shorter time

Travel time surface for hospital T

Time differences
• If we subtract the travel time surfaces areas
nearer one hospital will have positive
values for the difference, and areas nearer
the other will have negative ones
• Boundary between positive and negative
values is boundary of catchment for each
hospital.
• If you’re on the boundary (difference is
zero), it doesn’t matter which hospital you
choose

Roads, patient locations, hospital catchments

Indifference
• If we identify those rasters where the time
difference (ignoring the sign) is, say, 5 –
people in those areas are in the 5 minute
‘indifference’ zone.
• Patients might not be worried about a 5
minute increase in their travel time, they
might be worried about 30 minutes
• Indifference zones can be computed quite
easily

15-minute indifference zone between the two hospitals

Mean access time
• For each cell in the raster we can
compute the product of the number
of patients and the time required to
reach each hospital
• Dividing this by the number of
patients gives a patient weighted
average time for each hospital

Average travel times for the various specialties. There
isn’t much to chose between specialities with the
exception of paediatric episodes.

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

gather data
integrate data
process data
model data
display results

• All carried out using raster
operations

3: Schizophrenia in Cavan
• In 1996 Pringle, Waddington and
Youssef reported on evidence for the
causes of schizophrenia
• Ecological analysis of variation among
EDs
– Cases varied from 0-11 per ED
– Populations varied from 250-2750

• Poisson probabilities suggested that
one area had an unusually high number
of cases – these can be mapped

Male cases
superimposed on plot
of prevalence by ED

Male cases
superimposed on
rectified Landsat
image with ED
boundaries

4: GMS card holders: Maynooth
•Data on GMS – Medical Card holders
drawn under strict data protection
protocols from DOHC
• Anonymised address lists data matched
with GeoDirectory
•Address matching rate of around 77% in
Maynooth

• Interesting range of results around
average for the whole ED

•ED – single value 14.6 % (national average around 30%)
•Small Area – Range from 2% to 70% - noticeable internal variation and
good indicator of elderly or poor populations

•Strategic value in comparing datasets
•Important in policy analysis

Mapping invites comparison

Role of GIS
• Data integration
– Tally of cases in small areas requires
data on locations of cases and small
area boundaries
– ‘spatial join’ places small area codes
on the case records
– Counting is a standard database
operation

• Data display

Finally
• GIS encourages you to think about
space
• In a ‘conventional’ map of Ireland
areas (EDs, Counties…) are shown in
proportion to their physical size
• There are alternative ways of
displaying maps…

In a cartogram the areas are drawn
proportional to their populations –
this is a “density-equalised” map
projection

A map of the boundaries of counties and
electoral divisions – the areas are drawn
proportional to their physical size

By comparison, the more deprived
western area of Dublin, northern area
of Cork and south east of Limerick are
clearly apparent in the cartogram.

Haase’s deprivation index suggests that the
most deprived populations are in peripheral
rural areas in the north west.

Redrawing the data on the cartogram
reveals wide variation in the SMR, and also
uncovers the urban dimension which is
hidden in the conventional display

Patient data matched to Electoral Divisions –
hospital admissions for psychiatric problems – the
patchwork effect does not suggest any pattern

The cartogram reveals the urban nature
of the problem – those areas which are
more deprived are also those with
higher levels of illness/disability

Residents aged 15+ who are unable to work
through illness or disability: the map pattern
suggests that might be a largely rural
phenomenon

The roads, rail, and urban areas data have
been transformed into the same space as
the cartogram. Dublin and Cork seem
poorly provided with roads…

Other spatial information can be transformed
to the space of the density equalised
projection. Here is the familiar map of main
roads and towns.

Using a cartogram we see that the
incidence of lung cancer is randomly
spatially distributed

Lung cancer in males in New York State [Gastner & Newman: 2004]

Plotting locations of lung cancer cases, we
obtain a map which follows the underlying
population distribution – there appear to be
“clusters”

What about Ireland?
• Potentially hampered by challenge of
geocoding patient records
• Lack of small area population data
– EDs have an average population of 1150
– Wide variation in size (50-28000)
– Socially inhomogenous

Rural addresses
• A major challenge is non-uniqueness
of addresses in rural areas.
• For instance in Moy ED, 11 properties
share the address
‘Lackamore, Lahinch, Co. Clare’

What’s needed
• Postcoding should provide a
mechanism for geocoding patient data
with the granularity required for
planning the delivery of health care –
this has been the case in the UK for a
for long time
• Census data should be made available
for areas with small and similar
populations (cf: UK Output Areas)

Finally…
• GIS has an important role to play
in health studies
• Health data analysis is a rapidly
developing field
• However, in Ireland we are
hampered by data deficiencies –
now is the time for change

Maynooth: image in Google Earth

Central Dublin: image in Google Earth

Taoiseach’s Office: image in Google Earth

Downing Street, London: image in Google Earth

Maynooth: OSi orthophotograph added locally uisng KML

Sellafield: image in Google Earth

NCU – On Line Atlas of Cancer Mortality

The Philippine Institute
for Development
Studies makes an online atlas of socioeconomic indicators
available – contains
some health measures

The future
• Patient empowerment…

• GIS used to merge information from
regulatory agencies on 19196 landfill sites
• Sites buffered to 2km and postcodes
within buffer identified
• Postcoded live births (8.2x106), stillbirths
(43471) and congenital anomalies
(124,597) between 1982 and 1997
• Poisson regression modelling undertaken
• Evidence found for excess risk of
congenital anomaly and low birthweight

• Distance from each
census ward to
nearest hospital
computed
• Map shows number
of patients waiting
longer than 6
months for elective
inpatient care per
available and
unoccupied bed
within 60 minutes
travel time


Slide 29

GIS and Health
Martin Charlton1
Ronan Foley2
Dennis Pringle2
1National

Centre for Geocomputation
2Department of Geography
NUI Maynooth

Outline
• Preliminary observations on GIS
and health
• Examples of GIS in Health:
research and practice
• Mapping people not places

Patterns in Health Data
• Three main areas:
– Mapping of disease incidence
• Relative risk estimation

– Identification of disease clustering
• Surveillance

– Ecological analysis
• Relationship between incidence and
covariates
(Lawson et al, 2003)

Health Data
• Typically patient data is available
in two forms
• Individual – perhaps anonymised
to postcode/zip code level
• Aggregate – counts are available
for a set of zones
• The nature of the data affects the
nature of the analysis

Geographic Information Systems
• GIS is often confused with mapping
• A range of activities which includes
inter alia mapping and analysis
• Will typically involve several datasets
• May also involve several different
pieces of software

Mapping: EpiINFO
• This is a rather simple analysis and
mapping program
• It can be downloaded from the internet
• It doesn’t provide much in the way of
spatial data manipulation…
• … nor are its analytical capabilities very
advanced
• It is easy to use

Spatial Data Manipulation
• GIS isn’t merely concerned with
data display or query
• It’s useful in bringing together
different spatial datasets in the
first stages of an analysis
• This may well require a range of
different geometrical and
database operations

Analysis with Spatial Data
• Statistical analysis usually involves
some separate software.
• Spatial data have some properties
which mean that SPSS isn’t always
appropriate.
• There is now a range of specialist
software – much of it available for
download

GIS may be ideal for data manipulation, display and query,
but it’s not always ideal for analysis – in epidemiology a
variety of analytical software is available … WinBUGS,
GeoBUGS (MRC Biostatistics Unit, Cambridge)

Software to implement
particular methods – for
example scan statistics - can be
downloaded quickly and easily
from the Internet. Kulldorf’s
SaTScan system implements a
range of different scan
statistical models

GIS in Health in Practice
• Facility location – dialysis units
• Facility location – obs & gyn
• Data integration – schizophrenia
in Cavan
• Data manipulation and display –
GMS card holders in Maynooth

1: Provision of Dialysis Units
• There are 5 dialysis units for renal
patients in a region with a population
of about 3,000,000
• Major population centre has two units
about 5 miles apart
• Where would you locate an extra one?
• What would happen if one was
closed?

Location-allocation
• Location-allocation problems deal
with optimum location of facilities
• Attempt to minimise total
weighted travel time/distance
from users to facilities
• What data is needed?

Data requirements
• Digitised road network with classification
of road types
• Mean travel speeds on each type for
urban/non-urban roads
• Extents of urban areas
• Anonymised postcoded patient records
• Postcode->grid reference lookup
• Locations of hospitals in region (both with
and without dialysis units)

GIS processing
• Data integration
• Determine which segments of road network
are urban
• Determine travel times using road type
urban/non-urban speeds
• Tally numbers of patients at each node in
the road network
• Find nearest nodes in network to each
hospital
• Code hospitals as ‘existing’ or ‘potential’
units

Modelling/Analysis
• Carried out using standard GIS
functions
• Convenient ‘front end’ written
using system’s macro language
(save a lot of typing)
• Explore a range of alternatives

Hospital with
dialysis unit

Study region showing hospital locations and urban areas

Patients requiring dialysis (tallied to road nodes)

Classified road network – includes roads outside study area

•

Some alternative
scenarios:

(a) Optimal allocation
of patients
(b) Provide 1 extra
unit
(c) Provide 2 extra
units

(d) Close 1 unit in
city and reallocate
its patients

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

integrate data
process data
model and analyse
generate alternative scenarios
display the results

2: Facility Location
• Two hospitals both provided facilities
for obstetrics, gynaecology, paediatrics
and a special care baby unit.
• Was there a case for rationalisation and
improving the facilities at one hospital
only?
• Would closure disadvantage any
patients?

Data
• Locations of hospitals in study region
• Anonymised postcoded finished
consultant episodes for each of the
four specialities at each hospital
• Classified road network
• Speeds associated with each road
type

Data preparation
• Postcoded records allocated grid
references using Central Postcode
Directory (POSTZON)
• Vector data converted to raster
• Patient/specialty/hospital counts
produced for each raster
• Passage times computed for each
raster cell based on road
classification and speed estimates

Roads, urban areas, patient locations, hospitals in study area

Processing
• Travel time surfaces computed for
each hospital
• Each raster cell contains time
required to reach hospital in minutes
• Assumed walking speed in ‘non road’
areas

Travel time surface for hospital S – darker = shorter time

Travel time surface for hospital T

Time differences
• If we subtract the travel time surfaces areas
nearer one hospital will have positive
values for the difference, and areas nearer
the other will have negative ones
• Boundary between positive and negative
values is boundary of catchment for each
hospital.
• If you’re on the boundary (difference is
zero), it doesn’t matter which hospital you
choose

Roads, patient locations, hospital catchments

Indifference
• If we identify those rasters where the time
difference (ignoring the sign) is, say, 5 –
people in those areas are in the 5 minute
‘indifference’ zone.
• Patients might not be worried about a 5
minute increase in their travel time, they
might be worried about 30 minutes
• Indifference zones can be computed quite
easily

15-minute indifference zone between the two hospitals

Mean access time
• For each cell in the raster we can
compute the product of the number
of patients and the time required to
reach each hospital
• Dividing this by the number of
patients gives a patient weighted
average time for each hospital

Average travel times for the various specialties. There
isn’t much to chose between specialities with the
exception of paediatric episodes.

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

gather data
integrate data
process data
model data
display results

• All carried out using raster
operations

3: Schizophrenia in Cavan
• In 1996 Pringle, Waddington and
Youssef reported on evidence for the
causes of schizophrenia
• Ecological analysis of variation among
EDs
– Cases varied from 0-11 per ED
– Populations varied from 250-2750

• Poisson probabilities suggested that
one area had an unusually high number
of cases – these can be mapped

Male cases
superimposed on plot
of prevalence by ED

Male cases
superimposed on
rectified Landsat
image with ED
boundaries

4: GMS card holders: Maynooth
•Data on GMS – Medical Card holders
drawn under strict data protection
protocols from DOHC
• Anonymised address lists data matched
with GeoDirectory
•Address matching rate of around 77% in
Maynooth

• Interesting range of results around
average for the whole ED

•ED – single value 14.6 % (national average around 30%)
•Small Area – Range from 2% to 70% - noticeable internal variation and
good indicator of elderly or poor populations

•Strategic value in comparing datasets
•Important in policy analysis

Mapping invites comparison

Role of GIS
• Data integration
– Tally of cases in small areas requires
data on locations of cases and small
area boundaries
– ‘spatial join’ places small area codes
on the case records
– Counting is a standard database
operation

• Data display

Finally
• GIS encourages you to think about
space
• In a ‘conventional’ map of Ireland
areas (EDs, Counties…) are shown in
proportion to their physical size
• There are alternative ways of
displaying maps…

In a cartogram the areas are drawn
proportional to their populations –
this is a “density-equalised” map
projection

A map of the boundaries of counties and
electoral divisions – the areas are drawn
proportional to their physical size

By comparison, the more deprived
western area of Dublin, northern area
of Cork and south east of Limerick are
clearly apparent in the cartogram.

Haase’s deprivation index suggests that the
most deprived populations are in peripheral
rural areas in the north west.

Redrawing the data on the cartogram
reveals wide variation in the SMR, and also
uncovers the urban dimension which is
hidden in the conventional display

Patient data matched to Electoral Divisions –
hospital admissions for psychiatric problems – the
patchwork effect does not suggest any pattern

The cartogram reveals the urban nature
of the problem – those areas which are
more deprived are also those with
higher levels of illness/disability

Residents aged 15+ who are unable to work
through illness or disability: the map pattern
suggests that might be a largely rural
phenomenon

The roads, rail, and urban areas data have
been transformed into the same space as
the cartogram. Dublin and Cork seem
poorly provided with roads…

Other spatial information can be transformed
to the space of the density equalised
projection. Here is the familiar map of main
roads and towns.

Using a cartogram we see that the
incidence of lung cancer is randomly
spatially distributed

Lung cancer in males in New York State [Gastner & Newman: 2004]

Plotting locations of lung cancer cases, we
obtain a map which follows the underlying
population distribution – there appear to be
“clusters”

What about Ireland?
• Potentially hampered by challenge of
geocoding patient records
• Lack of small area population data
– EDs have an average population of 1150
– Wide variation in size (50-28000)
– Socially inhomogenous

Rural addresses
• A major challenge is non-uniqueness
of addresses in rural areas.
• For instance in Moy ED, 11 properties
share the address
‘Lackamore, Lahinch, Co. Clare’

What’s needed
• Postcoding should provide a
mechanism for geocoding patient data
with the granularity required for
planning the delivery of health care –
this has been the case in the UK for a
for long time
• Census data should be made available
for areas with small and similar
populations (cf: UK Output Areas)

Finally…
• GIS has an important role to play
in health studies
• Health data analysis is a rapidly
developing field
• However, in Ireland we are
hampered by data deficiencies –
now is the time for change

Maynooth: image in Google Earth

Central Dublin: image in Google Earth

Taoiseach’s Office: image in Google Earth

Downing Street, London: image in Google Earth

Maynooth: OSi orthophotograph added locally uisng KML

Sellafield: image in Google Earth

NCU – On Line Atlas of Cancer Mortality

The Philippine Institute
for Development
Studies makes an online atlas of socioeconomic indicators
available – contains
some health measures

The future
• Patient empowerment…

• GIS used to merge information from
regulatory agencies on 19196 landfill sites
• Sites buffered to 2km and postcodes
within buffer identified
• Postcoded live births (8.2x106), stillbirths
(43471) and congenital anomalies
(124,597) between 1982 and 1997
• Poisson regression modelling undertaken
• Evidence found for excess risk of
congenital anomaly and low birthweight

• Distance from each
census ward to
nearest hospital
computed
• Map shows number
of patients waiting
longer than 6
months for elective
inpatient care per
available and
unoccupied bed
within 60 minutes
travel time


Slide 30

GIS and Health
Martin Charlton1
Ronan Foley2
Dennis Pringle2
1National

Centre for Geocomputation
2Department of Geography
NUI Maynooth

Outline
• Preliminary observations on GIS
and health
• Examples of GIS in Health:
research and practice
• Mapping people not places

Patterns in Health Data
• Three main areas:
– Mapping of disease incidence
• Relative risk estimation

– Identification of disease clustering
• Surveillance

– Ecological analysis
• Relationship between incidence and
covariates
(Lawson et al, 2003)

Health Data
• Typically patient data is available
in two forms
• Individual – perhaps anonymised
to postcode/zip code level
• Aggregate – counts are available
for a set of zones
• The nature of the data affects the
nature of the analysis

Geographic Information Systems
• GIS is often confused with mapping
• A range of activities which includes
inter alia mapping and analysis
• Will typically involve several datasets
• May also involve several different
pieces of software

Mapping: EpiINFO
• This is a rather simple analysis and
mapping program
• It can be downloaded from the internet
• It doesn’t provide much in the way of
spatial data manipulation…
• … nor are its analytical capabilities very
advanced
• It is easy to use

Spatial Data Manipulation
• GIS isn’t merely concerned with
data display or query
• It’s useful in bringing together
different spatial datasets in the
first stages of an analysis
• This may well require a range of
different geometrical and
database operations

Analysis with Spatial Data
• Statistical analysis usually involves
some separate software.
• Spatial data have some properties
which mean that SPSS isn’t always
appropriate.
• There is now a range of specialist
software – much of it available for
download

GIS may be ideal for data manipulation, display and query,
but it’s not always ideal for analysis – in epidemiology a
variety of analytical software is available … WinBUGS,
GeoBUGS (MRC Biostatistics Unit, Cambridge)

Software to implement
particular methods – for
example scan statistics - can be
downloaded quickly and easily
from the Internet. Kulldorf’s
SaTScan system implements a
range of different scan
statistical models

GIS in Health in Practice
• Facility location – dialysis units
• Facility location – obs & gyn
• Data integration – schizophrenia
in Cavan
• Data manipulation and display –
GMS card holders in Maynooth

1: Provision of Dialysis Units
• There are 5 dialysis units for renal
patients in a region with a population
of about 3,000,000
• Major population centre has two units
about 5 miles apart
• Where would you locate an extra one?
• What would happen if one was
closed?

Location-allocation
• Location-allocation problems deal
with optimum location of facilities
• Attempt to minimise total
weighted travel time/distance
from users to facilities
• What data is needed?

Data requirements
• Digitised road network with classification
of road types
• Mean travel speeds on each type for
urban/non-urban roads
• Extents of urban areas
• Anonymised postcoded patient records
• Postcode->grid reference lookup
• Locations of hospitals in region (both with
and without dialysis units)

GIS processing
• Data integration
• Determine which segments of road network
are urban
• Determine travel times using road type
urban/non-urban speeds
• Tally numbers of patients at each node in
the road network
• Find nearest nodes in network to each
hospital
• Code hospitals as ‘existing’ or ‘potential’
units

Modelling/Analysis
• Carried out using standard GIS
functions
• Convenient ‘front end’ written
using system’s macro language
(save a lot of typing)
• Explore a range of alternatives

Hospital with
dialysis unit

Study region showing hospital locations and urban areas

Patients requiring dialysis (tallied to road nodes)

Classified road network – includes roads outside study area

•

Some alternative
scenarios:

(a) Optimal allocation
of patients
(b) Provide 1 extra
unit
(c) Provide 2 extra
units

(d) Close 1 unit in
city and reallocate
its patients

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

integrate data
process data
model and analyse
generate alternative scenarios
display the results

2: Facility Location
• Two hospitals both provided facilities
for obstetrics, gynaecology, paediatrics
and a special care baby unit.
• Was there a case for rationalisation and
improving the facilities at one hospital
only?
• Would closure disadvantage any
patients?

Data
• Locations of hospitals in study region
• Anonymised postcoded finished
consultant episodes for each of the
four specialities at each hospital
• Classified road network
• Speeds associated with each road
type

Data preparation
• Postcoded records allocated grid
references using Central Postcode
Directory (POSTZON)
• Vector data converted to raster
• Patient/specialty/hospital counts
produced for each raster
• Passage times computed for each
raster cell based on road
classification and speed estimates

Roads, urban areas, patient locations, hospitals in study area

Processing
• Travel time surfaces computed for
each hospital
• Each raster cell contains time
required to reach hospital in minutes
• Assumed walking speed in ‘non road’
areas

Travel time surface for hospital S – darker = shorter time

Travel time surface for hospital T

Time differences
• If we subtract the travel time surfaces areas
nearer one hospital will have positive
values for the difference, and areas nearer
the other will have negative ones
• Boundary between positive and negative
values is boundary of catchment for each
hospital.
• If you’re on the boundary (difference is
zero), it doesn’t matter which hospital you
choose

Roads, patient locations, hospital catchments

Indifference
• If we identify those rasters where the time
difference (ignoring the sign) is, say, 5 –
people in those areas are in the 5 minute
‘indifference’ zone.
• Patients might not be worried about a 5
minute increase in their travel time, they
might be worried about 30 minutes
• Indifference zones can be computed quite
easily

15-minute indifference zone between the two hospitals

Mean access time
• For each cell in the raster we can
compute the product of the number
of patients and the time required to
reach each hospital
• Dividing this by the number of
patients gives a patient weighted
average time for each hospital

Average travel times for the various specialties. There
isn’t much to chose between specialities with the
exception of paediatric episodes.

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

gather data
integrate data
process data
model data
display results

• All carried out using raster
operations

3: Schizophrenia in Cavan
• In 1996 Pringle, Waddington and
Youssef reported on evidence for the
causes of schizophrenia
• Ecological analysis of variation among
EDs
– Cases varied from 0-11 per ED
– Populations varied from 250-2750

• Poisson probabilities suggested that
one area had an unusually high number
of cases – these can be mapped

Male cases
superimposed on plot
of prevalence by ED

Male cases
superimposed on
rectified Landsat
image with ED
boundaries

4: GMS card holders: Maynooth
•Data on GMS – Medical Card holders
drawn under strict data protection
protocols from DOHC
• Anonymised address lists data matched
with GeoDirectory
•Address matching rate of around 77% in
Maynooth

• Interesting range of results around
average for the whole ED

•ED – single value 14.6 % (national average around 30%)
•Small Area – Range from 2% to 70% - noticeable internal variation and
good indicator of elderly or poor populations

•Strategic value in comparing datasets
•Important in policy analysis

Mapping invites comparison

Role of GIS
• Data integration
– Tally of cases in small areas requires
data on locations of cases and small
area boundaries
– ‘spatial join’ places small area codes
on the case records
– Counting is a standard database
operation

• Data display

Finally
• GIS encourages you to think about
space
• In a ‘conventional’ map of Ireland
areas (EDs, Counties…) are shown in
proportion to their physical size
• There are alternative ways of
displaying maps…

In a cartogram the areas are drawn
proportional to their populations –
this is a “density-equalised” map
projection

A map of the boundaries of counties and
electoral divisions – the areas are drawn
proportional to their physical size

By comparison, the more deprived
western area of Dublin, northern area
of Cork and south east of Limerick are
clearly apparent in the cartogram.

Haase’s deprivation index suggests that the
most deprived populations are in peripheral
rural areas in the north west.

Redrawing the data on the cartogram
reveals wide variation in the SMR, and also
uncovers the urban dimension which is
hidden in the conventional display

Patient data matched to Electoral Divisions –
hospital admissions for psychiatric problems – the
patchwork effect does not suggest any pattern

The cartogram reveals the urban nature
of the problem – those areas which are
more deprived are also those with
higher levels of illness/disability

Residents aged 15+ who are unable to work
through illness or disability: the map pattern
suggests that might be a largely rural
phenomenon

The roads, rail, and urban areas data have
been transformed into the same space as
the cartogram. Dublin and Cork seem
poorly provided with roads…

Other spatial information can be transformed
to the space of the density equalised
projection. Here is the familiar map of main
roads and towns.

Using a cartogram we see that the
incidence of lung cancer is randomly
spatially distributed

Lung cancer in males in New York State [Gastner & Newman: 2004]

Plotting locations of lung cancer cases, we
obtain a map which follows the underlying
population distribution – there appear to be
“clusters”

What about Ireland?
• Potentially hampered by challenge of
geocoding patient records
• Lack of small area population data
– EDs have an average population of 1150
– Wide variation in size (50-28000)
– Socially inhomogenous

Rural addresses
• A major challenge is non-uniqueness
of addresses in rural areas.
• For instance in Moy ED, 11 properties
share the address
‘Lackamore, Lahinch, Co. Clare’

What’s needed
• Postcoding should provide a
mechanism for geocoding patient data
with the granularity required for
planning the delivery of health care –
this has been the case in the UK for a
for long time
• Census data should be made available
for areas with small and similar
populations (cf: UK Output Areas)

Finally…
• GIS has an important role to play
in health studies
• Health data analysis is a rapidly
developing field
• However, in Ireland we are
hampered by data deficiencies –
now is the time for change

Maynooth: image in Google Earth

Central Dublin: image in Google Earth

Taoiseach’s Office: image in Google Earth

Downing Street, London: image in Google Earth

Maynooth: OSi orthophotograph added locally uisng KML

Sellafield: image in Google Earth

NCU – On Line Atlas of Cancer Mortality

The Philippine Institute
for Development
Studies makes an online atlas of socioeconomic indicators
available – contains
some health measures

The future
• Patient empowerment…

• GIS used to merge information from
regulatory agencies on 19196 landfill sites
• Sites buffered to 2km and postcodes
within buffer identified
• Postcoded live births (8.2x106), stillbirths
(43471) and congenital anomalies
(124,597) between 1982 and 1997
• Poisson regression modelling undertaken
• Evidence found for excess risk of
congenital anomaly and low birthweight

• Distance from each
census ward to
nearest hospital
computed
• Map shows number
of patients waiting
longer than 6
months for elective
inpatient care per
available and
unoccupied bed
within 60 minutes
travel time


Slide 31

GIS and Health
Martin Charlton1
Ronan Foley2
Dennis Pringle2
1National

Centre for Geocomputation
2Department of Geography
NUI Maynooth

Outline
• Preliminary observations on GIS
and health
• Examples of GIS in Health:
research and practice
• Mapping people not places

Patterns in Health Data
• Three main areas:
– Mapping of disease incidence
• Relative risk estimation

– Identification of disease clustering
• Surveillance

– Ecological analysis
• Relationship between incidence and
covariates
(Lawson et al, 2003)

Health Data
• Typically patient data is available
in two forms
• Individual – perhaps anonymised
to postcode/zip code level
• Aggregate – counts are available
for a set of zones
• The nature of the data affects the
nature of the analysis

Geographic Information Systems
• GIS is often confused with mapping
• A range of activities which includes
inter alia mapping and analysis
• Will typically involve several datasets
• May also involve several different
pieces of software

Mapping: EpiINFO
• This is a rather simple analysis and
mapping program
• It can be downloaded from the internet
• It doesn’t provide much in the way of
spatial data manipulation…
• … nor are its analytical capabilities very
advanced
• It is easy to use

Spatial Data Manipulation
• GIS isn’t merely concerned with
data display or query
• It’s useful in bringing together
different spatial datasets in the
first stages of an analysis
• This may well require a range of
different geometrical and
database operations

Analysis with Spatial Data
• Statistical analysis usually involves
some separate software.
• Spatial data have some properties
which mean that SPSS isn’t always
appropriate.
• There is now a range of specialist
software – much of it available for
download

GIS may be ideal for data manipulation, display and query,
but it’s not always ideal for analysis – in epidemiology a
variety of analytical software is available … WinBUGS,
GeoBUGS (MRC Biostatistics Unit, Cambridge)

Software to implement
particular methods – for
example scan statistics - can be
downloaded quickly and easily
from the Internet. Kulldorf’s
SaTScan system implements a
range of different scan
statistical models

GIS in Health in Practice
• Facility location – dialysis units
• Facility location – obs & gyn
• Data integration – schizophrenia
in Cavan
• Data manipulation and display –
GMS card holders in Maynooth

1: Provision of Dialysis Units
• There are 5 dialysis units for renal
patients in a region with a population
of about 3,000,000
• Major population centre has two units
about 5 miles apart
• Where would you locate an extra one?
• What would happen if one was
closed?

Location-allocation
• Location-allocation problems deal
with optimum location of facilities
• Attempt to minimise total
weighted travel time/distance
from users to facilities
• What data is needed?

Data requirements
• Digitised road network with classification
of road types
• Mean travel speeds on each type for
urban/non-urban roads
• Extents of urban areas
• Anonymised postcoded patient records
• Postcode->grid reference lookup
• Locations of hospitals in region (both with
and without dialysis units)

GIS processing
• Data integration
• Determine which segments of road network
are urban
• Determine travel times using road type
urban/non-urban speeds
• Tally numbers of patients at each node in
the road network
• Find nearest nodes in network to each
hospital
• Code hospitals as ‘existing’ or ‘potential’
units

Modelling/Analysis
• Carried out using standard GIS
functions
• Convenient ‘front end’ written
using system’s macro language
(save a lot of typing)
• Explore a range of alternatives

Hospital with
dialysis unit

Study region showing hospital locations and urban areas

Patients requiring dialysis (tallied to road nodes)

Classified road network – includes roads outside study area

•

Some alternative
scenarios:

(a) Optimal allocation
of patients
(b) Provide 1 extra
unit
(c) Provide 2 extra
units

(d) Close 1 unit in
city and reallocate
its patients

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

integrate data
process data
model and analyse
generate alternative scenarios
display the results

2: Facility Location
• Two hospitals both provided facilities
for obstetrics, gynaecology, paediatrics
and a special care baby unit.
• Was there a case for rationalisation and
improving the facilities at one hospital
only?
• Would closure disadvantage any
patients?

Data
• Locations of hospitals in study region
• Anonymised postcoded finished
consultant episodes for each of the
four specialities at each hospital
• Classified road network
• Speeds associated with each road
type

Data preparation
• Postcoded records allocated grid
references using Central Postcode
Directory (POSTZON)
• Vector data converted to raster
• Patient/specialty/hospital counts
produced for each raster
• Passage times computed for each
raster cell based on road
classification and speed estimates

Roads, urban areas, patient locations, hospitals in study area

Processing
• Travel time surfaces computed for
each hospital
• Each raster cell contains time
required to reach hospital in minutes
• Assumed walking speed in ‘non road’
areas

Travel time surface for hospital S – darker = shorter time

Travel time surface for hospital T

Time differences
• If we subtract the travel time surfaces areas
nearer one hospital will have positive
values for the difference, and areas nearer
the other will have negative ones
• Boundary between positive and negative
values is boundary of catchment for each
hospital.
• If you’re on the boundary (difference is
zero), it doesn’t matter which hospital you
choose

Roads, patient locations, hospital catchments

Indifference
• If we identify those rasters where the time
difference (ignoring the sign) is, say, 5 –
people in those areas are in the 5 minute
‘indifference’ zone.
• Patients might not be worried about a 5
minute increase in their travel time, they
might be worried about 30 minutes
• Indifference zones can be computed quite
easily

15-minute indifference zone between the two hospitals

Mean access time
• For each cell in the raster we can
compute the product of the number
of patients and the time required to
reach each hospital
• Dividing this by the number of
patients gives a patient weighted
average time for each hospital

Average travel times for the various specialties. There
isn’t much to chose between specialities with the
exception of paediatric episodes.

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

gather data
integrate data
process data
model data
display results

• All carried out using raster
operations

3: Schizophrenia in Cavan
• In 1996 Pringle, Waddington and
Youssef reported on evidence for the
causes of schizophrenia
• Ecological analysis of variation among
EDs
– Cases varied from 0-11 per ED
– Populations varied from 250-2750

• Poisson probabilities suggested that
one area had an unusually high number
of cases – these can be mapped

Male cases
superimposed on plot
of prevalence by ED

Male cases
superimposed on
rectified Landsat
image with ED
boundaries

4: GMS card holders: Maynooth
•Data on GMS – Medical Card holders
drawn under strict data protection
protocols from DOHC
• Anonymised address lists data matched
with GeoDirectory
•Address matching rate of around 77% in
Maynooth

• Interesting range of results around
average for the whole ED

•ED – single value 14.6 % (national average around 30%)
•Small Area – Range from 2% to 70% - noticeable internal variation and
good indicator of elderly or poor populations

•Strategic value in comparing datasets
•Important in policy analysis

Mapping invites comparison

Role of GIS
• Data integration
– Tally of cases in small areas requires
data on locations of cases and small
area boundaries
– ‘spatial join’ places small area codes
on the case records
– Counting is a standard database
operation

• Data display

Finally
• GIS encourages you to think about
space
• In a ‘conventional’ map of Ireland
areas (EDs, Counties…) are shown in
proportion to their physical size
• There are alternative ways of
displaying maps…

In a cartogram the areas are drawn
proportional to their populations –
this is a “density-equalised” map
projection

A map of the boundaries of counties and
electoral divisions – the areas are drawn
proportional to their physical size

By comparison, the more deprived
western area of Dublin, northern area
of Cork and south east of Limerick are
clearly apparent in the cartogram.

Haase’s deprivation index suggests that the
most deprived populations are in peripheral
rural areas in the north west.

Redrawing the data on the cartogram
reveals wide variation in the SMR, and also
uncovers the urban dimension which is
hidden in the conventional display

Patient data matched to Electoral Divisions –
hospital admissions for psychiatric problems – the
patchwork effect does not suggest any pattern

The cartogram reveals the urban nature
of the problem – those areas which are
more deprived are also those with
higher levels of illness/disability

Residents aged 15+ who are unable to work
through illness or disability: the map pattern
suggests that might be a largely rural
phenomenon

The roads, rail, and urban areas data have
been transformed into the same space as
the cartogram. Dublin and Cork seem
poorly provided with roads…

Other spatial information can be transformed
to the space of the density equalised
projection. Here is the familiar map of main
roads and towns.

Using a cartogram we see that the
incidence of lung cancer is randomly
spatially distributed

Lung cancer in males in New York State [Gastner & Newman: 2004]

Plotting locations of lung cancer cases, we
obtain a map which follows the underlying
population distribution – there appear to be
“clusters”

What about Ireland?
• Potentially hampered by challenge of
geocoding patient records
• Lack of small area population data
– EDs have an average population of 1150
– Wide variation in size (50-28000)
– Socially inhomogenous

Rural addresses
• A major challenge is non-uniqueness
of addresses in rural areas.
• For instance in Moy ED, 11 properties
share the address
‘Lackamore, Lahinch, Co. Clare’

What’s needed
• Postcoding should provide a
mechanism for geocoding patient data
with the granularity required for
planning the delivery of health care –
this has been the case in the UK for a
for long time
• Census data should be made available
for areas with small and similar
populations (cf: UK Output Areas)

Finally…
• GIS has an important role to play
in health studies
• Health data analysis is a rapidly
developing field
• However, in Ireland we are
hampered by data deficiencies –
now is the time for change

Maynooth: image in Google Earth

Central Dublin: image in Google Earth

Taoiseach’s Office: image in Google Earth

Downing Street, London: image in Google Earth

Maynooth: OSi orthophotograph added locally uisng KML

Sellafield: image in Google Earth

NCU – On Line Atlas of Cancer Mortality

The Philippine Institute
for Development
Studies makes an online atlas of socioeconomic indicators
available – contains
some health measures

The future
• Patient empowerment…

• GIS used to merge information from
regulatory agencies on 19196 landfill sites
• Sites buffered to 2km and postcodes
within buffer identified
• Postcoded live births (8.2x106), stillbirths
(43471) and congenital anomalies
(124,597) between 1982 and 1997
• Poisson regression modelling undertaken
• Evidence found for excess risk of
congenital anomaly and low birthweight

• Distance from each
census ward to
nearest hospital
computed
• Map shows number
of patients waiting
longer than 6
months for elective
inpatient care per
available and
unoccupied bed
within 60 minutes
travel time


Slide 32

GIS and Health
Martin Charlton1
Ronan Foley2
Dennis Pringle2
1National

Centre for Geocomputation
2Department of Geography
NUI Maynooth

Outline
• Preliminary observations on GIS
and health
• Examples of GIS in Health:
research and practice
• Mapping people not places

Patterns in Health Data
• Three main areas:
– Mapping of disease incidence
• Relative risk estimation

– Identification of disease clustering
• Surveillance

– Ecological analysis
• Relationship between incidence and
covariates
(Lawson et al, 2003)

Health Data
• Typically patient data is available
in two forms
• Individual – perhaps anonymised
to postcode/zip code level
• Aggregate – counts are available
for a set of zones
• The nature of the data affects the
nature of the analysis

Geographic Information Systems
• GIS is often confused with mapping
• A range of activities which includes
inter alia mapping and analysis
• Will typically involve several datasets
• May also involve several different
pieces of software

Mapping: EpiINFO
• This is a rather simple analysis and
mapping program
• It can be downloaded from the internet
• It doesn’t provide much in the way of
spatial data manipulation…
• … nor are its analytical capabilities very
advanced
• It is easy to use

Spatial Data Manipulation
• GIS isn’t merely concerned with
data display or query
• It’s useful in bringing together
different spatial datasets in the
first stages of an analysis
• This may well require a range of
different geometrical and
database operations

Analysis with Spatial Data
• Statistical analysis usually involves
some separate software.
• Spatial data have some properties
which mean that SPSS isn’t always
appropriate.
• There is now a range of specialist
software – much of it available for
download

GIS may be ideal for data manipulation, display and query,
but it’s not always ideal for analysis – in epidemiology a
variety of analytical software is available … WinBUGS,
GeoBUGS (MRC Biostatistics Unit, Cambridge)

Software to implement
particular methods – for
example scan statistics - can be
downloaded quickly and easily
from the Internet. Kulldorf’s
SaTScan system implements a
range of different scan
statistical models

GIS in Health in Practice
• Facility location – dialysis units
• Facility location – obs & gyn
• Data integration – schizophrenia
in Cavan
• Data manipulation and display –
GMS card holders in Maynooth

1: Provision of Dialysis Units
• There are 5 dialysis units for renal
patients in a region with a population
of about 3,000,000
• Major population centre has two units
about 5 miles apart
• Where would you locate an extra one?
• What would happen if one was
closed?

Location-allocation
• Location-allocation problems deal
with optimum location of facilities
• Attempt to minimise total
weighted travel time/distance
from users to facilities
• What data is needed?

Data requirements
• Digitised road network with classification
of road types
• Mean travel speeds on each type for
urban/non-urban roads
• Extents of urban areas
• Anonymised postcoded patient records
• Postcode->grid reference lookup
• Locations of hospitals in region (both with
and without dialysis units)

GIS processing
• Data integration
• Determine which segments of road network
are urban
• Determine travel times using road type
urban/non-urban speeds
• Tally numbers of patients at each node in
the road network
• Find nearest nodes in network to each
hospital
• Code hospitals as ‘existing’ or ‘potential’
units

Modelling/Analysis
• Carried out using standard GIS
functions
• Convenient ‘front end’ written
using system’s macro language
(save a lot of typing)
• Explore a range of alternatives

Hospital with
dialysis unit

Study region showing hospital locations and urban areas

Patients requiring dialysis (tallied to road nodes)

Classified road network – includes roads outside study area

•

Some alternative
scenarios:

(a) Optimal allocation
of patients
(b) Provide 1 extra
unit
(c) Provide 2 extra
units

(d) Close 1 unit in
city and reallocate
its patients

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

integrate data
process data
model and analyse
generate alternative scenarios
display the results

2: Facility Location
• Two hospitals both provided facilities
for obstetrics, gynaecology, paediatrics
and a special care baby unit.
• Was there a case for rationalisation and
improving the facilities at one hospital
only?
• Would closure disadvantage any
patients?

Data
• Locations of hospitals in study region
• Anonymised postcoded finished
consultant episodes for each of the
four specialities at each hospital
• Classified road network
• Speeds associated with each road
type

Data preparation
• Postcoded records allocated grid
references using Central Postcode
Directory (POSTZON)
• Vector data converted to raster
• Patient/specialty/hospital counts
produced for each raster
• Passage times computed for each
raster cell based on road
classification and speed estimates

Roads, urban areas, patient locations, hospitals in study area

Processing
• Travel time surfaces computed for
each hospital
• Each raster cell contains time
required to reach hospital in minutes
• Assumed walking speed in ‘non road’
areas

Travel time surface for hospital S – darker = shorter time

Travel time surface for hospital T

Time differences
• If we subtract the travel time surfaces areas
nearer one hospital will have positive
values for the difference, and areas nearer
the other will have negative ones
• Boundary between positive and negative
values is boundary of catchment for each
hospital.
• If you’re on the boundary (difference is
zero), it doesn’t matter which hospital you
choose

Roads, patient locations, hospital catchments

Indifference
• If we identify those rasters where the time
difference (ignoring the sign) is, say, 5 –
people in those areas are in the 5 minute
‘indifference’ zone.
• Patients might not be worried about a 5
minute increase in their travel time, they
might be worried about 30 minutes
• Indifference zones can be computed quite
easily

15-minute indifference zone between the two hospitals

Mean access time
• For each cell in the raster we can
compute the product of the number
of patients and the time required to
reach each hospital
• Dividing this by the number of
patients gives a patient weighted
average time for each hospital

Average travel times for the various specialties. There
isn’t much to chose between specialities with the
exception of paediatric episodes.

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

gather data
integrate data
process data
model data
display results

• All carried out using raster
operations

3: Schizophrenia in Cavan
• In 1996 Pringle, Waddington and
Youssef reported on evidence for the
causes of schizophrenia
• Ecological analysis of variation among
EDs
– Cases varied from 0-11 per ED
– Populations varied from 250-2750

• Poisson probabilities suggested that
one area had an unusually high number
of cases – these can be mapped

Male cases
superimposed on plot
of prevalence by ED

Male cases
superimposed on
rectified Landsat
image with ED
boundaries

4: GMS card holders: Maynooth
•Data on GMS – Medical Card holders
drawn under strict data protection
protocols from DOHC
• Anonymised address lists data matched
with GeoDirectory
•Address matching rate of around 77% in
Maynooth

• Interesting range of results around
average for the whole ED

•ED – single value 14.6 % (national average around 30%)
•Small Area – Range from 2% to 70% - noticeable internal variation and
good indicator of elderly or poor populations

•Strategic value in comparing datasets
•Important in policy analysis

Mapping invites comparison

Role of GIS
• Data integration
– Tally of cases in small areas requires
data on locations of cases and small
area boundaries
– ‘spatial join’ places small area codes
on the case records
– Counting is a standard database
operation

• Data display

Finally
• GIS encourages you to think about
space
• In a ‘conventional’ map of Ireland
areas (EDs, Counties…) are shown in
proportion to their physical size
• There are alternative ways of
displaying maps…

In a cartogram the areas are drawn
proportional to their populations –
this is a “density-equalised” map
projection

A map of the boundaries of counties and
electoral divisions – the areas are drawn
proportional to their physical size

By comparison, the more deprived
western area of Dublin, northern area
of Cork and south east of Limerick are
clearly apparent in the cartogram.

Haase’s deprivation index suggests that the
most deprived populations are in peripheral
rural areas in the north west.

Redrawing the data on the cartogram
reveals wide variation in the SMR, and also
uncovers the urban dimension which is
hidden in the conventional display

Patient data matched to Electoral Divisions –
hospital admissions for psychiatric problems – the
patchwork effect does not suggest any pattern

The cartogram reveals the urban nature
of the problem – those areas which are
more deprived are also those with
higher levels of illness/disability

Residents aged 15+ who are unable to work
through illness or disability: the map pattern
suggests that might be a largely rural
phenomenon

The roads, rail, and urban areas data have
been transformed into the same space as
the cartogram. Dublin and Cork seem
poorly provided with roads…

Other spatial information can be transformed
to the space of the density equalised
projection. Here is the familiar map of main
roads and towns.

Using a cartogram we see that the
incidence of lung cancer is randomly
spatially distributed

Lung cancer in males in New York State [Gastner & Newman: 2004]

Plotting locations of lung cancer cases, we
obtain a map which follows the underlying
population distribution – there appear to be
“clusters”

What about Ireland?
• Potentially hampered by challenge of
geocoding patient records
• Lack of small area population data
– EDs have an average population of 1150
– Wide variation in size (50-28000)
– Socially inhomogenous

Rural addresses
• A major challenge is non-uniqueness
of addresses in rural areas.
• For instance in Moy ED, 11 properties
share the address
‘Lackamore, Lahinch, Co. Clare’

What’s needed
• Postcoding should provide a
mechanism for geocoding patient data
with the granularity required for
planning the delivery of health care –
this has been the case in the UK for a
for long time
• Census data should be made available
for areas with small and similar
populations (cf: UK Output Areas)

Finally…
• GIS has an important role to play
in health studies
• Health data analysis is a rapidly
developing field
• However, in Ireland we are
hampered by data deficiencies –
now is the time for change

Maynooth: image in Google Earth

Central Dublin: image in Google Earth

Taoiseach’s Office: image in Google Earth

Downing Street, London: image in Google Earth

Maynooth: OSi orthophotograph added locally uisng KML

Sellafield: image in Google Earth

NCU – On Line Atlas of Cancer Mortality

The Philippine Institute
for Development
Studies makes an online atlas of socioeconomic indicators
available – contains
some health measures

The future
• Patient empowerment…

• GIS used to merge information from
regulatory agencies on 19196 landfill sites
• Sites buffered to 2km and postcodes
within buffer identified
• Postcoded live births (8.2x106), stillbirths
(43471) and congenital anomalies
(124,597) between 1982 and 1997
• Poisson regression modelling undertaken
• Evidence found for excess risk of
congenital anomaly and low birthweight

• Distance from each
census ward to
nearest hospital
computed
• Map shows number
of patients waiting
longer than 6
months for elective
inpatient care per
available and
unoccupied bed
within 60 minutes
travel time


Slide 33

GIS and Health
Martin Charlton1
Ronan Foley2
Dennis Pringle2
1National

Centre for Geocomputation
2Department of Geography
NUI Maynooth

Outline
• Preliminary observations on GIS
and health
• Examples of GIS in Health:
research and practice
• Mapping people not places

Patterns in Health Data
• Three main areas:
– Mapping of disease incidence
• Relative risk estimation

– Identification of disease clustering
• Surveillance

– Ecological analysis
• Relationship between incidence and
covariates
(Lawson et al, 2003)

Health Data
• Typically patient data is available
in two forms
• Individual – perhaps anonymised
to postcode/zip code level
• Aggregate – counts are available
for a set of zones
• The nature of the data affects the
nature of the analysis

Geographic Information Systems
• GIS is often confused with mapping
• A range of activities which includes
inter alia mapping and analysis
• Will typically involve several datasets
• May also involve several different
pieces of software

Mapping: EpiINFO
• This is a rather simple analysis and
mapping program
• It can be downloaded from the internet
• It doesn’t provide much in the way of
spatial data manipulation…
• … nor are its analytical capabilities very
advanced
• It is easy to use

Spatial Data Manipulation
• GIS isn’t merely concerned with
data display or query
• It’s useful in bringing together
different spatial datasets in the
first stages of an analysis
• This may well require a range of
different geometrical and
database operations

Analysis with Spatial Data
• Statistical analysis usually involves
some separate software.
• Spatial data have some properties
which mean that SPSS isn’t always
appropriate.
• There is now a range of specialist
software – much of it available for
download

GIS may be ideal for data manipulation, display and query,
but it’s not always ideal for analysis – in epidemiology a
variety of analytical software is available … WinBUGS,
GeoBUGS (MRC Biostatistics Unit, Cambridge)

Software to implement
particular methods – for
example scan statistics - can be
downloaded quickly and easily
from the Internet. Kulldorf’s
SaTScan system implements a
range of different scan
statistical models

GIS in Health in Practice
• Facility location – dialysis units
• Facility location – obs & gyn
• Data integration – schizophrenia
in Cavan
• Data manipulation and display –
GMS card holders in Maynooth

1: Provision of Dialysis Units
• There are 5 dialysis units for renal
patients in a region with a population
of about 3,000,000
• Major population centre has two units
about 5 miles apart
• Where would you locate an extra one?
• What would happen if one was
closed?

Location-allocation
• Location-allocation problems deal
with optimum location of facilities
• Attempt to minimise total
weighted travel time/distance
from users to facilities
• What data is needed?

Data requirements
• Digitised road network with classification
of road types
• Mean travel speeds on each type for
urban/non-urban roads
• Extents of urban areas
• Anonymised postcoded patient records
• Postcode->grid reference lookup
• Locations of hospitals in region (both with
and without dialysis units)

GIS processing
• Data integration
• Determine which segments of road network
are urban
• Determine travel times using road type
urban/non-urban speeds
• Tally numbers of patients at each node in
the road network
• Find nearest nodes in network to each
hospital
• Code hospitals as ‘existing’ or ‘potential’
units

Modelling/Analysis
• Carried out using standard GIS
functions
• Convenient ‘front end’ written
using system’s macro language
(save a lot of typing)
• Explore a range of alternatives

Hospital with
dialysis unit

Study region showing hospital locations and urban areas

Patients requiring dialysis (tallied to road nodes)

Classified road network – includes roads outside study area

•

Some alternative
scenarios:

(a) Optimal allocation
of patients
(b) Provide 1 extra
unit
(c) Provide 2 extra
units

(d) Close 1 unit in
city and reallocate
its patients

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

integrate data
process data
model and analyse
generate alternative scenarios
display the results

2: Facility Location
• Two hospitals both provided facilities
for obstetrics, gynaecology, paediatrics
and a special care baby unit.
• Was there a case for rationalisation and
improving the facilities at one hospital
only?
• Would closure disadvantage any
patients?

Data
• Locations of hospitals in study region
• Anonymised postcoded finished
consultant episodes for each of the
four specialities at each hospital
• Classified road network
• Speeds associated with each road
type

Data preparation
• Postcoded records allocated grid
references using Central Postcode
Directory (POSTZON)
• Vector data converted to raster
• Patient/specialty/hospital counts
produced for each raster
• Passage times computed for each
raster cell based on road
classification and speed estimates

Roads, urban areas, patient locations, hospitals in study area

Processing
• Travel time surfaces computed for
each hospital
• Each raster cell contains time
required to reach hospital in minutes
• Assumed walking speed in ‘non road’
areas

Travel time surface for hospital S – darker = shorter time

Travel time surface for hospital T

Time differences
• If we subtract the travel time surfaces areas
nearer one hospital will have positive
values for the difference, and areas nearer
the other will have negative ones
• Boundary between positive and negative
values is boundary of catchment for each
hospital.
• If you’re on the boundary (difference is
zero), it doesn’t matter which hospital you
choose

Roads, patient locations, hospital catchments

Indifference
• If we identify those rasters where the time
difference (ignoring the sign) is, say, 5 –
people in those areas are in the 5 minute
‘indifference’ zone.
• Patients might not be worried about a 5
minute increase in their travel time, they
might be worried about 30 minutes
• Indifference zones can be computed quite
easily

15-minute indifference zone between the two hospitals

Mean access time
• For each cell in the raster we can
compute the product of the number
of patients and the time required to
reach each hospital
• Dividing this by the number of
patients gives a patient weighted
average time for each hospital

Average travel times for the various specialties. There
isn’t much to chose between specialities with the
exception of paediatric episodes.

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

gather data
integrate data
process data
model data
display results

• All carried out using raster
operations

3: Schizophrenia in Cavan
• In 1996 Pringle, Waddington and
Youssef reported on evidence for the
causes of schizophrenia
• Ecological analysis of variation among
EDs
– Cases varied from 0-11 per ED
– Populations varied from 250-2750

• Poisson probabilities suggested that
one area had an unusually high number
of cases – these can be mapped

Male cases
superimposed on plot
of prevalence by ED

Male cases
superimposed on
rectified Landsat
image with ED
boundaries

4: GMS card holders: Maynooth
•Data on GMS – Medical Card holders
drawn under strict data protection
protocols from DOHC
• Anonymised address lists data matched
with GeoDirectory
•Address matching rate of around 77% in
Maynooth

• Interesting range of results around
average for the whole ED

•ED – single value 14.6 % (national average around 30%)
•Small Area – Range from 2% to 70% - noticeable internal variation and
good indicator of elderly or poor populations

•Strategic value in comparing datasets
•Important in policy analysis

Mapping invites comparison

Role of GIS
• Data integration
– Tally of cases in small areas requires
data on locations of cases and small
area boundaries
– ‘spatial join’ places small area codes
on the case records
– Counting is a standard database
operation

• Data display

Finally
• GIS encourages you to think about
space
• In a ‘conventional’ map of Ireland
areas (EDs, Counties…) are shown in
proportion to their physical size
• There are alternative ways of
displaying maps…

In a cartogram the areas are drawn
proportional to their populations –
this is a “density-equalised” map
projection

A map of the boundaries of counties and
electoral divisions – the areas are drawn
proportional to their physical size

By comparison, the more deprived
western area of Dublin, northern area
of Cork and south east of Limerick are
clearly apparent in the cartogram.

Haase’s deprivation index suggests that the
most deprived populations are in peripheral
rural areas in the north west.

Redrawing the data on the cartogram
reveals wide variation in the SMR, and also
uncovers the urban dimension which is
hidden in the conventional display

Patient data matched to Electoral Divisions –
hospital admissions for psychiatric problems – the
patchwork effect does not suggest any pattern

The cartogram reveals the urban nature
of the problem – those areas which are
more deprived are also those with
higher levels of illness/disability

Residents aged 15+ who are unable to work
through illness or disability: the map pattern
suggests that might be a largely rural
phenomenon

The roads, rail, and urban areas data have
been transformed into the same space as
the cartogram. Dublin and Cork seem
poorly provided with roads…

Other spatial information can be transformed
to the space of the density equalised
projection. Here is the familiar map of main
roads and towns.

Using a cartogram we see that the
incidence of lung cancer is randomly
spatially distributed

Lung cancer in males in New York State [Gastner & Newman: 2004]

Plotting locations of lung cancer cases, we
obtain a map which follows the underlying
population distribution – there appear to be
“clusters”

What about Ireland?
• Potentially hampered by challenge of
geocoding patient records
• Lack of small area population data
– EDs have an average population of 1150
– Wide variation in size (50-28000)
– Socially inhomogenous

Rural addresses
• A major challenge is non-uniqueness
of addresses in rural areas.
• For instance in Moy ED, 11 properties
share the address
‘Lackamore, Lahinch, Co. Clare’

What’s needed
• Postcoding should provide a
mechanism for geocoding patient data
with the granularity required for
planning the delivery of health care –
this has been the case in the UK for a
for long time
• Census data should be made available
for areas with small and similar
populations (cf: UK Output Areas)

Finally…
• GIS has an important role to play
in health studies
• Health data analysis is a rapidly
developing field
• However, in Ireland we are
hampered by data deficiencies –
now is the time for change

Maynooth: image in Google Earth

Central Dublin: image in Google Earth

Taoiseach’s Office: image in Google Earth

Downing Street, London: image in Google Earth

Maynooth: OSi orthophotograph added locally uisng KML

Sellafield: image in Google Earth

NCU – On Line Atlas of Cancer Mortality

The Philippine Institute
for Development
Studies makes an online atlas of socioeconomic indicators
available – contains
some health measures

The future
• Patient empowerment…

• GIS used to merge information from
regulatory agencies on 19196 landfill sites
• Sites buffered to 2km and postcodes
within buffer identified
• Postcoded live births (8.2x106), stillbirths
(43471) and congenital anomalies
(124,597) between 1982 and 1997
• Poisson regression modelling undertaken
• Evidence found for excess risk of
congenital anomaly and low birthweight

• Distance from each
census ward to
nearest hospital
computed
• Map shows number
of patients waiting
longer than 6
months for elective
inpatient care per
available and
unoccupied bed
within 60 minutes
travel time


Slide 34

GIS and Health
Martin Charlton1
Ronan Foley2
Dennis Pringle2
1National

Centre for Geocomputation
2Department of Geography
NUI Maynooth

Outline
• Preliminary observations on GIS
and health
• Examples of GIS in Health:
research and practice
• Mapping people not places

Patterns in Health Data
• Three main areas:
– Mapping of disease incidence
• Relative risk estimation

– Identification of disease clustering
• Surveillance

– Ecological analysis
• Relationship between incidence and
covariates
(Lawson et al, 2003)

Health Data
• Typically patient data is available
in two forms
• Individual – perhaps anonymised
to postcode/zip code level
• Aggregate – counts are available
for a set of zones
• The nature of the data affects the
nature of the analysis

Geographic Information Systems
• GIS is often confused with mapping
• A range of activities which includes
inter alia mapping and analysis
• Will typically involve several datasets
• May also involve several different
pieces of software

Mapping: EpiINFO
• This is a rather simple analysis and
mapping program
• It can be downloaded from the internet
• It doesn’t provide much in the way of
spatial data manipulation…
• … nor are its analytical capabilities very
advanced
• It is easy to use

Spatial Data Manipulation
• GIS isn’t merely concerned with
data display or query
• It’s useful in bringing together
different spatial datasets in the
first stages of an analysis
• This may well require a range of
different geometrical and
database operations

Analysis with Spatial Data
• Statistical analysis usually involves
some separate software.
• Spatial data have some properties
which mean that SPSS isn’t always
appropriate.
• There is now a range of specialist
software – much of it available for
download

GIS may be ideal for data manipulation, display and query,
but it’s not always ideal for analysis – in epidemiology a
variety of analytical software is available … WinBUGS,
GeoBUGS (MRC Biostatistics Unit, Cambridge)

Software to implement
particular methods – for
example scan statistics - can be
downloaded quickly and easily
from the Internet. Kulldorf’s
SaTScan system implements a
range of different scan
statistical models

GIS in Health in Practice
• Facility location – dialysis units
• Facility location – obs & gyn
• Data integration – schizophrenia
in Cavan
• Data manipulation and display –
GMS card holders in Maynooth

1: Provision of Dialysis Units
• There are 5 dialysis units for renal
patients in a region with a population
of about 3,000,000
• Major population centre has two units
about 5 miles apart
• Where would you locate an extra one?
• What would happen if one was
closed?

Location-allocation
• Location-allocation problems deal
with optimum location of facilities
• Attempt to minimise total
weighted travel time/distance
from users to facilities
• What data is needed?

Data requirements
• Digitised road network with classification
of road types
• Mean travel speeds on each type for
urban/non-urban roads
• Extents of urban areas
• Anonymised postcoded patient records
• Postcode->grid reference lookup
• Locations of hospitals in region (both with
and without dialysis units)

GIS processing
• Data integration
• Determine which segments of road network
are urban
• Determine travel times using road type
urban/non-urban speeds
• Tally numbers of patients at each node in
the road network
• Find nearest nodes in network to each
hospital
• Code hospitals as ‘existing’ or ‘potential’
units

Modelling/Analysis
• Carried out using standard GIS
functions
• Convenient ‘front end’ written
using system’s macro language
(save a lot of typing)
• Explore a range of alternatives

Hospital with
dialysis unit

Study region showing hospital locations and urban areas

Patients requiring dialysis (tallied to road nodes)

Classified road network – includes roads outside study area

•

Some alternative
scenarios:

(a) Optimal allocation
of patients
(b) Provide 1 extra
unit
(c) Provide 2 extra
units

(d) Close 1 unit in
city and reallocate
its patients

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

integrate data
process data
model and analyse
generate alternative scenarios
display the results

2: Facility Location
• Two hospitals both provided facilities
for obstetrics, gynaecology, paediatrics
and a special care baby unit.
• Was there a case for rationalisation and
improving the facilities at one hospital
only?
• Would closure disadvantage any
patients?

Data
• Locations of hospitals in study region
• Anonymised postcoded finished
consultant episodes for each of the
four specialities at each hospital
• Classified road network
• Speeds associated with each road
type

Data preparation
• Postcoded records allocated grid
references using Central Postcode
Directory (POSTZON)
• Vector data converted to raster
• Patient/specialty/hospital counts
produced for each raster
• Passage times computed for each
raster cell based on road
classification and speed estimates

Roads, urban areas, patient locations, hospitals in study area

Processing
• Travel time surfaces computed for
each hospital
• Each raster cell contains time
required to reach hospital in minutes
• Assumed walking speed in ‘non road’
areas

Travel time surface for hospital S – darker = shorter time

Travel time surface for hospital T

Time differences
• If we subtract the travel time surfaces areas
nearer one hospital will have positive
values for the difference, and areas nearer
the other will have negative ones
• Boundary between positive and negative
values is boundary of catchment for each
hospital.
• If you’re on the boundary (difference is
zero), it doesn’t matter which hospital you
choose

Roads, patient locations, hospital catchments

Indifference
• If we identify those rasters where the time
difference (ignoring the sign) is, say, 5 –
people in those areas are in the 5 minute
‘indifference’ zone.
• Patients might not be worried about a 5
minute increase in their travel time, they
might be worried about 30 minutes
• Indifference zones can be computed quite
easily

15-minute indifference zone between the two hospitals

Mean access time
• For each cell in the raster we can
compute the product of the number
of patients and the time required to
reach each hospital
• Dividing this by the number of
patients gives a patient weighted
average time for each hospital

Average travel times for the various specialties. There
isn’t much to chose between specialities with the
exception of paediatric episodes.

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

gather data
integrate data
process data
model data
display results

• All carried out using raster
operations

3: Schizophrenia in Cavan
• In 1996 Pringle, Waddington and
Youssef reported on evidence for the
causes of schizophrenia
• Ecological analysis of variation among
EDs
– Cases varied from 0-11 per ED
– Populations varied from 250-2750

• Poisson probabilities suggested that
one area had an unusually high number
of cases – these can be mapped

Male cases
superimposed on plot
of prevalence by ED

Male cases
superimposed on
rectified Landsat
image with ED
boundaries

4: GMS card holders: Maynooth
•Data on GMS – Medical Card holders
drawn under strict data protection
protocols from DOHC
• Anonymised address lists data matched
with GeoDirectory
•Address matching rate of around 77% in
Maynooth

• Interesting range of results around
average for the whole ED

•ED – single value 14.6 % (national average around 30%)
•Small Area – Range from 2% to 70% - noticeable internal variation and
good indicator of elderly or poor populations

•Strategic value in comparing datasets
•Important in policy analysis

Mapping invites comparison

Role of GIS
• Data integration
– Tally of cases in small areas requires
data on locations of cases and small
area boundaries
– ‘spatial join’ places small area codes
on the case records
– Counting is a standard database
operation

• Data display

Finally
• GIS encourages you to think about
space
• In a ‘conventional’ map of Ireland
areas (EDs, Counties…) are shown in
proportion to their physical size
• There are alternative ways of
displaying maps…

In a cartogram the areas are drawn
proportional to their populations –
this is a “density-equalised” map
projection

A map of the boundaries of counties and
electoral divisions – the areas are drawn
proportional to their physical size

By comparison, the more deprived
western area of Dublin, northern area
of Cork and south east of Limerick are
clearly apparent in the cartogram.

Haase’s deprivation index suggests that the
most deprived populations are in peripheral
rural areas in the north west.

Redrawing the data on the cartogram
reveals wide variation in the SMR, and also
uncovers the urban dimension which is
hidden in the conventional display

Patient data matched to Electoral Divisions –
hospital admissions for psychiatric problems – the
patchwork effect does not suggest any pattern

The cartogram reveals the urban nature
of the problem – those areas which are
more deprived are also those with
higher levels of illness/disability

Residents aged 15+ who are unable to work
through illness or disability: the map pattern
suggests that might be a largely rural
phenomenon

The roads, rail, and urban areas data have
been transformed into the same space as
the cartogram. Dublin and Cork seem
poorly provided with roads…

Other spatial information can be transformed
to the space of the density equalised
projection. Here is the familiar map of main
roads and towns.

Using a cartogram we see that the
incidence of lung cancer is randomly
spatially distributed

Lung cancer in males in New York State [Gastner & Newman: 2004]

Plotting locations of lung cancer cases, we
obtain a map which follows the underlying
population distribution – there appear to be
“clusters”

What about Ireland?
• Potentially hampered by challenge of
geocoding patient records
• Lack of small area population data
– EDs have an average population of 1150
– Wide variation in size (50-28000)
– Socially inhomogenous

Rural addresses
• A major challenge is non-uniqueness
of addresses in rural areas.
• For instance in Moy ED, 11 properties
share the address
‘Lackamore, Lahinch, Co. Clare’

What’s needed
• Postcoding should provide a
mechanism for geocoding patient data
with the granularity required for
planning the delivery of health care –
this has been the case in the UK for a
for long time
• Census data should be made available
for areas with small and similar
populations (cf: UK Output Areas)

Finally…
• GIS has an important role to play
in health studies
• Health data analysis is a rapidly
developing field
• However, in Ireland we are
hampered by data deficiencies –
now is the time for change

Maynooth: image in Google Earth

Central Dublin: image in Google Earth

Taoiseach’s Office: image in Google Earth

Downing Street, London: image in Google Earth

Maynooth: OSi orthophotograph added locally uisng KML

Sellafield: image in Google Earth

NCU – On Line Atlas of Cancer Mortality

The Philippine Institute
for Development
Studies makes an online atlas of socioeconomic indicators
available – contains
some health measures

The future
• Patient empowerment…

• GIS used to merge information from
regulatory agencies on 19196 landfill sites
• Sites buffered to 2km and postcodes
within buffer identified
• Postcoded live births (8.2x106), stillbirths
(43471) and congenital anomalies
(124,597) between 1982 and 1997
• Poisson regression modelling undertaken
• Evidence found for excess risk of
congenital anomaly and low birthweight

• Distance from each
census ward to
nearest hospital
computed
• Map shows number
of patients waiting
longer than 6
months for elective
inpatient care per
available and
unoccupied bed
within 60 minutes
travel time


Slide 35

GIS and Health
Martin Charlton1
Ronan Foley2
Dennis Pringle2
1National

Centre for Geocomputation
2Department of Geography
NUI Maynooth

Outline
• Preliminary observations on GIS
and health
• Examples of GIS in Health:
research and practice
• Mapping people not places

Patterns in Health Data
• Three main areas:
– Mapping of disease incidence
• Relative risk estimation

– Identification of disease clustering
• Surveillance

– Ecological analysis
• Relationship between incidence and
covariates
(Lawson et al, 2003)

Health Data
• Typically patient data is available
in two forms
• Individual – perhaps anonymised
to postcode/zip code level
• Aggregate – counts are available
for a set of zones
• The nature of the data affects the
nature of the analysis

Geographic Information Systems
• GIS is often confused with mapping
• A range of activities which includes
inter alia mapping and analysis
• Will typically involve several datasets
• May also involve several different
pieces of software

Mapping: EpiINFO
• This is a rather simple analysis and
mapping program
• It can be downloaded from the internet
• It doesn’t provide much in the way of
spatial data manipulation…
• … nor are its analytical capabilities very
advanced
• It is easy to use

Spatial Data Manipulation
• GIS isn’t merely concerned with
data display or query
• It’s useful in bringing together
different spatial datasets in the
first stages of an analysis
• This may well require a range of
different geometrical and
database operations

Analysis with Spatial Data
• Statistical analysis usually involves
some separate software.
• Spatial data have some properties
which mean that SPSS isn’t always
appropriate.
• There is now a range of specialist
software – much of it available for
download

GIS may be ideal for data manipulation, display and query,
but it’s not always ideal for analysis – in epidemiology a
variety of analytical software is available … WinBUGS,
GeoBUGS (MRC Biostatistics Unit, Cambridge)

Software to implement
particular methods – for
example scan statistics - can be
downloaded quickly and easily
from the Internet. Kulldorf’s
SaTScan system implements a
range of different scan
statistical models

GIS in Health in Practice
• Facility location – dialysis units
• Facility location – obs & gyn
• Data integration – schizophrenia
in Cavan
• Data manipulation and display –
GMS card holders in Maynooth

1: Provision of Dialysis Units
• There are 5 dialysis units for renal
patients in a region with a population
of about 3,000,000
• Major population centre has two units
about 5 miles apart
• Where would you locate an extra one?
• What would happen if one was
closed?

Location-allocation
• Location-allocation problems deal
with optimum location of facilities
• Attempt to minimise total
weighted travel time/distance
from users to facilities
• What data is needed?

Data requirements
• Digitised road network with classification
of road types
• Mean travel speeds on each type for
urban/non-urban roads
• Extents of urban areas
• Anonymised postcoded patient records
• Postcode->grid reference lookup
• Locations of hospitals in region (both with
and without dialysis units)

GIS processing
• Data integration
• Determine which segments of road network
are urban
• Determine travel times using road type
urban/non-urban speeds
• Tally numbers of patients at each node in
the road network
• Find nearest nodes in network to each
hospital
• Code hospitals as ‘existing’ or ‘potential’
units

Modelling/Analysis
• Carried out using standard GIS
functions
• Convenient ‘front end’ written
using system’s macro language
(save a lot of typing)
• Explore a range of alternatives

Hospital with
dialysis unit

Study region showing hospital locations and urban areas

Patients requiring dialysis (tallied to road nodes)

Classified road network – includes roads outside study area

•

Some alternative
scenarios:

(a) Optimal allocation
of patients
(b) Provide 1 extra
unit
(c) Provide 2 extra
units

(d) Close 1 unit in
city and reallocate
its patients

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

integrate data
process data
model and analyse
generate alternative scenarios
display the results

2: Facility Location
• Two hospitals both provided facilities
for obstetrics, gynaecology, paediatrics
and a special care baby unit.
• Was there a case for rationalisation and
improving the facilities at one hospital
only?
• Would closure disadvantage any
patients?

Data
• Locations of hospitals in study region
• Anonymised postcoded finished
consultant episodes for each of the
four specialities at each hospital
• Classified road network
• Speeds associated with each road
type

Data preparation
• Postcoded records allocated grid
references using Central Postcode
Directory (POSTZON)
• Vector data converted to raster
• Patient/specialty/hospital counts
produced for each raster
• Passage times computed for each
raster cell based on road
classification and speed estimates

Roads, urban areas, patient locations, hospitals in study area

Processing
• Travel time surfaces computed for
each hospital
• Each raster cell contains time
required to reach hospital in minutes
• Assumed walking speed in ‘non road’
areas

Travel time surface for hospital S – darker = shorter time

Travel time surface for hospital T

Time differences
• If we subtract the travel time surfaces areas
nearer one hospital will have positive
values for the difference, and areas nearer
the other will have negative ones
• Boundary between positive and negative
values is boundary of catchment for each
hospital.
• If you’re on the boundary (difference is
zero), it doesn’t matter which hospital you
choose

Roads, patient locations, hospital catchments

Indifference
• If we identify those rasters where the time
difference (ignoring the sign) is, say, 5 –
people in those areas are in the 5 minute
‘indifference’ zone.
• Patients might not be worried about a 5
minute increase in their travel time, they
might be worried about 30 minutes
• Indifference zones can be computed quite
easily

15-minute indifference zone between the two hospitals

Mean access time
• For each cell in the raster we can
compute the product of the number
of patients and the time required to
reach each hospital
• Dividing this by the number of
patients gives a patient weighted
average time for each hospital

Average travel times for the various specialties. There
isn’t much to chose between specialities with the
exception of paediatric episodes.

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

gather data
integrate data
process data
model data
display results

• All carried out using raster
operations

3: Schizophrenia in Cavan
• In 1996 Pringle, Waddington and
Youssef reported on evidence for the
causes of schizophrenia
• Ecological analysis of variation among
EDs
– Cases varied from 0-11 per ED
– Populations varied from 250-2750

• Poisson probabilities suggested that
one area had an unusually high number
of cases – these can be mapped

Male cases
superimposed on plot
of prevalence by ED

Male cases
superimposed on
rectified Landsat
image with ED
boundaries

4: GMS card holders: Maynooth
•Data on GMS – Medical Card holders
drawn under strict data protection
protocols from DOHC
• Anonymised address lists data matched
with GeoDirectory
•Address matching rate of around 77% in
Maynooth

• Interesting range of results around
average for the whole ED

•ED – single value 14.6 % (national average around 30%)
•Small Area – Range from 2% to 70% - noticeable internal variation and
good indicator of elderly or poor populations

•Strategic value in comparing datasets
•Important in policy analysis

Mapping invites comparison

Role of GIS
• Data integration
– Tally of cases in small areas requires
data on locations of cases and small
area boundaries
– ‘spatial join’ places small area codes
on the case records
– Counting is a standard database
operation

• Data display

Finally
• GIS encourages you to think about
space
• In a ‘conventional’ map of Ireland
areas (EDs, Counties…) are shown in
proportion to their physical size
• There are alternative ways of
displaying maps…

In a cartogram the areas are drawn
proportional to their populations –
this is a “density-equalised” map
projection

A map of the boundaries of counties and
electoral divisions – the areas are drawn
proportional to their physical size

By comparison, the more deprived
western area of Dublin, northern area
of Cork and south east of Limerick are
clearly apparent in the cartogram.

Haase’s deprivation index suggests that the
most deprived populations are in peripheral
rural areas in the north west.

Redrawing the data on the cartogram
reveals wide variation in the SMR, and also
uncovers the urban dimension which is
hidden in the conventional display

Patient data matched to Electoral Divisions –
hospital admissions for psychiatric problems – the
patchwork effect does not suggest any pattern

The cartogram reveals the urban nature
of the problem – those areas which are
more deprived are also those with
higher levels of illness/disability

Residents aged 15+ who are unable to work
through illness or disability: the map pattern
suggests that might be a largely rural
phenomenon

The roads, rail, and urban areas data have
been transformed into the same space as
the cartogram. Dublin and Cork seem
poorly provided with roads…

Other spatial information can be transformed
to the space of the density equalised
projection. Here is the familiar map of main
roads and towns.

Using a cartogram we see that the
incidence of lung cancer is randomly
spatially distributed

Lung cancer in males in New York State [Gastner & Newman: 2004]

Plotting locations of lung cancer cases, we
obtain a map which follows the underlying
population distribution – there appear to be
“clusters”

What about Ireland?
• Potentially hampered by challenge of
geocoding patient records
• Lack of small area population data
– EDs have an average population of 1150
– Wide variation in size (50-28000)
– Socially inhomogenous

Rural addresses
• A major challenge is non-uniqueness
of addresses in rural areas.
• For instance in Moy ED, 11 properties
share the address
‘Lackamore, Lahinch, Co. Clare’

What’s needed
• Postcoding should provide a
mechanism for geocoding patient data
with the granularity required for
planning the delivery of health care –
this has been the case in the UK for a
for long time
• Census data should be made available
for areas with small and similar
populations (cf: UK Output Areas)

Finally…
• GIS has an important role to play
in health studies
• Health data analysis is a rapidly
developing field
• However, in Ireland we are
hampered by data deficiencies –
now is the time for change

Maynooth: image in Google Earth

Central Dublin: image in Google Earth

Taoiseach’s Office: image in Google Earth

Downing Street, London: image in Google Earth

Maynooth: OSi orthophotograph added locally uisng KML

Sellafield: image in Google Earth

NCU – On Line Atlas of Cancer Mortality

The Philippine Institute
for Development
Studies makes an online atlas of socioeconomic indicators
available – contains
some health measures

The future
• Patient empowerment…

• GIS used to merge information from
regulatory agencies on 19196 landfill sites
• Sites buffered to 2km and postcodes
within buffer identified
• Postcoded live births (8.2x106), stillbirths
(43471) and congenital anomalies
(124,597) between 1982 and 1997
• Poisson regression modelling undertaken
• Evidence found for excess risk of
congenital anomaly and low birthweight

• Distance from each
census ward to
nearest hospital
computed
• Map shows number
of patients waiting
longer than 6
months for elective
inpatient care per
available and
unoccupied bed
within 60 minutes
travel time


Slide 36

GIS and Health
Martin Charlton1
Ronan Foley2
Dennis Pringle2
1National

Centre for Geocomputation
2Department of Geography
NUI Maynooth

Outline
• Preliminary observations on GIS
and health
• Examples of GIS in Health:
research and practice
• Mapping people not places

Patterns in Health Data
• Three main areas:
– Mapping of disease incidence
• Relative risk estimation

– Identification of disease clustering
• Surveillance

– Ecological analysis
• Relationship between incidence and
covariates
(Lawson et al, 2003)

Health Data
• Typically patient data is available
in two forms
• Individual – perhaps anonymised
to postcode/zip code level
• Aggregate – counts are available
for a set of zones
• The nature of the data affects the
nature of the analysis

Geographic Information Systems
• GIS is often confused with mapping
• A range of activities which includes
inter alia mapping and analysis
• Will typically involve several datasets
• May also involve several different
pieces of software

Mapping: EpiINFO
• This is a rather simple analysis and
mapping program
• It can be downloaded from the internet
• It doesn’t provide much in the way of
spatial data manipulation…
• … nor are its analytical capabilities very
advanced
• It is easy to use

Spatial Data Manipulation
• GIS isn’t merely concerned with
data display or query
• It’s useful in bringing together
different spatial datasets in the
first stages of an analysis
• This may well require a range of
different geometrical and
database operations

Analysis with Spatial Data
• Statistical analysis usually involves
some separate software.
• Spatial data have some properties
which mean that SPSS isn’t always
appropriate.
• There is now a range of specialist
software – much of it available for
download

GIS may be ideal for data manipulation, display and query,
but it’s not always ideal for analysis – in epidemiology a
variety of analytical software is available … WinBUGS,
GeoBUGS (MRC Biostatistics Unit, Cambridge)

Software to implement
particular methods – for
example scan statistics - can be
downloaded quickly and easily
from the Internet. Kulldorf’s
SaTScan system implements a
range of different scan
statistical models

GIS in Health in Practice
• Facility location – dialysis units
• Facility location – obs & gyn
• Data integration – schizophrenia
in Cavan
• Data manipulation and display –
GMS card holders in Maynooth

1: Provision of Dialysis Units
• There are 5 dialysis units for renal
patients in a region with a population
of about 3,000,000
• Major population centre has two units
about 5 miles apart
• Where would you locate an extra one?
• What would happen if one was
closed?

Location-allocation
• Location-allocation problems deal
with optimum location of facilities
• Attempt to minimise total
weighted travel time/distance
from users to facilities
• What data is needed?

Data requirements
• Digitised road network with classification
of road types
• Mean travel speeds on each type for
urban/non-urban roads
• Extents of urban areas
• Anonymised postcoded patient records
• Postcode->grid reference lookup
• Locations of hospitals in region (both with
and without dialysis units)

GIS processing
• Data integration
• Determine which segments of road network
are urban
• Determine travel times using road type
urban/non-urban speeds
• Tally numbers of patients at each node in
the road network
• Find nearest nodes in network to each
hospital
• Code hospitals as ‘existing’ or ‘potential’
units

Modelling/Analysis
• Carried out using standard GIS
functions
• Convenient ‘front end’ written
using system’s macro language
(save a lot of typing)
• Explore a range of alternatives

Hospital with
dialysis unit

Study region showing hospital locations and urban areas

Patients requiring dialysis (tallied to road nodes)

Classified road network – includes roads outside study area

•

Some alternative
scenarios:

(a) Optimal allocation
of patients
(b) Provide 1 extra
unit
(c) Provide 2 extra
units

(d) Close 1 unit in
city and reallocate
its patients

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

integrate data
process data
model and analyse
generate alternative scenarios
display the results

2: Facility Location
• Two hospitals both provided facilities
for obstetrics, gynaecology, paediatrics
and a special care baby unit.
• Was there a case for rationalisation and
improving the facilities at one hospital
only?
• Would closure disadvantage any
patients?

Data
• Locations of hospitals in study region
• Anonymised postcoded finished
consultant episodes for each of the
four specialities at each hospital
• Classified road network
• Speeds associated with each road
type

Data preparation
• Postcoded records allocated grid
references using Central Postcode
Directory (POSTZON)
• Vector data converted to raster
• Patient/specialty/hospital counts
produced for each raster
• Passage times computed for each
raster cell based on road
classification and speed estimates

Roads, urban areas, patient locations, hospitals in study area

Processing
• Travel time surfaces computed for
each hospital
• Each raster cell contains time
required to reach hospital in minutes
• Assumed walking speed in ‘non road’
areas

Travel time surface for hospital S – darker = shorter time

Travel time surface for hospital T

Time differences
• If we subtract the travel time surfaces areas
nearer one hospital will have positive
values for the difference, and areas nearer
the other will have negative ones
• Boundary between positive and negative
values is boundary of catchment for each
hospital.
• If you’re on the boundary (difference is
zero), it doesn’t matter which hospital you
choose

Roads, patient locations, hospital catchments

Indifference
• If we identify those rasters where the time
difference (ignoring the sign) is, say, 5 –
people in those areas are in the 5 minute
‘indifference’ zone.
• Patients might not be worried about a 5
minute increase in their travel time, they
might be worried about 30 minutes
• Indifference zones can be computed quite
easily

15-minute indifference zone between the two hospitals

Mean access time
• For each cell in the raster we can
compute the product of the number
of patients and the time required to
reach each hospital
• Dividing this by the number of
patients gives a patient weighted
average time for each hospital

Average travel times for the various specialties. There
isn’t much to chose between specialities with the
exception of paediatric episodes.

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

gather data
integrate data
process data
model data
display results

• All carried out using raster
operations

3: Schizophrenia in Cavan
• In 1996 Pringle, Waddington and
Youssef reported on evidence for the
causes of schizophrenia
• Ecological analysis of variation among
EDs
– Cases varied from 0-11 per ED
– Populations varied from 250-2750

• Poisson probabilities suggested that
one area had an unusually high number
of cases – these can be mapped

Male cases
superimposed on plot
of prevalence by ED

Male cases
superimposed on
rectified Landsat
image with ED
boundaries

4: GMS card holders: Maynooth
•Data on GMS – Medical Card holders
drawn under strict data protection
protocols from DOHC
• Anonymised address lists data matched
with GeoDirectory
•Address matching rate of around 77% in
Maynooth

• Interesting range of results around
average for the whole ED

•ED – single value 14.6 % (national average around 30%)
•Small Area – Range from 2% to 70% - noticeable internal variation and
good indicator of elderly or poor populations

•Strategic value in comparing datasets
•Important in policy analysis

Mapping invites comparison

Role of GIS
• Data integration
– Tally of cases in small areas requires
data on locations of cases and small
area boundaries
– ‘spatial join’ places small area codes
on the case records
– Counting is a standard database
operation

• Data display

Finally
• GIS encourages you to think about
space
• In a ‘conventional’ map of Ireland
areas (EDs, Counties…) are shown in
proportion to their physical size
• There are alternative ways of
displaying maps…

In a cartogram the areas are drawn
proportional to their populations –
this is a “density-equalised” map
projection

A map of the boundaries of counties and
electoral divisions – the areas are drawn
proportional to their physical size

By comparison, the more deprived
western area of Dublin, northern area
of Cork and south east of Limerick are
clearly apparent in the cartogram.

Haase’s deprivation index suggests that the
most deprived populations are in peripheral
rural areas in the north west.

Redrawing the data on the cartogram
reveals wide variation in the SMR, and also
uncovers the urban dimension which is
hidden in the conventional display

Patient data matched to Electoral Divisions –
hospital admissions for psychiatric problems – the
patchwork effect does not suggest any pattern

The cartogram reveals the urban nature
of the problem – those areas which are
more deprived are also those with
higher levels of illness/disability

Residents aged 15+ who are unable to work
through illness or disability: the map pattern
suggests that might be a largely rural
phenomenon

The roads, rail, and urban areas data have
been transformed into the same space as
the cartogram. Dublin and Cork seem
poorly provided with roads…

Other spatial information can be transformed
to the space of the density equalised
projection. Here is the familiar map of main
roads and towns.

Using a cartogram we see that the
incidence of lung cancer is randomly
spatially distributed

Lung cancer in males in New York State [Gastner & Newman: 2004]

Plotting locations of lung cancer cases, we
obtain a map which follows the underlying
population distribution – there appear to be
“clusters”

What about Ireland?
• Potentially hampered by challenge of
geocoding patient records
• Lack of small area population data
– EDs have an average population of 1150
– Wide variation in size (50-28000)
– Socially inhomogenous

Rural addresses
• A major challenge is non-uniqueness
of addresses in rural areas.
• For instance in Moy ED, 11 properties
share the address
‘Lackamore, Lahinch, Co. Clare’

What’s needed
• Postcoding should provide a
mechanism for geocoding patient data
with the granularity required for
planning the delivery of health care –
this has been the case in the UK for a
for long time
• Census data should be made available
for areas with small and similar
populations (cf: UK Output Areas)

Finally…
• GIS has an important role to play
in health studies
• Health data analysis is a rapidly
developing field
• However, in Ireland we are
hampered by data deficiencies –
now is the time for change

Maynooth: image in Google Earth

Central Dublin: image in Google Earth

Taoiseach’s Office: image in Google Earth

Downing Street, London: image in Google Earth

Maynooth: OSi orthophotograph added locally uisng KML

Sellafield: image in Google Earth

NCU – On Line Atlas of Cancer Mortality

The Philippine Institute
for Development
Studies makes an online atlas of socioeconomic indicators
available – contains
some health measures

The future
• Patient empowerment…

• GIS used to merge information from
regulatory agencies on 19196 landfill sites
• Sites buffered to 2km and postcodes
within buffer identified
• Postcoded live births (8.2x106), stillbirths
(43471) and congenital anomalies
(124,597) between 1982 and 1997
• Poisson regression modelling undertaken
• Evidence found for excess risk of
congenital anomaly and low birthweight

• Distance from each
census ward to
nearest hospital
computed
• Map shows number
of patients waiting
longer than 6
months for elective
inpatient care per
available and
unoccupied bed
within 60 minutes
travel time


Slide 37

GIS and Health
Martin Charlton1
Ronan Foley2
Dennis Pringle2
1National

Centre for Geocomputation
2Department of Geography
NUI Maynooth

Outline
• Preliminary observations on GIS
and health
• Examples of GIS in Health:
research and practice
• Mapping people not places

Patterns in Health Data
• Three main areas:
– Mapping of disease incidence
• Relative risk estimation

– Identification of disease clustering
• Surveillance

– Ecological analysis
• Relationship between incidence and
covariates
(Lawson et al, 2003)

Health Data
• Typically patient data is available
in two forms
• Individual – perhaps anonymised
to postcode/zip code level
• Aggregate – counts are available
for a set of zones
• The nature of the data affects the
nature of the analysis

Geographic Information Systems
• GIS is often confused with mapping
• A range of activities which includes
inter alia mapping and analysis
• Will typically involve several datasets
• May also involve several different
pieces of software

Mapping: EpiINFO
• This is a rather simple analysis and
mapping program
• It can be downloaded from the internet
• It doesn’t provide much in the way of
spatial data manipulation…
• … nor are its analytical capabilities very
advanced
• It is easy to use

Spatial Data Manipulation
• GIS isn’t merely concerned with
data display or query
• It’s useful in bringing together
different spatial datasets in the
first stages of an analysis
• This may well require a range of
different geometrical and
database operations

Analysis with Spatial Data
• Statistical analysis usually involves
some separate software.
• Spatial data have some properties
which mean that SPSS isn’t always
appropriate.
• There is now a range of specialist
software – much of it available for
download

GIS may be ideal for data manipulation, display and query,
but it’s not always ideal for analysis – in epidemiology a
variety of analytical software is available … WinBUGS,
GeoBUGS (MRC Biostatistics Unit, Cambridge)

Software to implement
particular methods – for
example scan statistics - can be
downloaded quickly and easily
from the Internet. Kulldorf’s
SaTScan system implements a
range of different scan
statistical models

GIS in Health in Practice
• Facility location – dialysis units
• Facility location – obs & gyn
• Data integration – schizophrenia
in Cavan
• Data manipulation and display –
GMS card holders in Maynooth

1: Provision of Dialysis Units
• There are 5 dialysis units for renal
patients in a region with a population
of about 3,000,000
• Major population centre has two units
about 5 miles apart
• Where would you locate an extra one?
• What would happen if one was
closed?

Location-allocation
• Location-allocation problems deal
with optimum location of facilities
• Attempt to minimise total
weighted travel time/distance
from users to facilities
• What data is needed?

Data requirements
• Digitised road network with classification
of road types
• Mean travel speeds on each type for
urban/non-urban roads
• Extents of urban areas
• Anonymised postcoded patient records
• Postcode->grid reference lookup
• Locations of hospitals in region (both with
and without dialysis units)

GIS processing
• Data integration
• Determine which segments of road network
are urban
• Determine travel times using road type
urban/non-urban speeds
• Tally numbers of patients at each node in
the road network
• Find nearest nodes in network to each
hospital
• Code hospitals as ‘existing’ or ‘potential’
units

Modelling/Analysis
• Carried out using standard GIS
functions
• Convenient ‘front end’ written
using system’s macro language
(save a lot of typing)
• Explore a range of alternatives

Hospital with
dialysis unit

Study region showing hospital locations and urban areas

Patients requiring dialysis (tallied to road nodes)

Classified road network – includes roads outside study area

•

Some alternative
scenarios:

(a) Optimal allocation
of patients
(b) Provide 1 extra
unit
(c) Provide 2 extra
units

(d) Close 1 unit in
city and reallocate
its patients

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

integrate data
process data
model and analyse
generate alternative scenarios
display the results

2: Facility Location
• Two hospitals both provided facilities
for obstetrics, gynaecology, paediatrics
and a special care baby unit.
• Was there a case for rationalisation and
improving the facilities at one hospital
only?
• Would closure disadvantage any
patients?

Data
• Locations of hospitals in study region
• Anonymised postcoded finished
consultant episodes for each of the
four specialities at each hospital
• Classified road network
• Speeds associated with each road
type

Data preparation
• Postcoded records allocated grid
references using Central Postcode
Directory (POSTZON)
• Vector data converted to raster
• Patient/specialty/hospital counts
produced for each raster
• Passage times computed for each
raster cell based on road
classification and speed estimates

Roads, urban areas, patient locations, hospitals in study area

Processing
• Travel time surfaces computed for
each hospital
• Each raster cell contains time
required to reach hospital in minutes
• Assumed walking speed in ‘non road’
areas

Travel time surface for hospital S – darker = shorter time

Travel time surface for hospital T

Time differences
• If we subtract the travel time surfaces areas
nearer one hospital will have positive
values for the difference, and areas nearer
the other will have negative ones
• Boundary between positive and negative
values is boundary of catchment for each
hospital.
• If you’re on the boundary (difference is
zero), it doesn’t matter which hospital you
choose

Roads, patient locations, hospital catchments

Indifference
• If we identify those rasters where the time
difference (ignoring the sign) is, say, 5 –
people in those areas are in the 5 minute
‘indifference’ zone.
• Patients might not be worried about a 5
minute increase in their travel time, they
might be worried about 30 minutes
• Indifference zones can be computed quite
easily

15-minute indifference zone between the two hospitals

Mean access time
• For each cell in the raster we can
compute the product of the number
of patients and the time required to
reach each hospital
• Dividing this by the number of
patients gives a patient weighted
average time for each hospital

Average travel times for the various specialties. There
isn’t much to chose between specialities with the
exception of paediatric episodes.

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

gather data
integrate data
process data
model data
display results

• All carried out using raster
operations

3: Schizophrenia in Cavan
• In 1996 Pringle, Waddington and
Youssef reported on evidence for the
causes of schizophrenia
• Ecological analysis of variation among
EDs
– Cases varied from 0-11 per ED
– Populations varied from 250-2750

• Poisson probabilities suggested that
one area had an unusually high number
of cases – these can be mapped

Male cases
superimposed on plot
of prevalence by ED

Male cases
superimposed on
rectified Landsat
image with ED
boundaries

4: GMS card holders: Maynooth
•Data on GMS – Medical Card holders
drawn under strict data protection
protocols from DOHC
• Anonymised address lists data matched
with GeoDirectory
•Address matching rate of around 77% in
Maynooth

• Interesting range of results around
average for the whole ED

•ED – single value 14.6 % (national average around 30%)
•Small Area – Range from 2% to 70% - noticeable internal variation and
good indicator of elderly or poor populations

•Strategic value in comparing datasets
•Important in policy analysis

Mapping invites comparison

Role of GIS
• Data integration
– Tally of cases in small areas requires
data on locations of cases and small
area boundaries
– ‘spatial join’ places small area codes
on the case records
– Counting is a standard database
operation

• Data display

Finally
• GIS encourages you to think about
space
• In a ‘conventional’ map of Ireland
areas (EDs, Counties…) are shown in
proportion to their physical size
• There are alternative ways of
displaying maps…

In a cartogram the areas are drawn
proportional to their populations –
this is a “density-equalised” map
projection

A map of the boundaries of counties and
electoral divisions – the areas are drawn
proportional to their physical size

By comparison, the more deprived
western area of Dublin, northern area
of Cork and south east of Limerick are
clearly apparent in the cartogram.

Haase’s deprivation index suggests that the
most deprived populations are in peripheral
rural areas in the north west.

Redrawing the data on the cartogram
reveals wide variation in the SMR, and also
uncovers the urban dimension which is
hidden in the conventional display

Patient data matched to Electoral Divisions –
hospital admissions for psychiatric problems – the
patchwork effect does not suggest any pattern

The cartogram reveals the urban nature
of the problem – those areas which are
more deprived are also those with
higher levels of illness/disability

Residents aged 15+ who are unable to work
through illness or disability: the map pattern
suggests that might be a largely rural
phenomenon

The roads, rail, and urban areas data have
been transformed into the same space as
the cartogram. Dublin and Cork seem
poorly provided with roads…

Other spatial information can be transformed
to the space of the density equalised
projection. Here is the familiar map of main
roads and towns.

Using a cartogram we see that the
incidence of lung cancer is randomly
spatially distributed

Lung cancer in males in New York State [Gastner & Newman: 2004]

Plotting locations of lung cancer cases, we
obtain a map which follows the underlying
population distribution – there appear to be
“clusters”

What about Ireland?
• Potentially hampered by challenge of
geocoding patient records
• Lack of small area population data
– EDs have an average population of 1150
– Wide variation in size (50-28000)
– Socially inhomogenous

Rural addresses
• A major challenge is non-uniqueness
of addresses in rural areas.
• For instance in Moy ED, 11 properties
share the address
‘Lackamore, Lahinch, Co. Clare’

What’s needed
• Postcoding should provide a
mechanism for geocoding patient data
with the granularity required for
planning the delivery of health care –
this has been the case in the UK for a
for long time
• Census data should be made available
for areas with small and similar
populations (cf: UK Output Areas)

Finally…
• GIS has an important role to play
in health studies
• Health data analysis is a rapidly
developing field
• However, in Ireland we are
hampered by data deficiencies –
now is the time for change

Maynooth: image in Google Earth

Central Dublin: image in Google Earth

Taoiseach’s Office: image in Google Earth

Downing Street, London: image in Google Earth

Maynooth: OSi orthophotograph added locally uisng KML

Sellafield: image in Google Earth

NCU – On Line Atlas of Cancer Mortality

The Philippine Institute
for Development
Studies makes an online atlas of socioeconomic indicators
available – contains
some health measures

The future
• Patient empowerment…

• GIS used to merge information from
regulatory agencies on 19196 landfill sites
• Sites buffered to 2km and postcodes
within buffer identified
• Postcoded live births (8.2x106), stillbirths
(43471) and congenital anomalies
(124,597) between 1982 and 1997
• Poisson regression modelling undertaken
• Evidence found for excess risk of
congenital anomaly and low birthweight

• Distance from each
census ward to
nearest hospital
computed
• Map shows number
of patients waiting
longer than 6
months for elective
inpatient care per
available and
unoccupied bed
within 60 minutes
travel time


Slide 38

GIS and Health
Martin Charlton1
Ronan Foley2
Dennis Pringle2
1National

Centre for Geocomputation
2Department of Geography
NUI Maynooth

Outline
• Preliminary observations on GIS
and health
• Examples of GIS in Health:
research and practice
• Mapping people not places

Patterns in Health Data
• Three main areas:
– Mapping of disease incidence
• Relative risk estimation

– Identification of disease clustering
• Surveillance

– Ecological analysis
• Relationship between incidence and
covariates
(Lawson et al, 2003)

Health Data
• Typically patient data is available
in two forms
• Individual – perhaps anonymised
to postcode/zip code level
• Aggregate – counts are available
for a set of zones
• The nature of the data affects the
nature of the analysis

Geographic Information Systems
• GIS is often confused with mapping
• A range of activities which includes
inter alia mapping and analysis
• Will typically involve several datasets
• May also involve several different
pieces of software

Mapping: EpiINFO
• This is a rather simple analysis and
mapping program
• It can be downloaded from the internet
• It doesn’t provide much in the way of
spatial data manipulation…
• … nor are its analytical capabilities very
advanced
• It is easy to use

Spatial Data Manipulation
• GIS isn’t merely concerned with
data display or query
• It’s useful in bringing together
different spatial datasets in the
first stages of an analysis
• This may well require a range of
different geometrical and
database operations

Analysis with Spatial Data
• Statistical analysis usually involves
some separate software.
• Spatial data have some properties
which mean that SPSS isn’t always
appropriate.
• There is now a range of specialist
software – much of it available for
download

GIS may be ideal for data manipulation, display and query,
but it’s not always ideal for analysis – in epidemiology a
variety of analytical software is available … WinBUGS,
GeoBUGS (MRC Biostatistics Unit, Cambridge)

Software to implement
particular methods – for
example scan statistics - can be
downloaded quickly and easily
from the Internet. Kulldorf’s
SaTScan system implements a
range of different scan
statistical models

GIS in Health in Practice
• Facility location – dialysis units
• Facility location – obs & gyn
• Data integration – schizophrenia
in Cavan
• Data manipulation and display –
GMS card holders in Maynooth

1: Provision of Dialysis Units
• There are 5 dialysis units for renal
patients in a region with a population
of about 3,000,000
• Major population centre has two units
about 5 miles apart
• Where would you locate an extra one?
• What would happen if one was
closed?

Location-allocation
• Location-allocation problems deal
with optimum location of facilities
• Attempt to minimise total
weighted travel time/distance
from users to facilities
• What data is needed?

Data requirements
• Digitised road network with classification
of road types
• Mean travel speeds on each type for
urban/non-urban roads
• Extents of urban areas
• Anonymised postcoded patient records
• Postcode->grid reference lookup
• Locations of hospitals in region (both with
and without dialysis units)

GIS processing
• Data integration
• Determine which segments of road network
are urban
• Determine travel times using road type
urban/non-urban speeds
• Tally numbers of patients at each node in
the road network
• Find nearest nodes in network to each
hospital
• Code hospitals as ‘existing’ or ‘potential’
units

Modelling/Analysis
• Carried out using standard GIS
functions
• Convenient ‘front end’ written
using system’s macro language
(save a lot of typing)
• Explore a range of alternatives

Hospital with
dialysis unit

Study region showing hospital locations and urban areas

Patients requiring dialysis (tallied to road nodes)

Classified road network – includes roads outside study area

•

Some alternative
scenarios:

(a) Optimal allocation
of patients
(b) Provide 1 extra
unit
(c) Provide 2 extra
units

(d) Close 1 unit in
city and reallocate
its patients

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

integrate data
process data
model and analyse
generate alternative scenarios
display the results

2: Facility Location
• Two hospitals both provided facilities
for obstetrics, gynaecology, paediatrics
and a special care baby unit.
• Was there a case for rationalisation and
improving the facilities at one hospital
only?
• Would closure disadvantage any
patients?

Data
• Locations of hospitals in study region
• Anonymised postcoded finished
consultant episodes for each of the
four specialities at each hospital
• Classified road network
• Speeds associated with each road
type

Data preparation
• Postcoded records allocated grid
references using Central Postcode
Directory (POSTZON)
• Vector data converted to raster
• Patient/specialty/hospital counts
produced for each raster
• Passage times computed for each
raster cell based on road
classification and speed estimates

Roads, urban areas, patient locations, hospitals in study area

Processing
• Travel time surfaces computed for
each hospital
• Each raster cell contains time
required to reach hospital in minutes
• Assumed walking speed in ‘non road’
areas

Travel time surface for hospital S – darker = shorter time

Travel time surface for hospital T

Time differences
• If we subtract the travel time surfaces areas
nearer one hospital will have positive
values for the difference, and areas nearer
the other will have negative ones
• Boundary between positive and negative
values is boundary of catchment for each
hospital.
• If you’re on the boundary (difference is
zero), it doesn’t matter which hospital you
choose

Roads, patient locations, hospital catchments

Indifference
• If we identify those rasters where the time
difference (ignoring the sign) is, say, 5 –
people in those areas are in the 5 minute
‘indifference’ zone.
• Patients might not be worried about a 5
minute increase in their travel time, they
might be worried about 30 minutes
• Indifference zones can be computed quite
easily

15-minute indifference zone between the two hospitals

Mean access time
• For each cell in the raster we can
compute the product of the number
of patients and the time required to
reach each hospital
• Dividing this by the number of
patients gives a patient weighted
average time for each hospital

Average travel times for the various specialties. There
isn’t much to chose between specialities with the
exception of paediatric episodes.

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

gather data
integrate data
process data
model data
display results

• All carried out using raster
operations

3: Schizophrenia in Cavan
• In 1996 Pringle, Waddington and
Youssef reported on evidence for the
causes of schizophrenia
• Ecological analysis of variation among
EDs
– Cases varied from 0-11 per ED
– Populations varied from 250-2750

• Poisson probabilities suggested that
one area had an unusually high number
of cases – these can be mapped

Male cases
superimposed on plot
of prevalence by ED

Male cases
superimposed on
rectified Landsat
image with ED
boundaries

4: GMS card holders: Maynooth
•Data on GMS – Medical Card holders
drawn under strict data protection
protocols from DOHC
• Anonymised address lists data matched
with GeoDirectory
•Address matching rate of around 77% in
Maynooth

• Interesting range of results around
average for the whole ED

•ED – single value 14.6 % (national average around 30%)
•Small Area – Range from 2% to 70% - noticeable internal variation and
good indicator of elderly or poor populations

•Strategic value in comparing datasets
•Important in policy analysis

Mapping invites comparison

Role of GIS
• Data integration
– Tally of cases in small areas requires
data on locations of cases and small
area boundaries
– ‘spatial join’ places small area codes
on the case records
– Counting is a standard database
operation

• Data display

Finally
• GIS encourages you to think about
space
• In a ‘conventional’ map of Ireland
areas (EDs, Counties…) are shown in
proportion to their physical size
• There are alternative ways of
displaying maps…

In a cartogram the areas are drawn
proportional to their populations –
this is a “density-equalised” map
projection

A map of the boundaries of counties and
electoral divisions – the areas are drawn
proportional to their physical size

By comparison, the more deprived
western area of Dublin, northern area
of Cork and south east of Limerick are
clearly apparent in the cartogram.

Haase’s deprivation index suggests that the
most deprived populations are in peripheral
rural areas in the north west.

Redrawing the data on the cartogram
reveals wide variation in the SMR, and also
uncovers the urban dimension which is
hidden in the conventional display

Patient data matched to Electoral Divisions –
hospital admissions for psychiatric problems – the
patchwork effect does not suggest any pattern

The cartogram reveals the urban nature
of the problem – those areas which are
more deprived are also those with
higher levels of illness/disability

Residents aged 15+ who are unable to work
through illness or disability: the map pattern
suggests that might be a largely rural
phenomenon

The roads, rail, and urban areas data have
been transformed into the same space as
the cartogram. Dublin and Cork seem
poorly provided with roads…

Other spatial information can be transformed
to the space of the density equalised
projection. Here is the familiar map of main
roads and towns.

Using a cartogram we see that the
incidence of lung cancer is randomly
spatially distributed

Lung cancer in males in New York State [Gastner & Newman: 2004]

Plotting locations of lung cancer cases, we
obtain a map which follows the underlying
population distribution – there appear to be
“clusters”

What about Ireland?
• Potentially hampered by challenge of
geocoding patient records
• Lack of small area population data
– EDs have an average population of 1150
– Wide variation in size (50-28000)
– Socially inhomogenous

Rural addresses
• A major challenge is non-uniqueness
of addresses in rural areas.
• For instance in Moy ED, 11 properties
share the address
‘Lackamore, Lahinch, Co. Clare’

What’s needed
• Postcoding should provide a
mechanism for geocoding patient data
with the granularity required for
planning the delivery of health care –
this has been the case in the UK for a
for long time
• Census data should be made available
for areas with small and similar
populations (cf: UK Output Areas)

Finally…
• GIS has an important role to play
in health studies
• Health data analysis is a rapidly
developing field
• However, in Ireland we are
hampered by data deficiencies –
now is the time for change

Maynooth: image in Google Earth

Central Dublin: image in Google Earth

Taoiseach’s Office: image in Google Earth

Downing Street, London: image in Google Earth

Maynooth: OSi orthophotograph added locally uisng KML

Sellafield: image in Google Earth

NCU – On Line Atlas of Cancer Mortality

The Philippine Institute
for Development
Studies makes an online atlas of socioeconomic indicators
available – contains
some health measures

The future
• Patient empowerment…

• GIS used to merge information from
regulatory agencies on 19196 landfill sites
• Sites buffered to 2km and postcodes
within buffer identified
• Postcoded live births (8.2x106), stillbirths
(43471) and congenital anomalies
(124,597) between 1982 and 1997
• Poisson regression modelling undertaken
• Evidence found for excess risk of
congenital anomaly and low birthweight

• Distance from each
census ward to
nearest hospital
computed
• Map shows number
of patients waiting
longer than 6
months for elective
inpatient care per
available and
unoccupied bed
within 60 minutes
travel time


Slide 39

GIS and Health
Martin Charlton1
Ronan Foley2
Dennis Pringle2
1National

Centre for Geocomputation
2Department of Geography
NUI Maynooth

Outline
• Preliminary observations on GIS
and health
• Examples of GIS in Health:
research and practice
• Mapping people not places

Patterns in Health Data
• Three main areas:
– Mapping of disease incidence
• Relative risk estimation

– Identification of disease clustering
• Surveillance

– Ecological analysis
• Relationship between incidence and
covariates
(Lawson et al, 2003)

Health Data
• Typically patient data is available
in two forms
• Individual – perhaps anonymised
to postcode/zip code level
• Aggregate – counts are available
for a set of zones
• The nature of the data affects the
nature of the analysis

Geographic Information Systems
• GIS is often confused with mapping
• A range of activities which includes
inter alia mapping and analysis
• Will typically involve several datasets
• May also involve several different
pieces of software

Mapping: EpiINFO
• This is a rather simple analysis and
mapping program
• It can be downloaded from the internet
• It doesn’t provide much in the way of
spatial data manipulation…
• … nor are its analytical capabilities very
advanced
• It is easy to use

Spatial Data Manipulation
• GIS isn’t merely concerned with
data display or query
• It’s useful in bringing together
different spatial datasets in the
first stages of an analysis
• This may well require a range of
different geometrical and
database operations

Analysis with Spatial Data
• Statistical analysis usually involves
some separate software.
• Spatial data have some properties
which mean that SPSS isn’t always
appropriate.
• There is now a range of specialist
software – much of it available for
download

GIS may be ideal for data manipulation, display and query,
but it’s not always ideal for analysis – in epidemiology a
variety of analytical software is available … WinBUGS,
GeoBUGS (MRC Biostatistics Unit, Cambridge)

Software to implement
particular methods – for
example scan statistics - can be
downloaded quickly and easily
from the Internet. Kulldorf’s
SaTScan system implements a
range of different scan
statistical models

GIS in Health in Practice
• Facility location – dialysis units
• Facility location – obs & gyn
• Data integration – schizophrenia
in Cavan
• Data manipulation and display –
GMS card holders in Maynooth

1: Provision of Dialysis Units
• There are 5 dialysis units for renal
patients in a region with a population
of about 3,000,000
• Major population centre has two units
about 5 miles apart
• Where would you locate an extra one?
• What would happen if one was
closed?

Location-allocation
• Location-allocation problems deal
with optimum location of facilities
• Attempt to minimise total
weighted travel time/distance
from users to facilities
• What data is needed?

Data requirements
• Digitised road network with classification
of road types
• Mean travel speeds on each type for
urban/non-urban roads
• Extents of urban areas
• Anonymised postcoded patient records
• Postcode->grid reference lookup
• Locations of hospitals in region (both with
and without dialysis units)

GIS processing
• Data integration
• Determine which segments of road network
are urban
• Determine travel times using road type
urban/non-urban speeds
• Tally numbers of patients at each node in
the road network
• Find nearest nodes in network to each
hospital
• Code hospitals as ‘existing’ or ‘potential’
units

Modelling/Analysis
• Carried out using standard GIS
functions
• Convenient ‘front end’ written
using system’s macro language
(save a lot of typing)
• Explore a range of alternatives

Hospital with
dialysis unit

Study region showing hospital locations and urban areas

Patients requiring dialysis (tallied to road nodes)

Classified road network – includes roads outside study area

•

Some alternative
scenarios:

(a) Optimal allocation
of patients
(b) Provide 1 extra
unit
(c) Provide 2 extra
units

(d) Close 1 unit in
city and reallocate
its patients

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

integrate data
process data
model and analyse
generate alternative scenarios
display the results

2: Facility Location
• Two hospitals both provided facilities
for obstetrics, gynaecology, paediatrics
and a special care baby unit.
• Was there a case for rationalisation and
improving the facilities at one hospital
only?
• Would closure disadvantage any
patients?

Data
• Locations of hospitals in study region
• Anonymised postcoded finished
consultant episodes for each of the
four specialities at each hospital
• Classified road network
• Speeds associated with each road
type

Data preparation
• Postcoded records allocated grid
references using Central Postcode
Directory (POSTZON)
• Vector data converted to raster
• Patient/specialty/hospital counts
produced for each raster
• Passage times computed for each
raster cell based on road
classification and speed estimates

Roads, urban areas, patient locations, hospitals in study area

Processing
• Travel time surfaces computed for
each hospital
• Each raster cell contains time
required to reach hospital in minutes
• Assumed walking speed in ‘non road’
areas

Travel time surface for hospital S – darker = shorter time

Travel time surface for hospital T

Time differences
• If we subtract the travel time surfaces areas
nearer one hospital will have positive
values for the difference, and areas nearer
the other will have negative ones
• Boundary between positive and negative
values is boundary of catchment for each
hospital.
• If you’re on the boundary (difference is
zero), it doesn’t matter which hospital you
choose

Roads, patient locations, hospital catchments

Indifference
• If we identify those rasters where the time
difference (ignoring the sign) is, say, 5 –
people in those areas are in the 5 minute
‘indifference’ zone.
• Patients might not be worried about a 5
minute increase in their travel time, they
might be worried about 30 minutes
• Indifference zones can be computed quite
easily

15-minute indifference zone between the two hospitals

Mean access time
• For each cell in the raster we can
compute the product of the number
of patients and the time required to
reach each hospital
• Dividing this by the number of
patients gives a patient weighted
average time for each hospital

Average travel times for the various specialties. There
isn’t much to chose between specialities with the
exception of paediatric episodes.

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

gather data
integrate data
process data
model data
display results

• All carried out using raster
operations

3: Schizophrenia in Cavan
• In 1996 Pringle, Waddington and
Youssef reported on evidence for the
causes of schizophrenia
• Ecological analysis of variation among
EDs
– Cases varied from 0-11 per ED
– Populations varied from 250-2750

• Poisson probabilities suggested that
one area had an unusually high number
of cases – these can be mapped

Male cases
superimposed on plot
of prevalence by ED

Male cases
superimposed on
rectified Landsat
image with ED
boundaries

4: GMS card holders: Maynooth
•Data on GMS – Medical Card holders
drawn under strict data protection
protocols from DOHC
• Anonymised address lists data matched
with GeoDirectory
•Address matching rate of around 77% in
Maynooth

• Interesting range of results around
average for the whole ED

•ED – single value 14.6 % (national average around 30%)
•Small Area – Range from 2% to 70% - noticeable internal variation and
good indicator of elderly or poor populations

•Strategic value in comparing datasets
•Important in policy analysis

Mapping invites comparison

Role of GIS
• Data integration
– Tally of cases in small areas requires
data on locations of cases and small
area boundaries
– ‘spatial join’ places small area codes
on the case records
– Counting is a standard database
operation

• Data display

Finally
• GIS encourages you to think about
space
• In a ‘conventional’ map of Ireland
areas (EDs, Counties…) are shown in
proportion to their physical size
• There are alternative ways of
displaying maps…

In a cartogram the areas are drawn
proportional to their populations –
this is a “density-equalised” map
projection

A map of the boundaries of counties and
electoral divisions – the areas are drawn
proportional to their physical size

By comparison, the more deprived
western area of Dublin, northern area
of Cork and south east of Limerick are
clearly apparent in the cartogram.

Haase’s deprivation index suggests that the
most deprived populations are in peripheral
rural areas in the north west.

Redrawing the data on the cartogram
reveals wide variation in the SMR, and also
uncovers the urban dimension which is
hidden in the conventional display

Patient data matched to Electoral Divisions –
hospital admissions for psychiatric problems – the
patchwork effect does not suggest any pattern

The cartogram reveals the urban nature
of the problem – those areas which are
more deprived are also those with
higher levels of illness/disability

Residents aged 15+ who are unable to work
through illness or disability: the map pattern
suggests that might be a largely rural
phenomenon

The roads, rail, and urban areas data have
been transformed into the same space as
the cartogram. Dublin and Cork seem
poorly provided with roads…

Other spatial information can be transformed
to the space of the density equalised
projection. Here is the familiar map of main
roads and towns.

Using a cartogram we see that the
incidence of lung cancer is randomly
spatially distributed

Lung cancer in males in New York State [Gastner & Newman: 2004]

Plotting locations of lung cancer cases, we
obtain a map which follows the underlying
population distribution – there appear to be
“clusters”

What about Ireland?
• Potentially hampered by challenge of
geocoding patient records
• Lack of small area population data
– EDs have an average population of 1150
– Wide variation in size (50-28000)
– Socially inhomogenous

Rural addresses
• A major challenge is non-uniqueness
of addresses in rural areas.
• For instance in Moy ED, 11 properties
share the address
‘Lackamore, Lahinch, Co. Clare’

What’s needed
• Postcoding should provide a
mechanism for geocoding patient data
with the granularity required for
planning the delivery of health care –
this has been the case in the UK for a
for long time
• Census data should be made available
for areas with small and similar
populations (cf: UK Output Areas)

Finally…
• GIS has an important role to play
in health studies
• Health data analysis is a rapidly
developing field
• However, in Ireland we are
hampered by data deficiencies –
now is the time for change

Maynooth: image in Google Earth

Central Dublin: image in Google Earth

Taoiseach’s Office: image in Google Earth

Downing Street, London: image in Google Earth

Maynooth: OSi orthophotograph added locally uisng KML

Sellafield: image in Google Earth

NCU – On Line Atlas of Cancer Mortality

The Philippine Institute
for Development
Studies makes an online atlas of socioeconomic indicators
available – contains
some health measures

The future
• Patient empowerment…

• GIS used to merge information from
regulatory agencies on 19196 landfill sites
• Sites buffered to 2km and postcodes
within buffer identified
• Postcoded live births (8.2x106), stillbirths
(43471) and congenital anomalies
(124,597) between 1982 and 1997
• Poisson regression modelling undertaken
• Evidence found for excess risk of
congenital anomaly and low birthweight

• Distance from each
census ward to
nearest hospital
computed
• Map shows number
of patients waiting
longer than 6
months for elective
inpatient care per
available and
unoccupied bed
within 60 minutes
travel time


Slide 40

GIS and Health
Martin Charlton1
Ronan Foley2
Dennis Pringle2
1National

Centre for Geocomputation
2Department of Geography
NUI Maynooth

Outline
• Preliminary observations on GIS
and health
• Examples of GIS in Health:
research and practice
• Mapping people not places

Patterns in Health Data
• Three main areas:
– Mapping of disease incidence
• Relative risk estimation

– Identification of disease clustering
• Surveillance

– Ecological analysis
• Relationship between incidence and
covariates
(Lawson et al, 2003)

Health Data
• Typically patient data is available
in two forms
• Individual – perhaps anonymised
to postcode/zip code level
• Aggregate – counts are available
for a set of zones
• The nature of the data affects the
nature of the analysis

Geographic Information Systems
• GIS is often confused with mapping
• A range of activities which includes
inter alia mapping and analysis
• Will typically involve several datasets
• May also involve several different
pieces of software

Mapping: EpiINFO
• This is a rather simple analysis and
mapping program
• It can be downloaded from the internet
• It doesn’t provide much in the way of
spatial data manipulation…
• … nor are its analytical capabilities very
advanced
• It is easy to use

Spatial Data Manipulation
• GIS isn’t merely concerned with
data display or query
• It’s useful in bringing together
different spatial datasets in the
first stages of an analysis
• This may well require a range of
different geometrical and
database operations

Analysis with Spatial Data
• Statistical analysis usually involves
some separate software.
• Spatial data have some properties
which mean that SPSS isn’t always
appropriate.
• There is now a range of specialist
software – much of it available for
download

GIS may be ideal for data manipulation, display and query,
but it’s not always ideal for analysis – in epidemiology a
variety of analytical software is available … WinBUGS,
GeoBUGS (MRC Biostatistics Unit, Cambridge)

Software to implement
particular methods – for
example scan statistics - can be
downloaded quickly and easily
from the Internet. Kulldorf’s
SaTScan system implements a
range of different scan
statistical models

GIS in Health in Practice
• Facility location – dialysis units
• Facility location – obs & gyn
• Data integration – schizophrenia
in Cavan
• Data manipulation and display –
GMS card holders in Maynooth

1: Provision of Dialysis Units
• There are 5 dialysis units for renal
patients in a region with a population
of about 3,000,000
• Major population centre has two units
about 5 miles apart
• Where would you locate an extra one?
• What would happen if one was
closed?

Location-allocation
• Location-allocation problems deal
with optimum location of facilities
• Attempt to minimise total
weighted travel time/distance
from users to facilities
• What data is needed?

Data requirements
• Digitised road network with classification
of road types
• Mean travel speeds on each type for
urban/non-urban roads
• Extents of urban areas
• Anonymised postcoded patient records
• Postcode->grid reference lookup
• Locations of hospitals in region (both with
and without dialysis units)

GIS processing
• Data integration
• Determine which segments of road network
are urban
• Determine travel times using road type
urban/non-urban speeds
• Tally numbers of patients at each node in
the road network
• Find nearest nodes in network to each
hospital
• Code hospitals as ‘existing’ or ‘potential’
units

Modelling/Analysis
• Carried out using standard GIS
functions
• Convenient ‘front end’ written
using system’s macro language
(save a lot of typing)
• Explore a range of alternatives

Hospital with
dialysis unit

Study region showing hospital locations and urban areas

Patients requiring dialysis (tallied to road nodes)

Classified road network – includes roads outside study area

•

Some alternative
scenarios:

(a) Optimal allocation
of patients
(b) Provide 1 extra
unit
(c) Provide 2 extra
units

(d) Close 1 unit in
city and reallocate
its patients

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

integrate data
process data
model and analyse
generate alternative scenarios
display the results

2: Facility Location
• Two hospitals both provided facilities
for obstetrics, gynaecology, paediatrics
and a special care baby unit.
• Was there a case for rationalisation and
improving the facilities at one hospital
only?
• Would closure disadvantage any
patients?

Data
• Locations of hospitals in study region
• Anonymised postcoded finished
consultant episodes for each of the
four specialities at each hospital
• Classified road network
• Speeds associated with each road
type

Data preparation
• Postcoded records allocated grid
references using Central Postcode
Directory (POSTZON)
• Vector data converted to raster
• Patient/specialty/hospital counts
produced for each raster
• Passage times computed for each
raster cell based on road
classification and speed estimates

Roads, urban areas, patient locations, hospitals in study area

Processing
• Travel time surfaces computed for
each hospital
• Each raster cell contains time
required to reach hospital in minutes
• Assumed walking speed in ‘non road’
areas

Travel time surface for hospital S – darker = shorter time

Travel time surface for hospital T

Time differences
• If we subtract the travel time surfaces areas
nearer one hospital will have positive
values for the difference, and areas nearer
the other will have negative ones
• Boundary between positive and negative
values is boundary of catchment for each
hospital.
• If you’re on the boundary (difference is
zero), it doesn’t matter which hospital you
choose

Roads, patient locations, hospital catchments

Indifference
• If we identify those rasters where the time
difference (ignoring the sign) is, say, 5 –
people in those areas are in the 5 minute
‘indifference’ zone.
• Patients might not be worried about a 5
minute increase in their travel time, they
might be worried about 30 minutes
• Indifference zones can be computed quite
easily

15-minute indifference zone between the two hospitals

Mean access time
• For each cell in the raster we can
compute the product of the number
of patients and the time required to
reach each hospital
• Dividing this by the number of
patients gives a patient weighted
average time for each hospital

Average travel times for the various specialties. There
isn’t much to chose between specialities with the
exception of paediatric episodes.

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

gather data
integrate data
process data
model data
display results

• All carried out using raster
operations

3: Schizophrenia in Cavan
• In 1996 Pringle, Waddington and
Youssef reported on evidence for the
causes of schizophrenia
• Ecological analysis of variation among
EDs
– Cases varied from 0-11 per ED
– Populations varied from 250-2750

• Poisson probabilities suggested that
one area had an unusually high number
of cases – these can be mapped

Male cases
superimposed on plot
of prevalence by ED

Male cases
superimposed on
rectified Landsat
image with ED
boundaries

4: GMS card holders: Maynooth
•Data on GMS – Medical Card holders
drawn under strict data protection
protocols from DOHC
• Anonymised address lists data matched
with GeoDirectory
•Address matching rate of around 77% in
Maynooth

• Interesting range of results around
average for the whole ED

•ED – single value 14.6 % (national average around 30%)
•Small Area – Range from 2% to 70% - noticeable internal variation and
good indicator of elderly or poor populations

•Strategic value in comparing datasets
•Important in policy analysis

Mapping invites comparison

Role of GIS
• Data integration
– Tally of cases in small areas requires
data on locations of cases and small
area boundaries
– ‘spatial join’ places small area codes
on the case records
– Counting is a standard database
operation

• Data display

Finally
• GIS encourages you to think about
space
• In a ‘conventional’ map of Ireland
areas (EDs, Counties…) are shown in
proportion to their physical size
• There are alternative ways of
displaying maps…

In a cartogram the areas are drawn
proportional to their populations –
this is a “density-equalised” map
projection

A map of the boundaries of counties and
electoral divisions – the areas are drawn
proportional to their physical size

By comparison, the more deprived
western area of Dublin, northern area
of Cork and south east of Limerick are
clearly apparent in the cartogram.

Haase’s deprivation index suggests that the
most deprived populations are in peripheral
rural areas in the north west.

Redrawing the data on the cartogram
reveals wide variation in the SMR, and also
uncovers the urban dimension which is
hidden in the conventional display

Patient data matched to Electoral Divisions –
hospital admissions for psychiatric problems – the
patchwork effect does not suggest any pattern

The cartogram reveals the urban nature
of the problem – those areas which are
more deprived are also those with
higher levels of illness/disability

Residents aged 15+ who are unable to work
through illness or disability: the map pattern
suggests that might be a largely rural
phenomenon

The roads, rail, and urban areas data have
been transformed into the same space as
the cartogram. Dublin and Cork seem
poorly provided with roads…

Other spatial information can be transformed
to the space of the density equalised
projection. Here is the familiar map of main
roads and towns.

Using a cartogram we see that the
incidence of lung cancer is randomly
spatially distributed

Lung cancer in males in New York State [Gastner & Newman: 2004]

Plotting locations of lung cancer cases, we
obtain a map which follows the underlying
population distribution – there appear to be
“clusters”

What about Ireland?
• Potentially hampered by challenge of
geocoding patient records
• Lack of small area population data
– EDs have an average population of 1150
– Wide variation in size (50-28000)
– Socially inhomogenous

Rural addresses
• A major challenge is non-uniqueness
of addresses in rural areas.
• For instance in Moy ED, 11 properties
share the address
‘Lackamore, Lahinch, Co. Clare’

What’s needed
• Postcoding should provide a
mechanism for geocoding patient data
with the granularity required for
planning the delivery of health care –
this has been the case in the UK for a
for long time
• Census data should be made available
for areas with small and similar
populations (cf: UK Output Areas)

Finally…
• GIS has an important role to play
in health studies
• Health data analysis is a rapidly
developing field
• However, in Ireland we are
hampered by data deficiencies –
now is the time for change

Maynooth: image in Google Earth

Central Dublin: image in Google Earth

Taoiseach’s Office: image in Google Earth

Downing Street, London: image in Google Earth

Maynooth: OSi orthophotograph added locally uisng KML

Sellafield: image in Google Earth

NCU – On Line Atlas of Cancer Mortality

The Philippine Institute
for Development
Studies makes an online atlas of socioeconomic indicators
available – contains
some health measures

The future
• Patient empowerment…

• GIS used to merge information from
regulatory agencies on 19196 landfill sites
• Sites buffered to 2km and postcodes
within buffer identified
• Postcoded live births (8.2x106), stillbirths
(43471) and congenital anomalies
(124,597) between 1982 and 1997
• Poisson regression modelling undertaken
• Evidence found for excess risk of
congenital anomaly and low birthweight

• Distance from each
census ward to
nearest hospital
computed
• Map shows number
of patients waiting
longer than 6
months for elective
inpatient care per
available and
unoccupied bed
within 60 minutes
travel time


Slide 41

GIS and Health
Martin Charlton1
Ronan Foley2
Dennis Pringle2
1National

Centre for Geocomputation
2Department of Geography
NUI Maynooth

Outline
• Preliminary observations on GIS
and health
• Examples of GIS in Health:
research and practice
• Mapping people not places

Patterns in Health Data
• Three main areas:
– Mapping of disease incidence
• Relative risk estimation

– Identification of disease clustering
• Surveillance

– Ecological analysis
• Relationship between incidence and
covariates
(Lawson et al, 2003)

Health Data
• Typically patient data is available
in two forms
• Individual – perhaps anonymised
to postcode/zip code level
• Aggregate – counts are available
for a set of zones
• The nature of the data affects the
nature of the analysis

Geographic Information Systems
• GIS is often confused with mapping
• A range of activities which includes
inter alia mapping and analysis
• Will typically involve several datasets
• May also involve several different
pieces of software

Mapping: EpiINFO
• This is a rather simple analysis and
mapping program
• It can be downloaded from the internet
• It doesn’t provide much in the way of
spatial data manipulation…
• … nor are its analytical capabilities very
advanced
• It is easy to use

Spatial Data Manipulation
• GIS isn’t merely concerned with
data display or query
• It’s useful in bringing together
different spatial datasets in the
first stages of an analysis
• This may well require a range of
different geometrical and
database operations

Analysis with Spatial Data
• Statistical analysis usually involves
some separate software.
• Spatial data have some properties
which mean that SPSS isn’t always
appropriate.
• There is now a range of specialist
software – much of it available for
download

GIS may be ideal for data manipulation, display and query,
but it’s not always ideal for analysis – in epidemiology a
variety of analytical software is available … WinBUGS,
GeoBUGS (MRC Biostatistics Unit, Cambridge)

Software to implement
particular methods – for
example scan statistics - can be
downloaded quickly and easily
from the Internet. Kulldorf’s
SaTScan system implements a
range of different scan
statistical models

GIS in Health in Practice
• Facility location – dialysis units
• Facility location – obs & gyn
• Data integration – schizophrenia
in Cavan
• Data manipulation and display –
GMS card holders in Maynooth

1: Provision of Dialysis Units
• There are 5 dialysis units for renal
patients in a region with a population
of about 3,000,000
• Major population centre has two units
about 5 miles apart
• Where would you locate an extra one?
• What would happen if one was
closed?

Location-allocation
• Location-allocation problems deal
with optimum location of facilities
• Attempt to minimise total
weighted travel time/distance
from users to facilities
• What data is needed?

Data requirements
• Digitised road network with classification
of road types
• Mean travel speeds on each type for
urban/non-urban roads
• Extents of urban areas
• Anonymised postcoded patient records
• Postcode->grid reference lookup
• Locations of hospitals in region (both with
and without dialysis units)

GIS processing
• Data integration
• Determine which segments of road network
are urban
• Determine travel times using road type
urban/non-urban speeds
• Tally numbers of patients at each node in
the road network
• Find nearest nodes in network to each
hospital
• Code hospitals as ‘existing’ or ‘potential’
units

Modelling/Analysis
• Carried out using standard GIS
functions
• Convenient ‘front end’ written
using system’s macro language
(save a lot of typing)
• Explore a range of alternatives

Hospital with
dialysis unit

Study region showing hospital locations and urban areas

Patients requiring dialysis (tallied to road nodes)

Classified road network – includes roads outside study area

•

Some alternative
scenarios:

(a) Optimal allocation
of patients
(b) Provide 1 extra
unit
(c) Provide 2 extra
units

(d) Close 1 unit in
city and reallocate
its patients

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

integrate data
process data
model and analyse
generate alternative scenarios
display the results

2: Facility Location
• Two hospitals both provided facilities
for obstetrics, gynaecology, paediatrics
and a special care baby unit.
• Was there a case for rationalisation and
improving the facilities at one hospital
only?
• Would closure disadvantage any
patients?

Data
• Locations of hospitals in study region
• Anonymised postcoded finished
consultant episodes for each of the
four specialities at each hospital
• Classified road network
• Speeds associated with each road
type

Data preparation
• Postcoded records allocated grid
references using Central Postcode
Directory (POSTZON)
• Vector data converted to raster
• Patient/specialty/hospital counts
produced for each raster
• Passage times computed for each
raster cell based on road
classification and speed estimates

Roads, urban areas, patient locations, hospitals in study area

Processing
• Travel time surfaces computed for
each hospital
• Each raster cell contains time
required to reach hospital in minutes
• Assumed walking speed in ‘non road’
areas

Travel time surface for hospital S – darker = shorter time

Travel time surface for hospital T

Time differences
• If we subtract the travel time surfaces areas
nearer one hospital will have positive
values for the difference, and areas nearer
the other will have negative ones
• Boundary between positive and negative
values is boundary of catchment for each
hospital.
• If you’re on the boundary (difference is
zero), it doesn’t matter which hospital you
choose

Roads, patient locations, hospital catchments

Indifference
• If we identify those rasters where the time
difference (ignoring the sign) is, say, 5 –
people in those areas are in the 5 minute
‘indifference’ zone.
• Patients might not be worried about a 5
minute increase in their travel time, they
might be worried about 30 minutes
• Indifference zones can be computed quite
easily

15-minute indifference zone between the two hospitals

Mean access time
• For each cell in the raster we can
compute the product of the number
of patients and the time required to
reach each hospital
• Dividing this by the number of
patients gives a patient weighted
average time for each hospital

Average travel times for the various specialties. There
isn’t much to chose between specialities with the
exception of paediatric episodes.

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

gather data
integrate data
process data
model data
display results

• All carried out using raster
operations

3: Schizophrenia in Cavan
• In 1996 Pringle, Waddington and
Youssef reported on evidence for the
causes of schizophrenia
• Ecological analysis of variation among
EDs
– Cases varied from 0-11 per ED
– Populations varied from 250-2750

• Poisson probabilities suggested that
one area had an unusually high number
of cases – these can be mapped

Male cases
superimposed on plot
of prevalence by ED

Male cases
superimposed on
rectified Landsat
image with ED
boundaries

4: GMS card holders: Maynooth
•Data on GMS – Medical Card holders
drawn under strict data protection
protocols from DOHC
• Anonymised address lists data matched
with GeoDirectory
•Address matching rate of around 77% in
Maynooth

• Interesting range of results around
average for the whole ED

•ED – single value 14.6 % (national average around 30%)
•Small Area – Range from 2% to 70% - noticeable internal variation and
good indicator of elderly or poor populations

•Strategic value in comparing datasets
•Important in policy analysis

Mapping invites comparison

Role of GIS
• Data integration
– Tally of cases in small areas requires
data on locations of cases and small
area boundaries
– ‘spatial join’ places small area codes
on the case records
– Counting is a standard database
operation

• Data display

Finally
• GIS encourages you to think about
space
• In a ‘conventional’ map of Ireland
areas (EDs, Counties…) are shown in
proportion to their physical size
• There are alternative ways of
displaying maps…

In a cartogram the areas are drawn
proportional to their populations –
this is a “density-equalised” map
projection

A map of the boundaries of counties and
electoral divisions – the areas are drawn
proportional to their physical size

By comparison, the more deprived
western area of Dublin, northern area
of Cork and south east of Limerick are
clearly apparent in the cartogram.

Haase’s deprivation index suggests that the
most deprived populations are in peripheral
rural areas in the north west.

Redrawing the data on the cartogram
reveals wide variation in the SMR, and also
uncovers the urban dimension which is
hidden in the conventional display

Patient data matched to Electoral Divisions –
hospital admissions for psychiatric problems – the
patchwork effect does not suggest any pattern

The cartogram reveals the urban nature
of the problem – those areas which are
more deprived are also those with
higher levels of illness/disability

Residents aged 15+ who are unable to work
through illness or disability: the map pattern
suggests that might be a largely rural
phenomenon

The roads, rail, and urban areas data have
been transformed into the same space as
the cartogram. Dublin and Cork seem
poorly provided with roads…

Other spatial information can be transformed
to the space of the density equalised
projection. Here is the familiar map of main
roads and towns.

Using a cartogram we see that the
incidence of lung cancer is randomly
spatially distributed

Lung cancer in males in New York State [Gastner & Newman: 2004]

Plotting locations of lung cancer cases, we
obtain a map which follows the underlying
population distribution – there appear to be
“clusters”

What about Ireland?
• Potentially hampered by challenge of
geocoding patient records
• Lack of small area population data
– EDs have an average population of 1150
– Wide variation in size (50-28000)
– Socially inhomogenous

Rural addresses
• A major challenge is non-uniqueness
of addresses in rural areas.
• For instance in Moy ED, 11 properties
share the address
‘Lackamore, Lahinch, Co. Clare’

What’s needed
• Postcoding should provide a
mechanism for geocoding patient data
with the granularity required for
planning the delivery of health care –
this has been the case in the UK for a
for long time
• Census data should be made available
for areas with small and similar
populations (cf: UK Output Areas)

Finally…
• GIS has an important role to play
in health studies
• Health data analysis is a rapidly
developing field
• However, in Ireland we are
hampered by data deficiencies –
now is the time for change

Maynooth: image in Google Earth

Central Dublin: image in Google Earth

Taoiseach’s Office: image in Google Earth

Downing Street, London: image in Google Earth

Maynooth: OSi orthophotograph added locally uisng KML

Sellafield: image in Google Earth

NCU – On Line Atlas of Cancer Mortality

The Philippine Institute
for Development
Studies makes an online atlas of socioeconomic indicators
available – contains
some health measures

The future
• Patient empowerment…

• GIS used to merge information from
regulatory agencies on 19196 landfill sites
• Sites buffered to 2km and postcodes
within buffer identified
• Postcoded live births (8.2x106), stillbirths
(43471) and congenital anomalies
(124,597) between 1982 and 1997
• Poisson regression modelling undertaken
• Evidence found for excess risk of
congenital anomaly and low birthweight

• Distance from each
census ward to
nearest hospital
computed
• Map shows number
of patients waiting
longer than 6
months for elective
inpatient care per
available and
unoccupied bed
within 60 minutes
travel time


Slide 42

GIS and Health
Martin Charlton1
Ronan Foley2
Dennis Pringle2
1National

Centre for Geocomputation
2Department of Geography
NUI Maynooth

Outline
• Preliminary observations on GIS
and health
• Examples of GIS in Health:
research and practice
• Mapping people not places

Patterns in Health Data
• Three main areas:
– Mapping of disease incidence
• Relative risk estimation

– Identification of disease clustering
• Surveillance

– Ecological analysis
• Relationship between incidence and
covariates
(Lawson et al, 2003)

Health Data
• Typically patient data is available
in two forms
• Individual – perhaps anonymised
to postcode/zip code level
• Aggregate – counts are available
for a set of zones
• The nature of the data affects the
nature of the analysis

Geographic Information Systems
• GIS is often confused with mapping
• A range of activities which includes
inter alia mapping and analysis
• Will typically involve several datasets
• May also involve several different
pieces of software

Mapping: EpiINFO
• This is a rather simple analysis and
mapping program
• It can be downloaded from the internet
• It doesn’t provide much in the way of
spatial data manipulation…
• … nor are its analytical capabilities very
advanced
• It is easy to use

Spatial Data Manipulation
• GIS isn’t merely concerned with
data display or query
• It’s useful in bringing together
different spatial datasets in the
first stages of an analysis
• This may well require a range of
different geometrical and
database operations

Analysis with Spatial Data
• Statistical analysis usually involves
some separate software.
• Spatial data have some properties
which mean that SPSS isn’t always
appropriate.
• There is now a range of specialist
software – much of it available for
download

GIS may be ideal for data manipulation, display and query,
but it’s not always ideal for analysis – in epidemiology a
variety of analytical software is available … WinBUGS,
GeoBUGS (MRC Biostatistics Unit, Cambridge)

Software to implement
particular methods – for
example scan statistics - can be
downloaded quickly and easily
from the Internet. Kulldorf’s
SaTScan system implements a
range of different scan
statistical models

GIS in Health in Practice
• Facility location – dialysis units
• Facility location – obs & gyn
• Data integration – schizophrenia
in Cavan
• Data manipulation and display –
GMS card holders in Maynooth

1: Provision of Dialysis Units
• There are 5 dialysis units for renal
patients in a region with a population
of about 3,000,000
• Major population centre has two units
about 5 miles apart
• Where would you locate an extra one?
• What would happen if one was
closed?

Location-allocation
• Location-allocation problems deal
with optimum location of facilities
• Attempt to minimise total
weighted travel time/distance
from users to facilities
• What data is needed?

Data requirements
• Digitised road network with classification
of road types
• Mean travel speeds on each type for
urban/non-urban roads
• Extents of urban areas
• Anonymised postcoded patient records
• Postcode->grid reference lookup
• Locations of hospitals in region (both with
and without dialysis units)

GIS processing
• Data integration
• Determine which segments of road network
are urban
• Determine travel times using road type
urban/non-urban speeds
• Tally numbers of patients at each node in
the road network
• Find nearest nodes in network to each
hospital
• Code hospitals as ‘existing’ or ‘potential’
units

Modelling/Analysis
• Carried out using standard GIS
functions
• Convenient ‘front end’ written
using system’s macro language
(save a lot of typing)
• Explore a range of alternatives

Hospital with
dialysis unit

Study region showing hospital locations and urban areas

Patients requiring dialysis (tallied to road nodes)

Classified road network – includes roads outside study area

•

Some alternative
scenarios:

(a) Optimal allocation
of patients
(b) Provide 1 extra
unit
(c) Provide 2 extra
units

(d) Close 1 unit in
city and reallocate
its patients

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

integrate data
process data
model and analyse
generate alternative scenarios
display the results

2: Facility Location
• Two hospitals both provided facilities
for obstetrics, gynaecology, paediatrics
and a special care baby unit.
• Was there a case for rationalisation and
improving the facilities at one hospital
only?
• Would closure disadvantage any
patients?

Data
• Locations of hospitals in study region
• Anonymised postcoded finished
consultant episodes for each of the
four specialities at each hospital
• Classified road network
• Speeds associated with each road
type

Data preparation
• Postcoded records allocated grid
references using Central Postcode
Directory (POSTZON)
• Vector data converted to raster
• Patient/specialty/hospital counts
produced for each raster
• Passage times computed for each
raster cell based on road
classification and speed estimates

Roads, urban areas, patient locations, hospitals in study area

Processing
• Travel time surfaces computed for
each hospital
• Each raster cell contains time
required to reach hospital in minutes
• Assumed walking speed in ‘non road’
areas

Travel time surface for hospital S – darker = shorter time

Travel time surface for hospital T

Time differences
• If we subtract the travel time surfaces areas
nearer one hospital will have positive
values for the difference, and areas nearer
the other will have negative ones
• Boundary between positive and negative
values is boundary of catchment for each
hospital.
• If you’re on the boundary (difference is
zero), it doesn’t matter which hospital you
choose

Roads, patient locations, hospital catchments

Indifference
• If we identify those rasters where the time
difference (ignoring the sign) is, say, 5 –
people in those areas are in the 5 minute
‘indifference’ zone.
• Patients might not be worried about a 5
minute increase in their travel time, they
might be worried about 30 minutes
• Indifference zones can be computed quite
easily

15-minute indifference zone between the two hospitals

Mean access time
• For each cell in the raster we can
compute the product of the number
of patients and the time required to
reach each hospital
• Dividing this by the number of
patients gives a patient weighted
average time for each hospital

Average travel times for the various specialties. There
isn’t much to chose between specialities with the
exception of paediatric episodes.

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

gather data
integrate data
process data
model data
display results

• All carried out using raster
operations

3: Schizophrenia in Cavan
• In 1996 Pringle, Waddington and
Youssef reported on evidence for the
causes of schizophrenia
• Ecological analysis of variation among
EDs
– Cases varied from 0-11 per ED
– Populations varied from 250-2750

• Poisson probabilities suggested that
one area had an unusually high number
of cases – these can be mapped

Male cases
superimposed on plot
of prevalence by ED

Male cases
superimposed on
rectified Landsat
image with ED
boundaries

4: GMS card holders: Maynooth
•Data on GMS – Medical Card holders
drawn under strict data protection
protocols from DOHC
• Anonymised address lists data matched
with GeoDirectory
•Address matching rate of around 77% in
Maynooth

• Interesting range of results around
average for the whole ED

•ED – single value 14.6 % (national average around 30%)
•Small Area – Range from 2% to 70% - noticeable internal variation and
good indicator of elderly or poor populations

•Strategic value in comparing datasets
•Important in policy analysis

Mapping invites comparison

Role of GIS
• Data integration
– Tally of cases in small areas requires
data on locations of cases and small
area boundaries
– ‘spatial join’ places small area codes
on the case records
– Counting is a standard database
operation

• Data display

Finally
• GIS encourages you to think about
space
• In a ‘conventional’ map of Ireland
areas (EDs, Counties…) are shown in
proportion to their physical size
• There are alternative ways of
displaying maps…

In a cartogram the areas are drawn
proportional to their populations –
this is a “density-equalised” map
projection

A map of the boundaries of counties and
electoral divisions – the areas are drawn
proportional to their physical size

By comparison, the more deprived
western area of Dublin, northern area
of Cork and south east of Limerick are
clearly apparent in the cartogram.

Haase’s deprivation index suggests that the
most deprived populations are in peripheral
rural areas in the north west.

Redrawing the data on the cartogram
reveals wide variation in the SMR, and also
uncovers the urban dimension which is
hidden in the conventional display

Patient data matched to Electoral Divisions –
hospital admissions for psychiatric problems – the
patchwork effect does not suggest any pattern

The cartogram reveals the urban nature
of the problem – those areas which are
more deprived are also those with
higher levels of illness/disability

Residents aged 15+ who are unable to work
through illness or disability: the map pattern
suggests that might be a largely rural
phenomenon

The roads, rail, and urban areas data have
been transformed into the same space as
the cartogram. Dublin and Cork seem
poorly provided with roads…

Other spatial information can be transformed
to the space of the density equalised
projection. Here is the familiar map of main
roads and towns.

Using a cartogram we see that the
incidence of lung cancer is randomly
spatially distributed

Lung cancer in males in New York State [Gastner & Newman: 2004]

Plotting locations of lung cancer cases, we
obtain a map which follows the underlying
population distribution – there appear to be
“clusters”

What about Ireland?
• Potentially hampered by challenge of
geocoding patient records
• Lack of small area population data
– EDs have an average population of 1150
– Wide variation in size (50-28000)
– Socially inhomogenous

Rural addresses
• A major challenge is non-uniqueness
of addresses in rural areas.
• For instance in Moy ED, 11 properties
share the address
‘Lackamore, Lahinch, Co. Clare’

What’s needed
• Postcoding should provide a
mechanism for geocoding patient data
with the granularity required for
planning the delivery of health care –
this has been the case in the UK for a
for long time
• Census data should be made available
for areas with small and similar
populations (cf: UK Output Areas)

Finally…
• GIS has an important role to play
in health studies
• Health data analysis is a rapidly
developing field
• However, in Ireland we are
hampered by data deficiencies –
now is the time for change

Maynooth: image in Google Earth

Central Dublin: image in Google Earth

Taoiseach’s Office: image in Google Earth

Downing Street, London: image in Google Earth

Maynooth: OSi orthophotograph added locally uisng KML

Sellafield: image in Google Earth

NCU – On Line Atlas of Cancer Mortality

The Philippine Institute
for Development
Studies makes an online atlas of socioeconomic indicators
available – contains
some health measures

The future
• Patient empowerment…

• GIS used to merge information from
regulatory agencies on 19196 landfill sites
• Sites buffered to 2km and postcodes
within buffer identified
• Postcoded live births (8.2x106), stillbirths
(43471) and congenital anomalies
(124,597) between 1982 and 1997
• Poisson regression modelling undertaken
• Evidence found for excess risk of
congenital anomaly and low birthweight

• Distance from each
census ward to
nearest hospital
computed
• Map shows number
of patients waiting
longer than 6
months for elective
inpatient care per
available and
unoccupied bed
within 60 minutes
travel time


Slide 43

GIS and Health
Martin Charlton1
Ronan Foley2
Dennis Pringle2
1National

Centre for Geocomputation
2Department of Geography
NUI Maynooth

Outline
• Preliminary observations on GIS
and health
• Examples of GIS in Health:
research and practice
• Mapping people not places

Patterns in Health Data
• Three main areas:
– Mapping of disease incidence
• Relative risk estimation

– Identification of disease clustering
• Surveillance

– Ecological analysis
• Relationship between incidence and
covariates
(Lawson et al, 2003)

Health Data
• Typically patient data is available
in two forms
• Individual – perhaps anonymised
to postcode/zip code level
• Aggregate – counts are available
for a set of zones
• The nature of the data affects the
nature of the analysis

Geographic Information Systems
• GIS is often confused with mapping
• A range of activities which includes
inter alia mapping and analysis
• Will typically involve several datasets
• May also involve several different
pieces of software

Mapping: EpiINFO
• This is a rather simple analysis and
mapping program
• It can be downloaded from the internet
• It doesn’t provide much in the way of
spatial data manipulation…
• … nor are its analytical capabilities very
advanced
• It is easy to use

Spatial Data Manipulation
• GIS isn’t merely concerned with
data display or query
• It’s useful in bringing together
different spatial datasets in the
first stages of an analysis
• This may well require a range of
different geometrical and
database operations

Analysis with Spatial Data
• Statistical analysis usually involves
some separate software.
• Spatial data have some properties
which mean that SPSS isn’t always
appropriate.
• There is now a range of specialist
software – much of it available for
download

GIS may be ideal for data manipulation, display and query,
but it’s not always ideal for analysis – in epidemiology a
variety of analytical software is available … WinBUGS,
GeoBUGS (MRC Biostatistics Unit, Cambridge)

Software to implement
particular methods – for
example scan statistics - can be
downloaded quickly and easily
from the Internet. Kulldorf’s
SaTScan system implements a
range of different scan
statistical models

GIS in Health in Practice
• Facility location – dialysis units
• Facility location – obs & gyn
• Data integration – schizophrenia
in Cavan
• Data manipulation and display –
GMS card holders in Maynooth

1: Provision of Dialysis Units
• There are 5 dialysis units for renal
patients in a region with a population
of about 3,000,000
• Major population centre has two units
about 5 miles apart
• Where would you locate an extra one?
• What would happen if one was
closed?

Location-allocation
• Location-allocation problems deal
with optimum location of facilities
• Attempt to minimise total
weighted travel time/distance
from users to facilities
• What data is needed?

Data requirements
• Digitised road network with classification
of road types
• Mean travel speeds on each type for
urban/non-urban roads
• Extents of urban areas
• Anonymised postcoded patient records
• Postcode->grid reference lookup
• Locations of hospitals in region (both with
and without dialysis units)

GIS processing
• Data integration
• Determine which segments of road network
are urban
• Determine travel times using road type
urban/non-urban speeds
• Tally numbers of patients at each node in
the road network
• Find nearest nodes in network to each
hospital
• Code hospitals as ‘existing’ or ‘potential’
units

Modelling/Analysis
• Carried out using standard GIS
functions
• Convenient ‘front end’ written
using system’s macro language
(save a lot of typing)
• Explore a range of alternatives

Hospital with
dialysis unit

Study region showing hospital locations and urban areas

Patients requiring dialysis (tallied to road nodes)

Classified road network – includes roads outside study area

•

Some alternative
scenarios:

(a) Optimal allocation
of patients
(b) Provide 1 extra
unit
(c) Provide 2 extra
units

(d) Close 1 unit in
city and reallocate
its patients

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

integrate data
process data
model and analyse
generate alternative scenarios
display the results

2: Facility Location
• Two hospitals both provided facilities
for obstetrics, gynaecology, paediatrics
and a special care baby unit.
• Was there a case for rationalisation and
improving the facilities at one hospital
only?
• Would closure disadvantage any
patients?

Data
• Locations of hospitals in study region
• Anonymised postcoded finished
consultant episodes for each of the
four specialities at each hospital
• Classified road network
• Speeds associated with each road
type

Data preparation
• Postcoded records allocated grid
references using Central Postcode
Directory (POSTZON)
• Vector data converted to raster
• Patient/specialty/hospital counts
produced for each raster
• Passage times computed for each
raster cell based on road
classification and speed estimates

Roads, urban areas, patient locations, hospitals in study area

Processing
• Travel time surfaces computed for
each hospital
• Each raster cell contains time
required to reach hospital in minutes
• Assumed walking speed in ‘non road’
areas

Travel time surface for hospital S – darker = shorter time

Travel time surface for hospital T

Time differences
• If we subtract the travel time surfaces areas
nearer one hospital will have positive
values for the difference, and areas nearer
the other will have negative ones
• Boundary between positive and negative
values is boundary of catchment for each
hospital.
• If you’re on the boundary (difference is
zero), it doesn’t matter which hospital you
choose

Roads, patient locations, hospital catchments

Indifference
• If we identify those rasters where the time
difference (ignoring the sign) is, say, 5 –
people in those areas are in the 5 minute
‘indifference’ zone.
• Patients might not be worried about a 5
minute increase in their travel time, they
might be worried about 30 minutes
• Indifference zones can be computed quite
easily

15-minute indifference zone between the two hospitals

Mean access time
• For each cell in the raster we can
compute the product of the number
of patients and the time required to
reach each hospital
• Dividing this by the number of
patients gives a patient weighted
average time for each hospital

Average travel times for the various specialties. There
isn’t much to chose between specialities with the
exception of paediatric episodes.

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

gather data
integrate data
process data
model data
display results

• All carried out using raster
operations

3: Schizophrenia in Cavan
• In 1996 Pringle, Waddington and
Youssef reported on evidence for the
causes of schizophrenia
• Ecological analysis of variation among
EDs
– Cases varied from 0-11 per ED
– Populations varied from 250-2750

• Poisson probabilities suggested that
one area had an unusually high number
of cases – these can be mapped

Male cases
superimposed on plot
of prevalence by ED

Male cases
superimposed on
rectified Landsat
image with ED
boundaries

4: GMS card holders: Maynooth
•Data on GMS – Medical Card holders
drawn under strict data protection
protocols from DOHC
• Anonymised address lists data matched
with GeoDirectory
•Address matching rate of around 77% in
Maynooth

• Interesting range of results around
average for the whole ED

•ED – single value 14.6 % (national average around 30%)
•Small Area – Range from 2% to 70% - noticeable internal variation and
good indicator of elderly or poor populations

•Strategic value in comparing datasets
•Important in policy analysis

Mapping invites comparison

Role of GIS
• Data integration
– Tally of cases in small areas requires
data on locations of cases and small
area boundaries
– ‘spatial join’ places small area codes
on the case records
– Counting is a standard database
operation

• Data display

Finally
• GIS encourages you to think about
space
• In a ‘conventional’ map of Ireland
areas (EDs, Counties…) are shown in
proportion to their physical size
• There are alternative ways of
displaying maps…

In a cartogram the areas are drawn
proportional to their populations –
this is a “density-equalised” map
projection

A map of the boundaries of counties and
electoral divisions – the areas are drawn
proportional to their physical size

By comparison, the more deprived
western area of Dublin, northern area
of Cork and south east of Limerick are
clearly apparent in the cartogram.

Haase’s deprivation index suggests that the
most deprived populations are in peripheral
rural areas in the north west.

Redrawing the data on the cartogram
reveals wide variation in the SMR, and also
uncovers the urban dimension which is
hidden in the conventional display

Patient data matched to Electoral Divisions –
hospital admissions for psychiatric problems – the
patchwork effect does not suggest any pattern

The cartogram reveals the urban nature
of the problem – those areas which are
more deprived are also those with
higher levels of illness/disability

Residents aged 15+ who are unable to work
through illness or disability: the map pattern
suggests that might be a largely rural
phenomenon

The roads, rail, and urban areas data have
been transformed into the same space as
the cartogram. Dublin and Cork seem
poorly provided with roads…

Other spatial information can be transformed
to the space of the density equalised
projection. Here is the familiar map of main
roads and towns.

Using a cartogram we see that the
incidence of lung cancer is randomly
spatially distributed

Lung cancer in males in New York State [Gastner & Newman: 2004]

Plotting locations of lung cancer cases, we
obtain a map which follows the underlying
population distribution – there appear to be
“clusters”

What about Ireland?
• Potentially hampered by challenge of
geocoding patient records
• Lack of small area population data
– EDs have an average population of 1150
– Wide variation in size (50-28000)
– Socially inhomogenous

Rural addresses
• A major challenge is non-uniqueness
of addresses in rural areas.
• For instance in Moy ED, 11 properties
share the address
‘Lackamore, Lahinch, Co. Clare’

What’s needed
• Postcoding should provide a
mechanism for geocoding patient data
with the granularity required for
planning the delivery of health care –
this has been the case in the UK for a
for long time
• Census data should be made available
for areas with small and similar
populations (cf: UK Output Areas)

Finally…
• GIS has an important role to play
in health studies
• Health data analysis is a rapidly
developing field
• However, in Ireland we are
hampered by data deficiencies –
now is the time for change

Maynooth: image in Google Earth

Central Dublin: image in Google Earth

Taoiseach’s Office: image in Google Earth

Downing Street, London: image in Google Earth

Maynooth: OSi orthophotograph added locally uisng KML

Sellafield: image in Google Earth

NCU – On Line Atlas of Cancer Mortality

The Philippine Institute
for Development
Studies makes an online atlas of socioeconomic indicators
available – contains
some health measures

The future
• Patient empowerment…

• GIS used to merge information from
regulatory agencies on 19196 landfill sites
• Sites buffered to 2km and postcodes
within buffer identified
• Postcoded live births (8.2x106), stillbirths
(43471) and congenital anomalies
(124,597) between 1982 and 1997
• Poisson regression modelling undertaken
• Evidence found for excess risk of
congenital anomaly and low birthweight

• Distance from each
census ward to
nearest hospital
computed
• Map shows number
of patients waiting
longer than 6
months for elective
inpatient care per
available and
unoccupied bed
within 60 minutes
travel time


Slide 44

GIS and Health
Martin Charlton1
Ronan Foley2
Dennis Pringle2
1National

Centre for Geocomputation
2Department of Geography
NUI Maynooth

Outline
• Preliminary observations on GIS
and health
• Examples of GIS in Health:
research and practice
• Mapping people not places

Patterns in Health Data
• Three main areas:
– Mapping of disease incidence
• Relative risk estimation

– Identification of disease clustering
• Surveillance

– Ecological analysis
• Relationship between incidence and
covariates
(Lawson et al, 2003)

Health Data
• Typically patient data is available
in two forms
• Individual – perhaps anonymised
to postcode/zip code level
• Aggregate – counts are available
for a set of zones
• The nature of the data affects the
nature of the analysis

Geographic Information Systems
• GIS is often confused with mapping
• A range of activities which includes
inter alia mapping and analysis
• Will typically involve several datasets
• May also involve several different
pieces of software

Mapping: EpiINFO
• This is a rather simple analysis and
mapping program
• It can be downloaded from the internet
• It doesn’t provide much in the way of
spatial data manipulation…
• … nor are its analytical capabilities very
advanced
• It is easy to use

Spatial Data Manipulation
• GIS isn’t merely concerned with
data display or query
• It’s useful in bringing together
different spatial datasets in the
first stages of an analysis
• This may well require a range of
different geometrical and
database operations

Analysis with Spatial Data
• Statistical analysis usually involves
some separate software.
• Spatial data have some properties
which mean that SPSS isn’t always
appropriate.
• There is now a range of specialist
software – much of it available for
download

GIS may be ideal for data manipulation, display and query,
but it’s not always ideal for analysis – in epidemiology a
variety of analytical software is available … WinBUGS,
GeoBUGS (MRC Biostatistics Unit, Cambridge)

Software to implement
particular methods – for
example scan statistics - can be
downloaded quickly and easily
from the Internet. Kulldorf’s
SaTScan system implements a
range of different scan
statistical models

GIS in Health in Practice
• Facility location – dialysis units
• Facility location – obs & gyn
• Data integration – schizophrenia
in Cavan
• Data manipulation and display –
GMS card holders in Maynooth

1: Provision of Dialysis Units
• There are 5 dialysis units for renal
patients in a region with a population
of about 3,000,000
• Major population centre has two units
about 5 miles apart
• Where would you locate an extra one?
• What would happen if one was
closed?

Location-allocation
• Location-allocation problems deal
with optimum location of facilities
• Attempt to minimise total
weighted travel time/distance
from users to facilities
• What data is needed?

Data requirements
• Digitised road network with classification
of road types
• Mean travel speeds on each type for
urban/non-urban roads
• Extents of urban areas
• Anonymised postcoded patient records
• Postcode->grid reference lookup
• Locations of hospitals in region (both with
and without dialysis units)

GIS processing
• Data integration
• Determine which segments of road network
are urban
• Determine travel times using road type
urban/non-urban speeds
• Tally numbers of patients at each node in
the road network
• Find nearest nodes in network to each
hospital
• Code hospitals as ‘existing’ or ‘potential’
units

Modelling/Analysis
• Carried out using standard GIS
functions
• Convenient ‘front end’ written
using system’s macro language
(save a lot of typing)
• Explore a range of alternatives

Hospital with
dialysis unit

Study region showing hospital locations and urban areas

Patients requiring dialysis (tallied to road nodes)

Classified road network – includes roads outside study area

•

Some alternative
scenarios:

(a) Optimal allocation
of patients
(b) Provide 1 extra
unit
(c) Provide 2 extra
units

(d) Close 1 unit in
city and reallocate
its patients

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

integrate data
process data
model and analyse
generate alternative scenarios
display the results

2: Facility Location
• Two hospitals both provided facilities
for obstetrics, gynaecology, paediatrics
and a special care baby unit.
• Was there a case for rationalisation and
improving the facilities at one hospital
only?
• Would closure disadvantage any
patients?

Data
• Locations of hospitals in study region
• Anonymised postcoded finished
consultant episodes for each of the
four specialities at each hospital
• Classified road network
• Speeds associated with each road
type

Data preparation
• Postcoded records allocated grid
references using Central Postcode
Directory (POSTZON)
• Vector data converted to raster
• Patient/specialty/hospital counts
produced for each raster
• Passage times computed for each
raster cell based on road
classification and speed estimates

Roads, urban areas, patient locations, hospitals in study area

Processing
• Travel time surfaces computed for
each hospital
• Each raster cell contains time
required to reach hospital in minutes
• Assumed walking speed in ‘non road’
areas

Travel time surface for hospital S – darker = shorter time

Travel time surface for hospital T

Time differences
• If we subtract the travel time surfaces areas
nearer one hospital will have positive
values for the difference, and areas nearer
the other will have negative ones
• Boundary between positive and negative
values is boundary of catchment for each
hospital.
• If you’re on the boundary (difference is
zero), it doesn’t matter which hospital you
choose

Roads, patient locations, hospital catchments

Indifference
• If we identify those rasters where the time
difference (ignoring the sign) is, say, 5 –
people in those areas are in the 5 minute
‘indifference’ zone.
• Patients might not be worried about a 5
minute increase in their travel time, they
might be worried about 30 minutes
• Indifference zones can be computed quite
easily

15-minute indifference zone between the two hospitals

Mean access time
• For each cell in the raster we can
compute the product of the number
of patients and the time required to
reach each hospital
• Dividing this by the number of
patients gives a patient weighted
average time for each hospital

Average travel times for the various specialties. There
isn’t much to chose between specialities with the
exception of paediatric episodes.

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

gather data
integrate data
process data
model data
display results

• All carried out using raster
operations

3: Schizophrenia in Cavan
• In 1996 Pringle, Waddington and
Youssef reported on evidence for the
causes of schizophrenia
• Ecological analysis of variation among
EDs
– Cases varied from 0-11 per ED
– Populations varied from 250-2750

• Poisson probabilities suggested that
one area had an unusually high number
of cases – these can be mapped

Male cases
superimposed on plot
of prevalence by ED

Male cases
superimposed on
rectified Landsat
image with ED
boundaries

4: GMS card holders: Maynooth
•Data on GMS – Medical Card holders
drawn under strict data protection
protocols from DOHC
• Anonymised address lists data matched
with GeoDirectory
•Address matching rate of around 77% in
Maynooth

• Interesting range of results around
average for the whole ED

•ED – single value 14.6 % (national average around 30%)
•Small Area – Range from 2% to 70% - noticeable internal variation and
good indicator of elderly or poor populations

•Strategic value in comparing datasets
•Important in policy analysis

Mapping invites comparison

Role of GIS
• Data integration
– Tally of cases in small areas requires
data on locations of cases and small
area boundaries
– ‘spatial join’ places small area codes
on the case records
– Counting is a standard database
operation

• Data display

Finally
• GIS encourages you to think about
space
• In a ‘conventional’ map of Ireland
areas (EDs, Counties…) are shown in
proportion to their physical size
• There are alternative ways of
displaying maps…

In a cartogram the areas are drawn
proportional to their populations –
this is a “density-equalised” map
projection

A map of the boundaries of counties and
electoral divisions – the areas are drawn
proportional to their physical size

By comparison, the more deprived
western area of Dublin, northern area
of Cork and south east of Limerick are
clearly apparent in the cartogram.

Haase’s deprivation index suggests that the
most deprived populations are in peripheral
rural areas in the north west.

Redrawing the data on the cartogram
reveals wide variation in the SMR, and also
uncovers the urban dimension which is
hidden in the conventional display

Patient data matched to Electoral Divisions –
hospital admissions for psychiatric problems – the
patchwork effect does not suggest any pattern

The cartogram reveals the urban nature
of the problem – those areas which are
more deprived are also those with
higher levels of illness/disability

Residents aged 15+ who are unable to work
through illness or disability: the map pattern
suggests that might be a largely rural
phenomenon

The roads, rail, and urban areas data have
been transformed into the same space as
the cartogram. Dublin and Cork seem
poorly provided with roads…

Other spatial information can be transformed
to the space of the density equalised
projection. Here is the familiar map of main
roads and towns.

Using a cartogram we see that the
incidence of lung cancer is randomly
spatially distributed

Lung cancer in males in New York State [Gastner & Newman: 2004]

Plotting locations of lung cancer cases, we
obtain a map which follows the underlying
population distribution – there appear to be
“clusters”

What about Ireland?
• Potentially hampered by challenge of
geocoding patient records
• Lack of small area population data
– EDs have an average population of 1150
– Wide variation in size (50-28000)
– Socially inhomogenous

Rural addresses
• A major challenge is non-uniqueness
of addresses in rural areas.
• For instance in Moy ED, 11 properties
share the address
‘Lackamore, Lahinch, Co. Clare’

What’s needed
• Postcoding should provide a
mechanism for geocoding patient data
with the granularity required for
planning the delivery of health care –
this has been the case in the UK for a
for long time
• Census data should be made available
for areas with small and similar
populations (cf: UK Output Areas)

Finally…
• GIS has an important role to play
in health studies
• Health data analysis is a rapidly
developing field
• However, in Ireland we are
hampered by data deficiencies –
now is the time for change

Maynooth: image in Google Earth

Central Dublin: image in Google Earth

Taoiseach’s Office: image in Google Earth

Downing Street, London: image in Google Earth

Maynooth: OSi orthophotograph added locally uisng KML

Sellafield: image in Google Earth

NCU – On Line Atlas of Cancer Mortality

The Philippine Institute
for Development
Studies makes an online atlas of socioeconomic indicators
available – contains
some health measures

The future
• Patient empowerment…

• GIS used to merge information from
regulatory agencies on 19196 landfill sites
• Sites buffered to 2km and postcodes
within buffer identified
• Postcoded live births (8.2x106), stillbirths
(43471) and congenital anomalies
(124,597) between 1982 and 1997
• Poisson regression modelling undertaken
• Evidence found for excess risk of
congenital anomaly and low birthweight

• Distance from each
census ward to
nearest hospital
computed
• Map shows number
of patients waiting
longer than 6
months for elective
inpatient care per
available and
unoccupied bed
within 60 minutes
travel time


Slide 45

GIS and Health
Martin Charlton1
Ronan Foley2
Dennis Pringle2
1National

Centre for Geocomputation
2Department of Geography
NUI Maynooth

Outline
• Preliminary observations on GIS
and health
• Examples of GIS in Health:
research and practice
• Mapping people not places

Patterns in Health Data
• Three main areas:
– Mapping of disease incidence
• Relative risk estimation

– Identification of disease clustering
• Surveillance

– Ecological analysis
• Relationship between incidence and
covariates
(Lawson et al, 2003)

Health Data
• Typically patient data is available
in two forms
• Individual – perhaps anonymised
to postcode/zip code level
• Aggregate – counts are available
for a set of zones
• The nature of the data affects the
nature of the analysis

Geographic Information Systems
• GIS is often confused with mapping
• A range of activities which includes
inter alia mapping and analysis
• Will typically involve several datasets
• May also involve several different
pieces of software

Mapping: EpiINFO
• This is a rather simple analysis and
mapping program
• It can be downloaded from the internet
• It doesn’t provide much in the way of
spatial data manipulation…
• … nor are its analytical capabilities very
advanced
• It is easy to use

Spatial Data Manipulation
• GIS isn’t merely concerned with
data display or query
• It’s useful in bringing together
different spatial datasets in the
first stages of an analysis
• This may well require a range of
different geometrical and
database operations

Analysis with Spatial Data
• Statistical analysis usually involves
some separate software.
• Spatial data have some properties
which mean that SPSS isn’t always
appropriate.
• There is now a range of specialist
software – much of it available for
download

GIS may be ideal for data manipulation, display and query,
but it’s not always ideal for analysis – in epidemiology a
variety of analytical software is available … WinBUGS,
GeoBUGS (MRC Biostatistics Unit, Cambridge)

Software to implement
particular methods – for
example scan statistics - can be
downloaded quickly and easily
from the Internet. Kulldorf’s
SaTScan system implements a
range of different scan
statistical models

GIS in Health in Practice
• Facility location – dialysis units
• Facility location – obs & gyn
• Data integration – schizophrenia
in Cavan
• Data manipulation and display –
GMS card holders in Maynooth

1: Provision of Dialysis Units
• There are 5 dialysis units for renal
patients in a region with a population
of about 3,000,000
• Major population centre has two units
about 5 miles apart
• Where would you locate an extra one?
• What would happen if one was
closed?

Location-allocation
• Location-allocation problems deal
with optimum location of facilities
• Attempt to minimise total
weighted travel time/distance
from users to facilities
• What data is needed?

Data requirements
• Digitised road network with classification
of road types
• Mean travel speeds on each type for
urban/non-urban roads
• Extents of urban areas
• Anonymised postcoded patient records
• Postcode->grid reference lookup
• Locations of hospitals in region (both with
and without dialysis units)

GIS processing
• Data integration
• Determine which segments of road network
are urban
• Determine travel times using road type
urban/non-urban speeds
• Tally numbers of patients at each node in
the road network
• Find nearest nodes in network to each
hospital
• Code hospitals as ‘existing’ or ‘potential’
units

Modelling/Analysis
• Carried out using standard GIS
functions
• Convenient ‘front end’ written
using system’s macro language
(save a lot of typing)
• Explore a range of alternatives

Hospital with
dialysis unit

Study region showing hospital locations and urban areas

Patients requiring dialysis (tallied to road nodes)

Classified road network – includes roads outside study area

•

Some alternative
scenarios:

(a) Optimal allocation
of patients
(b) Provide 1 extra
unit
(c) Provide 2 extra
units

(d) Close 1 unit in
city and reallocate
its patients

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

integrate data
process data
model and analyse
generate alternative scenarios
display the results

2: Facility Location
• Two hospitals both provided facilities
for obstetrics, gynaecology, paediatrics
and a special care baby unit.
• Was there a case for rationalisation and
improving the facilities at one hospital
only?
• Would closure disadvantage any
patients?

Data
• Locations of hospitals in study region
• Anonymised postcoded finished
consultant episodes for each of the
four specialities at each hospital
• Classified road network
• Speeds associated with each road
type

Data preparation
• Postcoded records allocated grid
references using Central Postcode
Directory (POSTZON)
• Vector data converted to raster
• Patient/specialty/hospital counts
produced for each raster
• Passage times computed for each
raster cell based on road
classification and speed estimates

Roads, urban areas, patient locations, hospitals in study area

Processing
• Travel time surfaces computed for
each hospital
• Each raster cell contains time
required to reach hospital in minutes
• Assumed walking speed in ‘non road’
areas

Travel time surface for hospital S – darker = shorter time

Travel time surface for hospital T

Time differences
• If we subtract the travel time surfaces areas
nearer one hospital will have positive
values for the difference, and areas nearer
the other will have negative ones
• Boundary between positive and negative
values is boundary of catchment for each
hospital.
• If you’re on the boundary (difference is
zero), it doesn’t matter which hospital you
choose

Roads, patient locations, hospital catchments

Indifference
• If we identify those rasters where the time
difference (ignoring the sign) is, say, 5 –
people in those areas are in the 5 minute
‘indifference’ zone.
• Patients might not be worried about a 5
minute increase in their travel time, they
might be worried about 30 minutes
• Indifference zones can be computed quite
easily

15-minute indifference zone between the two hospitals

Mean access time
• For each cell in the raster we can
compute the product of the number
of patients and the time required to
reach each hospital
• Dividing this by the number of
patients gives a patient weighted
average time for each hospital

Average travel times for the various specialties. There
isn’t much to chose between specialities with the
exception of paediatric episodes.

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

gather data
integrate data
process data
model data
display results

• All carried out using raster
operations

3: Schizophrenia in Cavan
• In 1996 Pringle, Waddington and
Youssef reported on evidence for the
causes of schizophrenia
• Ecological analysis of variation among
EDs
– Cases varied from 0-11 per ED
– Populations varied from 250-2750

• Poisson probabilities suggested that
one area had an unusually high number
of cases – these can be mapped

Male cases
superimposed on plot
of prevalence by ED

Male cases
superimposed on
rectified Landsat
image with ED
boundaries

4: GMS card holders: Maynooth
•Data on GMS – Medical Card holders
drawn under strict data protection
protocols from DOHC
• Anonymised address lists data matched
with GeoDirectory
•Address matching rate of around 77% in
Maynooth

• Interesting range of results around
average for the whole ED

•ED – single value 14.6 % (national average around 30%)
•Small Area – Range from 2% to 70% - noticeable internal variation and
good indicator of elderly or poor populations

•Strategic value in comparing datasets
•Important in policy analysis

Mapping invites comparison

Role of GIS
• Data integration
– Tally of cases in small areas requires
data on locations of cases and small
area boundaries
– ‘spatial join’ places small area codes
on the case records
– Counting is a standard database
operation

• Data display

Finally
• GIS encourages you to think about
space
• In a ‘conventional’ map of Ireland
areas (EDs, Counties…) are shown in
proportion to their physical size
• There are alternative ways of
displaying maps…

In a cartogram the areas are drawn
proportional to their populations –
this is a “density-equalised” map
projection

A map of the boundaries of counties and
electoral divisions – the areas are drawn
proportional to their physical size

By comparison, the more deprived
western area of Dublin, northern area
of Cork and south east of Limerick are
clearly apparent in the cartogram.

Haase’s deprivation index suggests that the
most deprived populations are in peripheral
rural areas in the north west.

Redrawing the data on the cartogram
reveals wide variation in the SMR, and also
uncovers the urban dimension which is
hidden in the conventional display

Patient data matched to Electoral Divisions –
hospital admissions for psychiatric problems – the
patchwork effect does not suggest any pattern

The cartogram reveals the urban nature
of the problem – those areas which are
more deprived are also those with
higher levels of illness/disability

Residents aged 15+ who are unable to work
through illness or disability: the map pattern
suggests that might be a largely rural
phenomenon

The roads, rail, and urban areas data have
been transformed into the same space as
the cartogram. Dublin and Cork seem
poorly provided with roads…

Other spatial information can be transformed
to the space of the density equalised
projection. Here is the familiar map of main
roads and towns.

Using a cartogram we see that the
incidence of lung cancer is randomly
spatially distributed

Lung cancer in males in New York State [Gastner & Newman: 2004]

Plotting locations of lung cancer cases, we
obtain a map which follows the underlying
population distribution – there appear to be
“clusters”

What about Ireland?
• Potentially hampered by challenge of
geocoding patient records
• Lack of small area population data
– EDs have an average population of 1150
– Wide variation in size (50-28000)
– Socially inhomogenous

Rural addresses
• A major challenge is non-uniqueness
of addresses in rural areas.
• For instance in Moy ED, 11 properties
share the address
‘Lackamore, Lahinch, Co. Clare’

What’s needed
• Postcoding should provide a
mechanism for geocoding patient data
with the granularity required for
planning the delivery of health care –
this has been the case in the UK for a
for long time
• Census data should be made available
for areas with small and similar
populations (cf: UK Output Areas)

Finally…
• GIS has an important role to play
in health studies
• Health data analysis is a rapidly
developing field
• However, in Ireland we are
hampered by data deficiencies –
now is the time for change

Maynooth: image in Google Earth

Central Dublin: image in Google Earth

Taoiseach’s Office: image in Google Earth

Downing Street, London: image in Google Earth

Maynooth: OSi orthophotograph added locally uisng KML

Sellafield: image in Google Earth

NCU – On Line Atlas of Cancer Mortality

The Philippine Institute
for Development
Studies makes an online atlas of socioeconomic indicators
available – contains
some health measures

The future
• Patient empowerment…

• GIS used to merge information from
regulatory agencies on 19196 landfill sites
• Sites buffered to 2km and postcodes
within buffer identified
• Postcoded live births (8.2x106), stillbirths
(43471) and congenital anomalies
(124,597) between 1982 and 1997
• Poisson regression modelling undertaken
• Evidence found for excess risk of
congenital anomaly and low birthweight

• Distance from each
census ward to
nearest hospital
computed
• Map shows number
of patients waiting
longer than 6
months for elective
inpatient care per
available and
unoccupied bed
within 60 minutes
travel time


Slide 46

GIS and Health
Martin Charlton1
Ronan Foley2
Dennis Pringle2
1National

Centre for Geocomputation
2Department of Geography
NUI Maynooth

Outline
• Preliminary observations on GIS
and health
• Examples of GIS in Health:
research and practice
• Mapping people not places

Patterns in Health Data
• Three main areas:
– Mapping of disease incidence
• Relative risk estimation

– Identification of disease clustering
• Surveillance

– Ecological analysis
• Relationship between incidence and
covariates
(Lawson et al, 2003)

Health Data
• Typically patient data is available
in two forms
• Individual – perhaps anonymised
to postcode/zip code level
• Aggregate – counts are available
for a set of zones
• The nature of the data affects the
nature of the analysis

Geographic Information Systems
• GIS is often confused with mapping
• A range of activities which includes
inter alia mapping and analysis
• Will typically involve several datasets
• May also involve several different
pieces of software

Mapping: EpiINFO
• This is a rather simple analysis and
mapping program
• It can be downloaded from the internet
• It doesn’t provide much in the way of
spatial data manipulation…
• … nor are its analytical capabilities very
advanced
• It is easy to use

Spatial Data Manipulation
• GIS isn’t merely concerned with
data display or query
• It’s useful in bringing together
different spatial datasets in the
first stages of an analysis
• This may well require a range of
different geometrical and
database operations

Analysis with Spatial Data
• Statistical analysis usually involves
some separate software.
• Spatial data have some properties
which mean that SPSS isn’t always
appropriate.
• There is now a range of specialist
software – much of it available for
download

GIS may be ideal for data manipulation, display and query,
but it’s not always ideal for analysis – in epidemiology a
variety of analytical software is available … WinBUGS,
GeoBUGS (MRC Biostatistics Unit, Cambridge)

Software to implement
particular methods – for
example scan statistics - can be
downloaded quickly and easily
from the Internet. Kulldorf’s
SaTScan system implements a
range of different scan
statistical models

GIS in Health in Practice
• Facility location – dialysis units
• Facility location – obs & gyn
• Data integration – schizophrenia
in Cavan
• Data manipulation and display –
GMS card holders in Maynooth

1: Provision of Dialysis Units
• There are 5 dialysis units for renal
patients in a region with a population
of about 3,000,000
• Major population centre has two units
about 5 miles apart
• Where would you locate an extra one?
• What would happen if one was
closed?

Location-allocation
• Location-allocation problems deal
with optimum location of facilities
• Attempt to minimise total
weighted travel time/distance
from users to facilities
• What data is needed?

Data requirements
• Digitised road network with classification
of road types
• Mean travel speeds on each type for
urban/non-urban roads
• Extents of urban areas
• Anonymised postcoded patient records
• Postcode->grid reference lookup
• Locations of hospitals in region (both with
and without dialysis units)

GIS processing
• Data integration
• Determine which segments of road network
are urban
• Determine travel times using road type
urban/non-urban speeds
• Tally numbers of patients at each node in
the road network
• Find nearest nodes in network to each
hospital
• Code hospitals as ‘existing’ or ‘potential’
units

Modelling/Analysis
• Carried out using standard GIS
functions
• Convenient ‘front end’ written
using system’s macro language
(save a lot of typing)
• Explore a range of alternatives

Hospital with
dialysis unit

Study region showing hospital locations and urban areas

Patients requiring dialysis (tallied to road nodes)

Classified road network – includes roads outside study area

•

Some alternative
scenarios:

(a) Optimal allocation
of patients
(b) Provide 1 extra
unit
(c) Provide 2 extra
units

(d) Close 1 unit in
city and reallocate
its patients

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

integrate data
process data
model and analyse
generate alternative scenarios
display the results

2: Facility Location
• Two hospitals both provided facilities
for obstetrics, gynaecology, paediatrics
and a special care baby unit.
• Was there a case for rationalisation and
improving the facilities at one hospital
only?
• Would closure disadvantage any
patients?

Data
• Locations of hospitals in study region
• Anonymised postcoded finished
consultant episodes for each of the
four specialities at each hospital
• Classified road network
• Speeds associated with each road
type

Data preparation
• Postcoded records allocated grid
references using Central Postcode
Directory (POSTZON)
• Vector data converted to raster
• Patient/specialty/hospital counts
produced for each raster
• Passage times computed for each
raster cell based on road
classification and speed estimates

Roads, urban areas, patient locations, hospitals in study area

Processing
• Travel time surfaces computed for
each hospital
• Each raster cell contains time
required to reach hospital in minutes
• Assumed walking speed in ‘non road’
areas

Travel time surface for hospital S – darker = shorter time

Travel time surface for hospital T

Time differences
• If we subtract the travel time surfaces areas
nearer one hospital will have positive
values for the difference, and areas nearer
the other will have negative ones
• Boundary between positive and negative
values is boundary of catchment for each
hospital.
• If you’re on the boundary (difference is
zero), it doesn’t matter which hospital you
choose

Roads, patient locations, hospital catchments

Indifference
• If we identify those rasters where the time
difference (ignoring the sign) is, say, 5 –
people in those areas are in the 5 minute
‘indifference’ zone.
• Patients might not be worried about a 5
minute increase in their travel time, they
might be worried about 30 minutes
• Indifference zones can be computed quite
easily

15-minute indifference zone between the two hospitals

Mean access time
• For each cell in the raster we can
compute the product of the number
of patients and the time required to
reach each hospital
• Dividing this by the number of
patients gives a patient weighted
average time for each hospital

Average travel times for the various specialties. There
isn’t much to chose between specialities with the
exception of paediatric episodes.

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

gather data
integrate data
process data
model data
display results

• All carried out using raster
operations

3: Schizophrenia in Cavan
• In 1996 Pringle, Waddington and
Youssef reported on evidence for the
causes of schizophrenia
• Ecological analysis of variation among
EDs
– Cases varied from 0-11 per ED
– Populations varied from 250-2750

• Poisson probabilities suggested that
one area had an unusually high number
of cases – these can be mapped

Male cases
superimposed on plot
of prevalence by ED

Male cases
superimposed on
rectified Landsat
image with ED
boundaries

4: GMS card holders: Maynooth
•Data on GMS – Medical Card holders
drawn under strict data protection
protocols from DOHC
• Anonymised address lists data matched
with GeoDirectory
•Address matching rate of around 77% in
Maynooth

• Interesting range of results around
average for the whole ED

•ED – single value 14.6 % (national average around 30%)
•Small Area – Range from 2% to 70% - noticeable internal variation and
good indicator of elderly or poor populations

•Strategic value in comparing datasets
•Important in policy analysis

Mapping invites comparison

Role of GIS
• Data integration
– Tally of cases in small areas requires
data on locations of cases and small
area boundaries
– ‘spatial join’ places small area codes
on the case records
– Counting is a standard database
operation

• Data display

Finally
• GIS encourages you to think about
space
• In a ‘conventional’ map of Ireland
areas (EDs, Counties…) are shown in
proportion to their physical size
• There are alternative ways of
displaying maps…

In a cartogram the areas are drawn
proportional to their populations –
this is a “density-equalised” map
projection

A map of the boundaries of counties and
electoral divisions – the areas are drawn
proportional to their physical size

By comparison, the more deprived
western area of Dublin, northern area
of Cork and south east of Limerick are
clearly apparent in the cartogram.

Haase’s deprivation index suggests that the
most deprived populations are in peripheral
rural areas in the north west.

Redrawing the data on the cartogram
reveals wide variation in the SMR, and also
uncovers the urban dimension which is
hidden in the conventional display

Patient data matched to Electoral Divisions –
hospital admissions for psychiatric problems – the
patchwork effect does not suggest any pattern

The cartogram reveals the urban nature
of the problem – those areas which are
more deprived are also those with
higher levels of illness/disability

Residents aged 15+ who are unable to work
through illness or disability: the map pattern
suggests that might be a largely rural
phenomenon

The roads, rail, and urban areas data have
been transformed into the same space as
the cartogram. Dublin and Cork seem
poorly provided with roads…

Other spatial information can be transformed
to the space of the density equalised
projection. Here is the familiar map of main
roads and towns.

Using a cartogram we see that the
incidence of lung cancer is randomly
spatially distributed

Lung cancer in males in New York State [Gastner & Newman: 2004]

Plotting locations of lung cancer cases, we
obtain a map which follows the underlying
population distribution – there appear to be
“clusters”

What about Ireland?
• Potentially hampered by challenge of
geocoding patient records
• Lack of small area population data
– EDs have an average population of 1150
– Wide variation in size (50-28000)
– Socially inhomogenous

Rural addresses
• A major challenge is non-uniqueness
of addresses in rural areas.
• For instance in Moy ED, 11 properties
share the address
‘Lackamore, Lahinch, Co. Clare’

What’s needed
• Postcoding should provide a
mechanism for geocoding patient data
with the granularity required for
planning the delivery of health care –
this has been the case in the UK for a
for long time
• Census data should be made available
for areas with small and similar
populations (cf: UK Output Areas)

Finally…
• GIS has an important role to play
in health studies
• Health data analysis is a rapidly
developing field
• However, in Ireland we are
hampered by data deficiencies –
now is the time for change

Maynooth: image in Google Earth

Central Dublin: image in Google Earth

Taoiseach’s Office: image in Google Earth

Downing Street, London: image in Google Earth

Maynooth: OSi orthophotograph added locally uisng KML

Sellafield: image in Google Earth

NCU – On Line Atlas of Cancer Mortality

The Philippine Institute
for Development
Studies makes an online atlas of socioeconomic indicators
available – contains
some health measures

The future
• Patient empowerment…

• GIS used to merge information from
regulatory agencies on 19196 landfill sites
• Sites buffered to 2km and postcodes
within buffer identified
• Postcoded live births (8.2x106), stillbirths
(43471) and congenital anomalies
(124,597) between 1982 and 1997
• Poisson regression modelling undertaken
• Evidence found for excess risk of
congenital anomaly and low birthweight

• Distance from each
census ward to
nearest hospital
computed
• Map shows number
of patients waiting
longer than 6
months for elective
inpatient care per
available and
unoccupied bed
within 60 minutes
travel time


Slide 47

GIS and Health
Martin Charlton1
Ronan Foley2
Dennis Pringle2
1National

Centre for Geocomputation
2Department of Geography
NUI Maynooth

Outline
• Preliminary observations on GIS
and health
• Examples of GIS in Health:
research and practice
• Mapping people not places

Patterns in Health Data
• Three main areas:
– Mapping of disease incidence
• Relative risk estimation

– Identification of disease clustering
• Surveillance

– Ecological analysis
• Relationship between incidence and
covariates
(Lawson et al, 2003)

Health Data
• Typically patient data is available
in two forms
• Individual – perhaps anonymised
to postcode/zip code level
• Aggregate – counts are available
for a set of zones
• The nature of the data affects the
nature of the analysis

Geographic Information Systems
• GIS is often confused with mapping
• A range of activities which includes
inter alia mapping and analysis
• Will typically involve several datasets
• May also involve several different
pieces of software

Mapping: EpiINFO
• This is a rather simple analysis and
mapping program
• It can be downloaded from the internet
• It doesn’t provide much in the way of
spatial data manipulation…
• … nor are its analytical capabilities very
advanced
• It is easy to use

Spatial Data Manipulation
• GIS isn’t merely concerned with
data display or query
• It’s useful in bringing together
different spatial datasets in the
first stages of an analysis
• This may well require a range of
different geometrical and
database operations

Analysis with Spatial Data
• Statistical analysis usually involves
some separate software.
• Spatial data have some properties
which mean that SPSS isn’t always
appropriate.
• There is now a range of specialist
software – much of it available for
download

GIS may be ideal for data manipulation, display and query,
but it’s not always ideal for analysis – in epidemiology a
variety of analytical software is available … WinBUGS,
GeoBUGS (MRC Biostatistics Unit, Cambridge)

Software to implement
particular methods – for
example scan statistics - can be
downloaded quickly and easily
from the Internet. Kulldorf’s
SaTScan system implements a
range of different scan
statistical models

GIS in Health in Practice
• Facility location – dialysis units
• Facility location – obs & gyn
• Data integration – schizophrenia
in Cavan
• Data manipulation and display –
GMS card holders in Maynooth

1: Provision of Dialysis Units
• There are 5 dialysis units for renal
patients in a region with a population
of about 3,000,000
• Major population centre has two units
about 5 miles apart
• Where would you locate an extra one?
• What would happen if one was
closed?

Location-allocation
• Location-allocation problems deal
with optimum location of facilities
• Attempt to minimise total
weighted travel time/distance
from users to facilities
• What data is needed?

Data requirements
• Digitised road network with classification
of road types
• Mean travel speeds on each type for
urban/non-urban roads
• Extents of urban areas
• Anonymised postcoded patient records
• Postcode->grid reference lookup
• Locations of hospitals in region (both with
and without dialysis units)

GIS processing
• Data integration
• Determine which segments of road network
are urban
• Determine travel times using road type
urban/non-urban speeds
• Tally numbers of patients at each node in
the road network
• Find nearest nodes in network to each
hospital
• Code hospitals as ‘existing’ or ‘potential’
units

Modelling/Analysis
• Carried out using standard GIS
functions
• Convenient ‘front end’ written
using system’s macro language
(save a lot of typing)
• Explore a range of alternatives

Hospital with
dialysis unit

Study region showing hospital locations and urban areas

Patients requiring dialysis (tallied to road nodes)

Classified road network – includes roads outside study area

•

Some alternative
scenarios:

(a) Optimal allocation
of patients
(b) Provide 1 extra
unit
(c) Provide 2 extra
units

(d) Close 1 unit in
city and reallocate
its patients

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

integrate data
process data
model and analyse
generate alternative scenarios
display the results

2: Facility Location
• Two hospitals both provided facilities
for obstetrics, gynaecology, paediatrics
and a special care baby unit.
• Was there a case for rationalisation and
improving the facilities at one hospital
only?
• Would closure disadvantage any
patients?

Data
• Locations of hospitals in study region
• Anonymised postcoded finished
consultant episodes for each of the
four specialities at each hospital
• Classified road network
• Speeds associated with each road
type

Data preparation
• Postcoded records allocated grid
references using Central Postcode
Directory (POSTZON)
• Vector data converted to raster
• Patient/specialty/hospital counts
produced for each raster
• Passage times computed for each
raster cell based on road
classification and speed estimates

Roads, urban areas, patient locations, hospitals in study area

Processing
• Travel time surfaces computed for
each hospital
• Each raster cell contains time
required to reach hospital in minutes
• Assumed walking speed in ‘non road’
areas

Travel time surface for hospital S – darker = shorter time

Travel time surface for hospital T

Time differences
• If we subtract the travel time surfaces areas
nearer one hospital will have positive
values for the difference, and areas nearer
the other will have negative ones
• Boundary between positive and negative
values is boundary of catchment for each
hospital.
• If you’re on the boundary (difference is
zero), it doesn’t matter which hospital you
choose

Roads, patient locations, hospital catchments

Indifference
• If we identify those rasters where the time
difference (ignoring the sign) is, say, 5 –
people in those areas are in the 5 minute
‘indifference’ zone.
• Patients might not be worried about a 5
minute increase in their travel time, they
might be worried about 30 minutes
• Indifference zones can be computed quite
easily

15-minute indifference zone between the two hospitals

Mean access time
• For each cell in the raster we can
compute the product of the number
of patients and the time required to
reach each hospital
• Dividing this by the number of
patients gives a patient weighted
average time for each hospital

Average travel times for the various specialties. There
isn’t much to chose between specialities with the
exception of paediatric episodes.

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

gather data
integrate data
process data
model data
display results

• All carried out using raster
operations

3: Schizophrenia in Cavan
• In 1996 Pringle, Waddington and
Youssef reported on evidence for the
causes of schizophrenia
• Ecological analysis of variation among
EDs
– Cases varied from 0-11 per ED
– Populations varied from 250-2750

• Poisson probabilities suggested that
one area had an unusually high number
of cases – these can be mapped

Male cases
superimposed on plot
of prevalence by ED

Male cases
superimposed on
rectified Landsat
image with ED
boundaries

4: GMS card holders: Maynooth
•Data on GMS – Medical Card holders
drawn under strict data protection
protocols from DOHC
• Anonymised address lists data matched
with GeoDirectory
•Address matching rate of around 77% in
Maynooth

• Interesting range of results around
average for the whole ED

•ED – single value 14.6 % (national average around 30%)
•Small Area – Range from 2% to 70% - noticeable internal variation and
good indicator of elderly or poor populations

•Strategic value in comparing datasets
•Important in policy analysis

Mapping invites comparison

Role of GIS
• Data integration
– Tally of cases in small areas requires
data on locations of cases and small
area boundaries
– ‘spatial join’ places small area codes
on the case records
– Counting is a standard database
operation

• Data display

Finally
• GIS encourages you to think about
space
• In a ‘conventional’ map of Ireland
areas (EDs, Counties…) are shown in
proportion to their physical size
• There are alternative ways of
displaying maps…

In a cartogram the areas are drawn
proportional to their populations –
this is a “density-equalised” map
projection

A map of the boundaries of counties and
electoral divisions – the areas are drawn
proportional to their physical size

By comparison, the more deprived
western area of Dublin, northern area
of Cork and south east of Limerick are
clearly apparent in the cartogram.

Haase’s deprivation index suggests that the
most deprived populations are in peripheral
rural areas in the north west.

Redrawing the data on the cartogram
reveals wide variation in the SMR, and also
uncovers the urban dimension which is
hidden in the conventional display

Patient data matched to Electoral Divisions –
hospital admissions for psychiatric problems – the
patchwork effect does not suggest any pattern

The cartogram reveals the urban nature
of the problem – those areas which are
more deprived are also those with
higher levels of illness/disability

Residents aged 15+ who are unable to work
through illness or disability: the map pattern
suggests that might be a largely rural
phenomenon

The roads, rail, and urban areas data have
been transformed into the same space as
the cartogram. Dublin and Cork seem
poorly provided with roads…

Other spatial information can be transformed
to the space of the density equalised
projection. Here is the familiar map of main
roads and towns.

Using a cartogram we see that the
incidence of lung cancer is randomly
spatially distributed

Lung cancer in males in New York State [Gastner & Newman: 2004]

Plotting locations of lung cancer cases, we
obtain a map which follows the underlying
population distribution – there appear to be
“clusters”

What about Ireland?
• Potentially hampered by challenge of
geocoding patient records
• Lack of small area population data
– EDs have an average population of 1150
– Wide variation in size (50-28000)
– Socially inhomogenous

Rural addresses
• A major challenge is non-uniqueness
of addresses in rural areas.
• For instance in Moy ED, 11 properties
share the address
‘Lackamore, Lahinch, Co. Clare’

What’s needed
• Postcoding should provide a
mechanism for geocoding patient data
with the granularity required for
planning the delivery of health care –
this has been the case in the UK for a
for long time
• Census data should be made available
for areas with small and similar
populations (cf: UK Output Areas)

Finally…
• GIS has an important role to play
in health studies
• Health data analysis is a rapidly
developing field
• However, in Ireland we are
hampered by data deficiencies –
now is the time for change

Maynooth: image in Google Earth

Central Dublin: image in Google Earth

Taoiseach’s Office: image in Google Earth

Downing Street, London: image in Google Earth

Maynooth: OSi orthophotograph added locally uisng KML

Sellafield: image in Google Earth

NCU – On Line Atlas of Cancer Mortality

The Philippine Institute
for Development
Studies makes an online atlas of socioeconomic indicators
available – contains
some health measures

The future
• Patient empowerment…

• GIS used to merge information from
regulatory agencies on 19196 landfill sites
• Sites buffered to 2km and postcodes
within buffer identified
• Postcoded live births (8.2x106), stillbirths
(43471) and congenital anomalies
(124,597) between 1982 and 1997
• Poisson regression modelling undertaken
• Evidence found for excess risk of
congenital anomaly and low birthweight

• Distance from each
census ward to
nearest hospital
computed
• Map shows number
of patients waiting
longer than 6
months for elective
inpatient care per
available and
unoccupied bed
within 60 minutes
travel time


Slide 48

GIS and Health
Martin Charlton1
Ronan Foley2
Dennis Pringle2
1National

Centre for Geocomputation
2Department of Geography
NUI Maynooth

Outline
• Preliminary observations on GIS
and health
• Examples of GIS in Health:
research and practice
• Mapping people not places

Patterns in Health Data
• Three main areas:
– Mapping of disease incidence
• Relative risk estimation

– Identification of disease clustering
• Surveillance

– Ecological analysis
• Relationship between incidence and
covariates
(Lawson et al, 2003)

Health Data
• Typically patient data is available
in two forms
• Individual – perhaps anonymised
to postcode/zip code level
• Aggregate – counts are available
for a set of zones
• The nature of the data affects the
nature of the analysis

Geographic Information Systems
• GIS is often confused with mapping
• A range of activities which includes
inter alia mapping and analysis
• Will typically involve several datasets
• May also involve several different
pieces of software

Mapping: EpiINFO
• This is a rather simple analysis and
mapping program
• It can be downloaded from the internet
• It doesn’t provide much in the way of
spatial data manipulation…
• … nor are its analytical capabilities very
advanced
• It is easy to use

Spatial Data Manipulation
• GIS isn’t merely concerned with
data display or query
• It’s useful in bringing together
different spatial datasets in the
first stages of an analysis
• This may well require a range of
different geometrical and
database operations

Analysis with Spatial Data
• Statistical analysis usually involves
some separate software.
• Spatial data have some properties
which mean that SPSS isn’t always
appropriate.
• There is now a range of specialist
software – much of it available for
download

GIS may be ideal for data manipulation, display and query,
but it’s not always ideal for analysis – in epidemiology a
variety of analytical software is available … WinBUGS,
GeoBUGS (MRC Biostatistics Unit, Cambridge)

Software to implement
particular methods – for
example scan statistics - can be
downloaded quickly and easily
from the Internet. Kulldorf’s
SaTScan system implements a
range of different scan
statistical models

GIS in Health in Practice
• Facility location – dialysis units
• Facility location – obs & gyn
• Data integration – schizophrenia
in Cavan
• Data manipulation and display –
GMS card holders in Maynooth

1: Provision of Dialysis Units
• There are 5 dialysis units for renal
patients in a region with a population
of about 3,000,000
• Major population centre has two units
about 5 miles apart
• Where would you locate an extra one?
• What would happen if one was
closed?

Location-allocation
• Location-allocation problems deal
with optimum location of facilities
• Attempt to minimise total
weighted travel time/distance
from users to facilities
• What data is needed?

Data requirements
• Digitised road network with classification
of road types
• Mean travel speeds on each type for
urban/non-urban roads
• Extents of urban areas
• Anonymised postcoded patient records
• Postcode->grid reference lookup
• Locations of hospitals in region (both with
and without dialysis units)

GIS processing
• Data integration
• Determine which segments of road network
are urban
• Determine travel times using road type
urban/non-urban speeds
• Tally numbers of patients at each node in
the road network
• Find nearest nodes in network to each
hospital
• Code hospitals as ‘existing’ or ‘potential’
units

Modelling/Analysis
• Carried out using standard GIS
functions
• Convenient ‘front end’ written
using system’s macro language
(save a lot of typing)
• Explore a range of alternatives

Hospital with
dialysis unit

Study region showing hospital locations and urban areas

Patients requiring dialysis (tallied to road nodes)

Classified road network – includes roads outside study area

•

Some alternative
scenarios:

(a) Optimal allocation
of patients
(b) Provide 1 extra
unit
(c) Provide 2 extra
units

(d) Close 1 unit in
city and reallocate
its patients

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

integrate data
process data
model and analyse
generate alternative scenarios
display the results

2: Facility Location
• Two hospitals both provided facilities
for obstetrics, gynaecology, paediatrics
and a special care baby unit.
• Was there a case for rationalisation and
improving the facilities at one hospital
only?
• Would closure disadvantage any
patients?

Data
• Locations of hospitals in study region
• Anonymised postcoded finished
consultant episodes for each of the
four specialities at each hospital
• Classified road network
• Speeds associated with each road
type

Data preparation
• Postcoded records allocated grid
references using Central Postcode
Directory (POSTZON)
• Vector data converted to raster
• Patient/specialty/hospital counts
produced for each raster
• Passage times computed for each
raster cell based on road
classification and speed estimates

Roads, urban areas, patient locations, hospitals in study area

Processing
• Travel time surfaces computed for
each hospital
• Each raster cell contains time
required to reach hospital in minutes
• Assumed walking speed in ‘non road’
areas

Travel time surface for hospital S – darker = shorter time

Travel time surface for hospital T

Time differences
• If we subtract the travel time surfaces areas
nearer one hospital will have positive
values for the difference, and areas nearer
the other will have negative ones
• Boundary between positive and negative
values is boundary of catchment for each
hospital.
• If you’re on the boundary (difference is
zero), it doesn’t matter which hospital you
choose

Roads, patient locations, hospital catchments

Indifference
• If we identify those rasters where the time
difference (ignoring the sign) is, say, 5 –
people in those areas are in the 5 minute
‘indifference’ zone.
• Patients might not be worried about a 5
minute increase in their travel time, they
might be worried about 30 minutes
• Indifference zones can be computed quite
easily

15-minute indifference zone between the two hospitals

Mean access time
• For each cell in the raster we can
compute the product of the number
of patients and the time required to
reach each hospital
• Dividing this by the number of
patients gives a patient weighted
average time for each hospital

Average travel times for the various specialties. There
isn’t much to chose between specialities with the
exception of paediatric episodes.

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

gather data
integrate data
process data
model data
display results

• All carried out using raster
operations

3: Schizophrenia in Cavan
• In 1996 Pringle, Waddington and
Youssef reported on evidence for the
causes of schizophrenia
• Ecological analysis of variation among
EDs
– Cases varied from 0-11 per ED
– Populations varied from 250-2750

• Poisson probabilities suggested that
one area had an unusually high number
of cases – these can be mapped

Male cases
superimposed on plot
of prevalence by ED

Male cases
superimposed on
rectified Landsat
image with ED
boundaries

4: GMS card holders: Maynooth
•Data on GMS – Medical Card holders
drawn under strict data protection
protocols from DOHC
• Anonymised address lists data matched
with GeoDirectory
•Address matching rate of around 77% in
Maynooth

• Interesting range of results around
average for the whole ED

•ED – single value 14.6 % (national average around 30%)
•Small Area – Range from 2% to 70% - noticeable internal variation and
good indicator of elderly or poor populations

•Strategic value in comparing datasets
•Important in policy analysis

Mapping invites comparison

Role of GIS
• Data integration
– Tally of cases in small areas requires
data on locations of cases and small
area boundaries
– ‘spatial join’ places small area codes
on the case records
– Counting is a standard database
operation

• Data display

Finally
• GIS encourages you to think about
space
• In a ‘conventional’ map of Ireland
areas (EDs, Counties…) are shown in
proportion to their physical size
• There are alternative ways of
displaying maps…

In a cartogram the areas are drawn
proportional to their populations –
this is a “density-equalised” map
projection

A map of the boundaries of counties and
electoral divisions – the areas are drawn
proportional to their physical size

By comparison, the more deprived
western area of Dublin, northern area
of Cork and south east of Limerick are
clearly apparent in the cartogram.

Haase’s deprivation index suggests that the
most deprived populations are in peripheral
rural areas in the north west.

Redrawing the data on the cartogram
reveals wide variation in the SMR, and also
uncovers the urban dimension which is
hidden in the conventional display

Patient data matched to Electoral Divisions –
hospital admissions for psychiatric problems – the
patchwork effect does not suggest any pattern

The cartogram reveals the urban nature
of the problem – those areas which are
more deprived are also those with
higher levels of illness/disability

Residents aged 15+ who are unable to work
through illness or disability: the map pattern
suggests that might be a largely rural
phenomenon

The roads, rail, and urban areas data have
been transformed into the same space as
the cartogram. Dublin and Cork seem
poorly provided with roads…

Other spatial information can be transformed
to the space of the density equalised
projection. Here is the familiar map of main
roads and towns.

Using a cartogram we see that the
incidence of lung cancer is randomly
spatially distributed

Lung cancer in males in New York State [Gastner & Newman: 2004]

Plotting locations of lung cancer cases, we
obtain a map which follows the underlying
population distribution – there appear to be
“clusters”

What about Ireland?
• Potentially hampered by challenge of
geocoding patient records
• Lack of small area population data
– EDs have an average population of 1150
– Wide variation in size (50-28000)
– Socially inhomogenous

Rural addresses
• A major challenge is non-uniqueness
of addresses in rural areas.
• For instance in Moy ED, 11 properties
share the address
‘Lackamore, Lahinch, Co. Clare’

What’s needed
• Postcoding should provide a
mechanism for geocoding patient data
with the granularity required for
planning the delivery of health care –
this has been the case in the UK for a
for long time
• Census data should be made available
for areas with small and similar
populations (cf: UK Output Areas)

Finally…
• GIS has an important role to play
in health studies
• Health data analysis is a rapidly
developing field
• However, in Ireland we are
hampered by data deficiencies –
now is the time for change

Maynooth: image in Google Earth

Central Dublin: image in Google Earth

Taoiseach’s Office: image in Google Earth

Downing Street, London: image in Google Earth

Maynooth: OSi orthophotograph added locally uisng KML

Sellafield: image in Google Earth

NCU – On Line Atlas of Cancer Mortality

The Philippine Institute
for Development
Studies makes an online atlas of socioeconomic indicators
available – contains
some health measures

The future
• Patient empowerment…

• GIS used to merge information from
regulatory agencies on 19196 landfill sites
• Sites buffered to 2km and postcodes
within buffer identified
• Postcoded live births (8.2x106), stillbirths
(43471) and congenital anomalies
(124,597) between 1982 and 1997
• Poisson regression modelling undertaken
• Evidence found for excess risk of
congenital anomaly and low birthweight

• Distance from each
census ward to
nearest hospital
computed
• Map shows number
of patients waiting
longer than 6
months for elective
inpatient care per
available and
unoccupied bed
within 60 minutes
travel time


Slide 49

GIS and Health
Martin Charlton1
Ronan Foley2
Dennis Pringle2
1National

Centre for Geocomputation
2Department of Geography
NUI Maynooth

Outline
• Preliminary observations on GIS
and health
• Examples of GIS in Health:
research and practice
• Mapping people not places

Patterns in Health Data
• Three main areas:
– Mapping of disease incidence
• Relative risk estimation

– Identification of disease clustering
• Surveillance

– Ecological analysis
• Relationship between incidence and
covariates
(Lawson et al, 2003)

Health Data
• Typically patient data is available
in two forms
• Individual – perhaps anonymised
to postcode/zip code level
• Aggregate – counts are available
for a set of zones
• The nature of the data affects the
nature of the analysis

Geographic Information Systems
• GIS is often confused with mapping
• A range of activities which includes
inter alia mapping and analysis
• Will typically involve several datasets
• May also involve several different
pieces of software

Mapping: EpiINFO
• This is a rather simple analysis and
mapping program
• It can be downloaded from the internet
• It doesn’t provide much in the way of
spatial data manipulation…
• … nor are its analytical capabilities very
advanced
• It is easy to use

Spatial Data Manipulation
• GIS isn’t merely concerned with
data display or query
• It’s useful in bringing together
different spatial datasets in the
first stages of an analysis
• This may well require a range of
different geometrical and
database operations

Analysis with Spatial Data
• Statistical analysis usually involves
some separate software.
• Spatial data have some properties
which mean that SPSS isn’t always
appropriate.
• There is now a range of specialist
software – much of it available for
download

GIS may be ideal for data manipulation, display and query,
but it’s not always ideal for analysis – in epidemiology a
variety of analytical software is available … WinBUGS,
GeoBUGS (MRC Biostatistics Unit, Cambridge)

Software to implement
particular methods – for
example scan statistics - can be
downloaded quickly and easily
from the Internet. Kulldorf’s
SaTScan system implements a
range of different scan
statistical models

GIS in Health in Practice
• Facility location – dialysis units
• Facility location – obs & gyn
• Data integration – schizophrenia
in Cavan
• Data manipulation and display –
GMS card holders in Maynooth

1: Provision of Dialysis Units
• There are 5 dialysis units for renal
patients in a region with a population
of about 3,000,000
• Major population centre has two units
about 5 miles apart
• Where would you locate an extra one?
• What would happen if one was
closed?

Location-allocation
• Location-allocation problems deal
with optimum location of facilities
• Attempt to minimise total
weighted travel time/distance
from users to facilities
• What data is needed?

Data requirements
• Digitised road network with classification
of road types
• Mean travel speeds on each type for
urban/non-urban roads
• Extents of urban areas
• Anonymised postcoded patient records
• Postcode->grid reference lookup
• Locations of hospitals in region (both with
and without dialysis units)

GIS processing
• Data integration
• Determine which segments of road network
are urban
• Determine travel times using road type
urban/non-urban speeds
• Tally numbers of patients at each node in
the road network
• Find nearest nodes in network to each
hospital
• Code hospitals as ‘existing’ or ‘potential’
units

Modelling/Analysis
• Carried out using standard GIS
functions
• Convenient ‘front end’ written
using system’s macro language
(save a lot of typing)
• Explore a range of alternatives

Hospital with
dialysis unit

Study region showing hospital locations and urban areas

Patients requiring dialysis (tallied to road nodes)

Classified road network – includes roads outside study area

•

Some alternative
scenarios:

(a) Optimal allocation
of patients
(b) Provide 1 extra
unit
(c) Provide 2 extra
units

(d) Close 1 unit in
city and reallocate
its patients

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

integrate data
process data
model and analyse
generate alternative scenarios
display the results

2: Facility Location
• Two hospitals both provided facilities
for obstetrics, gynaecology, paediatrics
and a special care baby unit.
• Was there a case for rationalisation and
improving the facilities at one hospital
only?
• Would closure disadvantage any
patients?

Data
• Locations of hospitals in study region
• Anonymised postcoded finished
consultant episodes for each of the
four specialities at each hospital
• Classified road network
• Speeds associated with each road
type

Data preparation
• Postcoded records allocated grid
references using Central Postcode
Directory (POSTZON)
• Vector data converted to raster
• Patient/specialty/hospital counts
produced for each raster
• Passage times computed for each
raster cell based on road
classification and speed estimates

Roads, urban areas, patient locations, hospitals in study area

Processing
• Travel time surfaces computed for
each hospital
• Each raster cell contains time
required to reach hospital in minutes
• Assumed walking speed in ‘non road’
areas

Travel time surface for hospital S – darker = shorter time

Travel time surface for hospital T

Time differences
• If we subtract the travel time surfaces areas
nearer one hospital will have positive
values for the difference, and areas nearer
the other will have negative ones
• Boundary between positive and negative
values is boundary of catchment for each
hospital.
• If you’re on the boundary (difference is
zero), it doesn’t matter which hospital you
choose

Roads, patient locations, hospital catchments

Indifference
• If we identify those rasters where the time
difference (ignoring the sign) is, say, 5 –
people in those areas are in the 5 minute
‘indifference’ zone.
• Patients might not be worried about a 5
minute increase in their travel time, they
might be worried about 30 minutes
• Indifference zones can be computed quite
easily

15-minute indifference zone between the two hospitals

Mean access time
• For each cell in the raster we can
compute the product of the number
of patients and the time required to
reach each hospital
• Dividing this by the number of
patients gives a patient weighted
average time for each hospital

Average travel times for the various specialties. There
isn’t much to chose between specialities with the
exception of paediatric episodes.

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

gather data
integrate data
process data
model data
display results

• All carried out using raster
operations

3: Schizophrenia in Cavan
• In 1996 Pringle, Waddington and
Youssef reported on evidence for the
causes of schizophrenia
• Ecological analysis of variation among
EDs
– Cases varied from 0-11 per ED
– Populations varied from 250-2750

• Poisson probabilities suggested that
one area had an unusually high number
of cases – these can be mapped

Male cases
superimposed on plot
of prevalence by ED

Male cases
superimposed on
rectified Landsat
image with ED
boundaries

4: GMS card holders: Maynooth
•Data on GMS – Medical Card holders
drawn under strict data protection
protocols from DOHC
• Anonymised address lists data matched
with GeoDirectory
•Address matching rate of around 77% in
Maynooth

• Interesting range of results around
average for the whole ED

•ED – single value 14.6 % (national average around 30%)
•Small Area – Range from 2% to 70% - noticeable internal variation and
good indicator of elderly or poor populations

•Strategic value in comparing datasets
•Important in policy analysis

Mapping invites comparison

Role of GIS
• Data integration
– Tally of cases in small areas requires
data on locations of cases and small
area boundaries
– ‘spatial join’ places small area codes
on the case records
– Counting is a standard database
operation

• Data display

Finally
• GIS encourages you to think about
space
• In a ‘conventional’ map of Ireland
areas (EDs, Counties…) are shown in
proportion to their physical size
• There are alternative ways of
displaying maps…

In a cartogram the areas are drawn
proportional to their populations –
this is a “density-equalised” map
projection

A map of the boundaries of counties and
electoral divisions – the areas are drawn
proportional to their physical size

By comparison, the more deprived
western area of Dublin, northern area
of Cork and south east of Limerick are
clearly apparent in the cartogram.

Haase’s deprivation index suggests that the
most deprived populations are in peripheral
rural areas in the north west.

Redrawing the data on the cartogram
reveals wide variation in the SMR, and also
uncovers the urban dimension which is
hidden in the conventional display

Patient data matched to Electoral Divisions –
hospital admissions for psychiatric problems – the
patchwork effect does not suggest any pattern

The cartogram reveals the urban nature
of the problem – those areas which are
more deprived are also those with
higher levels of illness/disability

Residents aged 15+ who are unable to work
through illness or disability: the map pattern
suggests that might be a largely rural
phenomenon

The roads, rail, and urban areas data have
been transformed into the same space as
the cartogram. Dublin and Cork seem
poorly provided with roads…

Other spatial information can be transformed
to the space of the density equalised
projection. Here is the familiar map of main
roads and towns.

Using a cartogram we see that the
incidence of lung cancer is randomly
spatially distributed

Lung cancer in males in New York State [Gastner & Newman: 2004]

Plotting locations of lung cancer cases, we
obtain a map which follows the underlying
population distribution – there appear to be
“clusters”

What about Ireland?
• Potentially hampered by challenge of
geocoding patient records
• Lack of small area population data
– EDs have an average population of 1150
– Wide variation in size (50-28000)
– Socially inhomogenous

Rural addresses
• A major challenge is non-uniqueness
of addresses in rural areas.
• For instance in Moy ED, 11 properties
share the address
‘Lackamore, Lahinch, Co. Clare’

What’s needed
• Postcoding should provide a
mechanism for geocoding patient data
with the granularity required for
planning the delivery of health care –
this has been the case in the UK for a
for long time
• Census data should be made available
for areas with small and similar
populations (cf: UK Output Areas)

Finally…
• GIS has an important role to play
in health studies
• Health data analysis is a rapidly
developing field
• However, in Ireland we are
hampered by data deficiencies –
now is the time for change

Maynooth: image in Google Earth

Central Dublin: image in Google Earth

Taoiseach’s Office: image in Google Earth

Downing Street, London: image in Google Earth

Maynooth: OSi orthophotograph added locally uisng KML

Sellafield: image in Google Earth

NCU – On Line Atlas of Cancer Mortality

The Philippine Institute
for Development
Studies makes an online atlas of socioeconomic indicators
available – contains
some health measures

The future
• Patient empowerment…

• GIS used to merge information from
regulatory agencies on 19196 landfill sites
• Sites buffered to 2km and postcodes
within buffer identified
• Postcoded live births (8.2x106), stillbirths
(43471) and congenital anomalies
(124,597) between 1982 and 1997
• Poisson regression modelling undertaken
• Evidence found for excess risk of
congenital anomaly and low birthweight

• Distance from each
census ward to
nearest hospital
computed
• Map shows number
of patients waiting
longer than 6
months for elective
inpatient care per
available and
unoccupied bed
within 60 minutes
travel time


Slide 50

GIS and Health
Martin Charlton1
Ronan Foley2
Dennis Pringle2
1National

Centre for Geocomputation
2Department of Geography
NUI Maynooth

Outline
• Preliminary observations on GIS
and health
• Examples of GIS in Health:
research and practice
• Mapping people not places

Patterns in Health Data
• Three main areas:
– Mapping of disease incidence
• Relative risk estimation

– Identification of disease clustering
• Surveillance

– Ecological analysis
• Relationship between incidence and
covariates
(Lawson et al, 2003)

Health Data
• Typically patient data is available
in two forms
• Individual – perhaps anonymised
to postcode/zip code level
• Aggregate – counts are available
for a set of zones
• The nature of the data affects the
nature of the analysis

Geographic Information Systems
• GIS is often confused with mapping
• A range of activities which includes
inter alia mapping and analysis
• Will typically involve several datasets
• May also involve several different
pieces of software

Mapping: EpiINFO
• This is a rather simple analysis and
mapping program
• It can be downloaded from the internet
• It doesn’t provide much in the way of
spatial data manipulation…
• … nor are its analytical capabilities very
advanced
• It is easy to use

Spatial Data Manipulation
• GIS isn’t merely concerned with
data display or query
• It’s useful in bringing together
different spatial datasets in the
first stages of an analysis
• This may well require a range of
different geometrical and
database operations

Analysis with Spatial Data
• Statistical analysis usually involves
some separate software.
• Spatial data have some properties
which mean that SPSS isn’t always
appropriate.
• There is now a range of specialist
software – much of it available for
download

GIS may be ideal for data manipulation, display and query,
but it’s not always ideal for analysis – in epidemiology a
variety of analytical software is available … WinBUGS,
GeoBUGS (MRC Biostatistics Unit, Cambridge)

Software to implement
particular methods – for
example scan statistics - can be
downloaded quickly and easily
from the Internet. Kulldorf’s
SaTScan system implements a
range of different scan
statistical models

GIS in Health in Practice
• Facility location – dialysis units
• Facility location – obs & gyn
• Data integration – schizophrenia
in Cavan
• Data manipulation and display –
GMS card holders in Maynooth

1: Provision of Dialysis Units
• There are 5 dialysis units for renal
patients in a region with a population
of about 3,000,000
• Major population centre has two units
about 5 miles apart
• Where would you locate an extra one?
• What would happen if one was
closed?

Location-allocation
• Location-allocation problems deal
with optimum location of facilities
• Attempt to minimise total
weighted travel time/distance
from users to facilities
• What data is needed?

Data requirements
• Digitised road network with classification
of road types
• Mean travel speeds on each type for
urban/non-urban roads
• Extents of urban areas
• Anonymised postcoded patient records
• Postcode->grid reference lookup
• Locations of hospitals in region (both with
and without dialysis units)

GIS processing
• Data integration
• Determine which segments of road network
are urban
• Determine travel times using road type
urban/non-urban speeds
• Tally numbers of patients at each node in
the road network
• Find nearest nodes in network to each
hospital
• Code hospitals as ‘existing’ or ‘potential’
units

Modelling/Analysis
• Carried out using standard GIS
functions
• Convenient ‘front end’ written
using system’s macro language
(save a lot of typing)
• Explore a range of alternatives

Hospital with
dialysis unit

Study region showing hospital locations and urban areas

Patients requiring dialysis (tallied to road nodes)

Classified road network – includes roads outside study area

•

Some alternative
scenarios:

(a) Optimal allocation
of patients
(b) Provide 1 extra
unit
(c) Provide 2 extra
units

(d) Close 1 unit in
city and reallocate
its patients

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

integrate data
process data
model and analyse
generate alternative scenarios
display the results

2: Facility Location
• Two hospitals both provided facilities
for obstetrics, gynaecology, paediatrics
and a special care baby unit.
• Was there a case for rationalisation and
improving the facilities at one hospital
only?
• Would closure disadvantage any
patients?

Data
• Locations of hospitals in study region
• Anonymised postcoded finished
consultant episodes for each of the
four specialities at each hospital
• Classified road network
• Speeds associated with each road
type

Data preparation
• Postcoded records allocated grid
references using Central Postcode
Directory (POSTZON)
• Vector data converted to raster
• Patient/specialty/hospital counts
produced for each raster
• Passage times computed for each
raster cell based on road
classification and speed estimates

Roads, urban areas, patient locations, hospitals in study area

Processing
• Travel time surfaces computed for
each hospital
• Each raster cell contains time
required to reach hospital in minutes
• Assumed walking speed in ‘non road’
areas

Travel time surface for hospital S – darker = shorter time

Travel time surface for hospital T

Time differences
• If we subtract the travel time surfaces areas
nearer one hospital will have positive
values for the difference, and areas nearer
the other will have negative ones
• Boundary between positive and negative
values is boundary of catchment for each
hospital.
• If you’re on the boundary (difference is
zero), it doesn’t matter which hospital you
choose

Roads, patient locations, hospital catchments

Indifference
• If we identify those rasters where the time
difference (ignoring the sign) is, say, 5 –
people in those areas are in the 5 minute
‘indifference’ zone.
• Patients might not be worried about a 5
minute increase in their travel time, they
might be worried about 30 minutes
• Indifference zones can be computed quite
easily

15-minute indifference zone between the two hospitals

Mean access time
• For each cell in the raster we can
compute the product of the number
of patients and the time required to
reach each hospital
• Dividing this by the number of
patients gives a patient weighted
average time for each hospital

Average travel times for the various specialties. There
isn’t much to chose between specialities with the
exception of paediatric episodes.

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

gather data
integrate data
process data
model data
display results

• All carried out using raster
operations

3: Schizophrenia in Cavan
• In 1996 Pringle, Waddington and
Youssef reported on evidence for the
causes of schizophrenia
• Ecological analysis of variation among
EDs
– Cases varied from 0-11 per ED
– Populations varied from 250-2750

• Poisson probabilities suggested that
one area had an unusually high number
of cases – these can be mapped

Male cases
superimposed on plot
of prevalence by ED

Male cases
superimposed on
rectified Landsat
image with ED
boundaries

4: GMS card holders: Maynooth
•Data on GMS – Medical Card holders
drawn under strict data protection
protocols from DOHC
• Anonymised address lists data matched
with GeoDirectory
•Address matching rate of around 77% in
Maynooth

• Interesting range of results around
average for the whole ED

•ED – single value 14.6 % (national average around 30%)
•Small Area – Range from 2% to 70% - noticeable internal variation and
good indicator of elderly or poor populations

•Strategic value in comparing datasets
•Important in policy analysis

Mapping invites comparison

Role of GIS
• Data integration
– Tally of cases in small areas requires
data on locations of cases and small
area boundaries
– ‘spatial join’ places small area codes
on the case records
– Counting is a standard database
operation

• Data display

Finally
• GIS encourages you to think about
space
• In a ‘conventional’ map of Ireland
areas (EDs, Counties…) are shown in
proportion to their physical size
• There are alternative ways of
displaying maps…

In a cartogram the areas are drawn
proportional to their populations –
this is a “density-equalised” map
projection

A map of the boundaries of counties and
electoral divisions – the areas are drawn
proportional to their physical size

By comparison, the more deprived
western area of Dublin, northern area
of Cork and south east of Limerick are
clearly apparent in the cartogram.

Haase’s deprivation index suggests that the
most deprived populations are in peripheral
rural areas in the north west.

Redrawing the data on the cartogram
reveals wide variation in the SMR, and also
uncovers the urban dimension which is
hidden in the conventional display

Patient data matched to Electoral Divisions –
hospital admissions for psychiatric problems – the
patchwork effect does not suggest any pattern

The cartogram reveals the urban nature
of the problem – those areas which are
more deprived are also those with
higher levels of illness/disability

Residents aged 15+ who are unable to work
through illness or disability: the map pattern
suggests that might be a largely rural
phenomenon

The roads, rail, and urban areas data have
been transformed into the same space as
the cartogram. Dublin and Cork seem
poorly provided with roads…

Other spatial information can be transformed
to the space of the density equalised
projection. Here is the familiar map of main
roads and towns.

Using a cartogram we see that the
incidence of lung cancer is randomly
spatially distributed

Lung cancer in males in New York State [Gastner & Newman: 2004]

Plotting locations of lung cancer cases, we
obtain a map which follows the underlying
population distribution – there appear to be
“clusters”

What about Ireland?
• Potentially hampered by challenge of
geocoding patient records
• Lack of small area population data
– EDs have an average population of 1150
– Wide variation in size (50-28000)
– Socially inhomogenous

Rural addresses
• A major challenge is non-uniqueness
of addresses in rural areas.
• For instance in Moy ED, 11 properties
share the address
‘Lackamore, Lahinch, Co. Clare’

What’s needed
• Postcoding should provide a
mechanism for geocoding patient data
with the granularity required for
planning the delivery of health care –
this has been the case in the UK for a
for long time
• Census data should be made available
for areas with small and similar
populations (cf: UK Output Areas)

Finally…
• GIS has an important role to play
in health studies
• Health data analysis is a rapidly
developing field
• However, in Ireland we are
hampered by data deficiencies –
now is the time for change

Maynooth: image in Google Earth

Central Dublin: image in Google Earth

Taoiseach’s Office: image in Google Earth

Downing Street, London: image in Google Earth

Maynooth: OSi orthophotograph added locally uisng KML

Sellafield: image in Google Earth

NCU – On Line Atlas of Cancer Mortality

The Philippine Institute
for Development
Studies makes an online atlas of socioeconomic indicators
available – contains
some health measures

The future
• Patient empowerment…

• GIS used to merge information from
regulatory agencies on 19196 landfill sites
• Sites buffered to 2km and postcodes
within buffer identified
• Postcoded live births (8.2x106), stillbirths
(43471) and congenital anomalies
(124,597) between 1982 and 1997
• Poisson regression modelling undertaken
• Evidence found for excess risk of
congenital anomaly and low birthweight

• Distance from each
census ward to
nearest hospital
computed
• Map shows number
of patients waiting
longer than 6
months for elective
inpatient care per
available and
unoccupied bed
within 60 minutes
travel time


Slide 51

GIS and Health
Martin Charlton1
Ronan Foley2
Dennis Pringle2
1National

Centre for Geocomputation
2Department of Geography
NUI Maynooth

Outline
• Preliminary observations on GIS
and health
• Examples of GIS in Health:
research and practice
• Mapping people not places

Patterns in Health Data
• Three main areas:
– Mapping of disease incidence
• Relative risk estimation

– Identification of disease clustering
• Surveillance

– Ecological analysis
• Relationship between incidence and
covariates
(Lawson et al, 2003)

Health Data
• Typically patient data is available
in two forms
• Individual – perhaps anonymised
to postcode/zip code level
• Aggregate – counts are available
for a set of zones
• The nature of the data affects the
nature of the analysis

Geographic Information Systems
• GIS is often confused with mapping
• A range of activities which includes
inter alia mapping and analysis
• Will typically involve several datasets
• May also involve several different
pieces of software

Mapping: EpiINFO
• This is a rather simple analysis and
mapping program
• It can be downloaded from the internet
• It doesn’t provide much in the way of
spatial data manipulation…
• … nor are its analytical capabilities very
advanced
• It is easy to use

Spatial Data Manipulation
• GIS isn’t merely concerned with
data display or query
• It’s useful in bringing together
different spatial datasets in the
first stages of an analysis
• This may well require a range of
different geometrical and
database operations

Analysis with Spatial Data
• Statistical analysis usually involves
some separate software.
• Spatial data have some properties
which mean that SPSS isn’t always
appropriate.
• There is now a range of specialist
software – much of it available for
download

GIS may be ideal for data manipulation, display and query,
but it’s not always ideal for analysis – in epidemiology a
variety of analytical software is available … WinBUGS,
GeoBUGS (MRC Biostatistics Unit, Cambridge)

Software to implement
particular methods – for
example scan statistics - can be
downloaded quickly and easily
from the Internet. Kulldorf’s
SaTScan system implements a
range of different scan
statistical models

GIS in Health in Practice
• Facility location – dialysis units
• Facility location – obs & gyn
• Data integration – schizophrenia
in Cavan
• Data manipulation and display –
GMS card holders in Maynooth

1: Provision of Dialysis Units
• There are 5 dialysis units for renal
patients in a region with a population
of about 3,000,000
• Major population centre has two units
about 5 miles apart
• Where would you locate an extra one?
• What would happen if one was
closed?

Location-allocation
• Location-allocation problems deal
with optimum location of facilities
• Attempt to minimise total
weighted travel time/distance
from users to facilities
• What data is needed?

Data requirements
• Digitised road network with classification
of road types
• Mean travel speeds on each type for
urban/non-urban roads
• Extents of urban areas
• Anonymised postcoded patient records
• Postcode->grid reference lookup
• Locations of hospitals in region (both with
and without dialysis units)

GIS processing
• Data integration
• Determine which segments of road network
are urban
• Determine travel times using road type
urban/non-urban speeds
• Tally numbers of patients at each node in
the road network
• Find nearest nodes in network to each
hospital
• Code hospitals as ‘existing’ or ‘potential’
units

Modelling/Analysis
• Carried out using standard GIS
functions
• Convenient ‘front end’ written
using system’s macro language
(save a lot of typing)
• Explore a range of alternatives

Hospital with
dialysis unit

Study region showing hospital locations and urban areas

Patients requiring dialysis (tallied to road nodes)

Classified road network – includes roads outside study area

•

Some alternative
scenarios:

(a) Optimal allocation
of patients
(b) Provide 1 extra
unit
(c) Provide 2 extra
units

(d) Close 1 unit in
city and reallocate
its patients

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

integrate data
process data
model and analyse
generate alternative scenarios
display the results

2: Facility Location
• Two hospitals both provided facilities
for obstetrics, gynaecology, paediatrics
and a special care baby unit.
• Was there a case for rationalisation and
improving the facilities at one hospital
only?
• Would closure disadvantage any
patients?

Data
• Locations of hospitals in study region
• Anonymised postcoded finished
consultant episodes for each of the
four specialities at each hospital
• Classified road network
• Speeds associated with each road
type

Data preparation
• Postcoded records allocated grid
references using Central Postcode
Directory (POSTZON)
• Vector data converted to raster
• Patient/specialty/hospital counts
produced for each raster
• Passage times computed for each
raster cell based on road
classification and speed estimates

Roads, urban areas, patient locations, hospitals in study area

Processing
• Travel time surfaces computed for
each hospital
• Each raster cell contains time
required to reach hospital in minutes
• Assumed walking speed in ‘non road’
areas

Travel time surface for hospital S – darker = shorter time

Travel time surface for hospital T

Time differences
• If we subtract the travel time surfaces areas
nearer one hospital will have positive
values for the difference, and areas nearer
the other will have negative ones
• Boundary between positive and negative
values is boundary of catchment for each
hospital.
• If you’re on the boundary (difference is
zero), it doesn’t matter which hospital you
choose

Roads, patient locations, hospital catchments

Indifference
• If we identify those rasters where the time
difference (ignoring the sign) is, say, 5 –
people in those areas are in the 5 minute
‘indifference’ zone.
• Patients might not be worried about a 5
minute increase in their travel time, they
might be worried about 30 minutes
• Indifference zones can be computed quite
easily

15-minute indifference zone between the two hospitals

Mean access time
• For each cell in the raster we can
compute the product of the number
of patients and the time required to
reach each hospital
• Dividing this by the number of
patients gives a patient weighted
average time for each hospital

Average travel times for the various specialties. There
isn’t much to chose between specialities with the
exception of paediatric episodes.

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

gather data
integrate data
process data
model data
display results

• All carried out using raster
operations

3: Schizophrenia in Cavan
• In 1996 Pringle, Waddington and
Youssef reported on evidence for the
causes of schizophrenia
• Ecological analysis of variation among
EDs
– Cases varied from 0-11 per ED
– Populations varied from 250-2750

• Poisson probabilities suggested that
one area had an unusually high number
of cases – these can be mapped

Male cases
superimposed on plot
of prevalence by ED

Male cases
superimposed on
rectified Landsat
image with ED
boundaries

4: GMS card holders: Maynooth
•Data on GMS – Medical Card holders
drawn under strict data protection
protocols from DOHC
• Anonymised address lists data matched
with GeoDirectory
•Address matching rate of around 77% in
Maynooth

• Interesting range of results around
average for the whole ED

•ED – single value 14.6 % (national average around 30%)
•Small Area – Range from 2% to 70% - noticeable internal variation and
good indicator of elderly or poor populations

•Strategic value in comparing datasets
•Important in policy analysis

Mapping invites comparison

Role of GIS
• Data integration
– Tally of cases in small areas requires
data on locations of cases and small
area boundaries
– ‘spatial join’ places small area codes
on the case records
– Counting is a standard database
operation

• Data display

Finally
• GIS encourages you to think about
space
• In a ‘conventional’ map of Ireland
areas (EDs, Counties…) are shown in
proportion to their physical size
• There are alternative ways of
displaying maps…

In a cartogram the areas are drawn
proportional to their populations –
this is a “density-equalised” map
projection

A map of the boundaries of counties and
electoral divisions – the areas are drawn
proportional to their physical size

By comparison, the more deprived
western area of Dublin, northern area
of Cork and south east of Limerick are
clearly apparent in the cartogram.

Haase’s deprivation index suggests that the
most deprived populations are in peripheral
rural areas in the north west.

Redrawing the data on the cartogram
reveals wide variation in the SMR, and also
uncovers the urban dimension which is
hidden in the conventional display

Patient data matched to Electoral Divisions –
hospital admissions for psychiatric problems – the
patchwork effect does not suggest any pattern

The cartogram reveals the urban nature
of the problem – those areas which are
more deprived are also those with
higher levels of illness/disability

Residents aged 15+ who are unable to work
through illness or disability: the map pattern
suggests that might be a largely rural
phenomenon

The roads, rail, and urban areas data have
been transformed into the same space as
the cartogram. Dublin and Cork seem
poorly provided with roads…

Other spatial information can be transformed
to the space of the density equalised
projection. Here is the familiar map of main
roads and towns.

Using a cartogram we see that the
incidence of lung cancer is randomly
spatially distributed

Lung cancer in males in New York State [Gastner & Newman: 2004]

Plotting locations of lung cancer cases, we
obtain a map which follows the underlying
population distribution – there appear to be
“clusters”

What about Ireland?
• Potentially hampered by challenge of
geocoding patient records
• Lack of small area population data
– EDs have an average population of 1150
– Wide variation in size (50-28000)
– Socially inhomogenous

Rural addresses
• A major challenge is non-uniqueness
of addresses in rural areas.
• For instance in Moy ED, 11 properties
share the address
‘Lackamore, Lahinch, Co. Clare’

What’s needed
• Postcoding should provide a
mechanism for geocoding patient data
with the granularity required for
planning the delivery of health care –
this has been the case in the UK for a
for long time
• Census data should be made available
for areas with small and similar
populations (cf: UK Output Areas)

Finally…
• GIS has an important role to play
in health studies
• Health data analysis is a rapidly
developing field
• However, in Ireland we are
hampered by data deficiencies –
now is the time for change

Maynooth: image in Google Earth

Central Dublin: image in Google Earth

Taoiseach’s Office: image in Google Earth

Downing Street, London: image in Google Earth

Maynooth: OSi orthophotograph added locally uisng KML

Sellafield: image in Google Earth

NCU – On Line Atlas of Cancer Mortality

The Philippine Institute
for Development
Studies makes an online atlas of socioeconomic indicators
available – contains
some health measures

The future
• Patient empowerment…

• GIS used to merge information from
regulatory agencies on 19196 landfill sites
• Sites buffered to 2km and postcodes
within buffer identified
• Postcoded live births (8.2x106), stillbirths
(43471) and congenital anomalies
(124,597) between 1982 and 1997
• Poisson regression modelling undertaken
• Evidence found for excess risk of
congenital anomaly and low birthweight

• Distance from each
census ward to
nearest hospital
computed
• Map shows number
of patients waiting
longer than 6
months for elective
inpatient care per
available and
unoccupied bed
within 60 minutes
travel time


Slide 52

GIS and Health
Martin Charlton1
Ronan Foley2
Dennis Pringle2
1National

Centre for Geocomputation
2Department of Geography
NUI Maynooth

Outline
• Preliminary observations on GIS
and health
• Examples of GIS in Health:
research and practice
• Mapping people not places

Patterns in Health Data
• Three main areas:
– Mapping of disease incidence
• Relative risk estimation

– Identification of disease clustering
• Surveillance

– Ecological analysis
• Relationship between incidence and
covariates
(Lawson et al, 2003)

Health Data
• Typically patient data is available
in two forms
• Individual – perhaps anonymised
to postcode/zip code level
• Aggregate – counts are available
for a set of zones
• The nature of the data affects the
nature of the analysis

Geographic Information Systems
• GIS is often confused with mapping
• A range of activities which includes
inter alia mapping and analysis
• Will typically involve several datasets
• May also involve several different
pieces of software

Mapping: EpiINFO
• This is a rather simple analysis and
mapping program
• It can be downloaded from the internet
• It doesn’t provide much in the way of
spatial data manipulation…
• … nor are its analytical capabilities very
advanced
• It is easy to use

Spatial Data Manipulation
• GIS isn’t merely concerned with
data display or query
• It’s useful in bringing together
different spatial datasets in the
first stages of an analysis
• This may well require a range of
different geometrical and
database operations

Analysis with Spatial Data
• Statistical analysis usually involves
some separate software.
• Spatial data have some properties
which mean that SPSS isn’t always
appropriate.
• There is now a range of specialist
software – much of it available for
download

GIS may be ideal for data manipulation, display and query,
but it’s not always ideal for analysis – in epidemiology a
variety of analytical software is available … WinBUGS,
GeoBUGS (MRC Biostatistics Unit, Cambridge)

Software to implement
particular methods – for
example scan statistics - can be
downloaded quickly and easily
from the Internet. Kulldorf’s
SaTScan system implements a
range of different scan
statistical models

GIS in Health in Practice
• Facility location – dialysis units
• Facility location – obs & gyn
• Data integration – schizophrenia
in Cavan
• Data manipulation and display –
GMS card holders in Maynooth

1: Provision of Dialysis Units
• There are 5 dialysis units for renal
patients in a region with a population
of about 3,000,000
• Major population centre has two units
about 5 miles apart
• Where would you locate an extra one?
• What would happen if one was
closed?

Location-allocation
• Location-allocation problems deal
with optimum location of facilities
• Attempt to minimise total
weighted travel time/distance
from users to facilities
• What data is needed?

Data requirements
• Digitised road network with classification
of road types
• Mean travel speeds on each type for
urban/non-urban roads
• Extents of urban areas
• Anonymised postcoded patient records
• Postcode->grid reference lookup
• Locations of hospitals in region (both with
and without dialysis units)

GIS processing
• Data integration
• Determine which segments of road network
are urban
• Determine travel times using road type
urban/non-urban speeds
• Tally numbers of patients at each node in
the road network
• Find nearest nodes in network to each
hospital
• Code hospitals as ‘existing’ or ‘potential’
units

Modelling/Analysis
• Carried out using standard GIS
functions
• Convenient ‘front end’ written
using system’s macro language
(save a lot of typing)
• Explore a range of alternatives

Hospital with
dialysis unit

Study region showing hospital locations and urban areas

Patients requiring dialysis (tallied to road nodes)

Classified road network – includes roads outside study area

•

Some alternative
scenarios:

(a) Optimal allocation
of patients
(b) Provide 1 extra
unit
(c) Provide 2 extra
units

(d) Close 1 unit in
city and reallocate
its patients

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

integrate data
process data
model and analyse
generate alternative scenarios
display the results

2: Facility Location
• Two hospitals both provided facilities
for obstetrics, gynaecology, paediatrics
and a special care baby unit.
• Was there a case for rationalisation and
improving the facilities at one hospital
only?
• Would closure disadvantage any
patients?

Data
• Locations of hospitals in study region
• Anonymised postcoded finished
consultant episodes for each of the
four specialities at each hospital
• Classified road network
• Speeds associated with each road
type

Data preparation
• Postcoded records allocated grid
references using Central Postcode
Directory (POSTZON)
• Vector data converted to raster
• Patient/specialty/hospital counts
produced for each raster
• Passage times computed for each
raster cell based on road
classification and speed estimates

Roads, urban areas, patient locations, hospitals in study area

Processing
• Travel time surfaces computed for
each hospital
• Each raster cell contains time
required to reach hospital in minutes
• Assumed walking speed in ‘non road’
areas

Travel time surface for hospital S – darker = shorter time

Travel time surface for hospital T

Time differences
• If we subtract the travel time surfaces areas
nearer one hospital will have positive
values for the difference, and areas nearer
the other will have negative ones
• Boundary between positive and negative
values is boundary of catchment for each
hospital.
• If you’re on the boundary (difference is
zero), it doesn’t matter which hospital you
choose

Roads, patient locations, hospital catchments

Indifference
• If we identify those rasters where the time
difference (ignoring the sign) is, say, 5 –
people in those areas are in the 5 minute
‘indifference’ zone.
• Patients might not be worried about a 5
minute increase in their travel time, they
might be worried about 30 minutes
• Indifference zones can be computed quite
easily

15-minute indifference zone between the two hospitals

Mean access time
• For each cell in the raster we can
compute the product of the number
of patients and the time required to
reach each hospital
• Dividing this by the number of
patients gives a patient weighted
average time for each hospital

Average travel times for the various specialties. There
isn’t much to chose between specialities with the
exception of paediatric episodes.

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

gather data
integrate data
process data
model data
display results

• All carried out using raster
operations

3: Schizophrenia in Cavan
• In 1996 Pringle, Waddington and
Youssef reported on evidence for the
causes of schizophrenia
• Ecological analysis of variation among
EDs
– Cases varied from 0-11 per ED
– Populations varied from 250-2750

• Poisson probabilities suggested that
one area had an unusually high number
of cases – these can be mapped

Male cases
superimposed on plot
of prevalence by ED

Male cases
superimposed on
rectified Landsat
image with ED
boundaries

4: GMS card holders: Maynooth
•Data on GMS – Medical Card holders
drawn under strict data protection
protocols from DOHC
• Anonymised address lists data matched
with GeoDirectory
•Address matching rate of around 77% in
Maynooth

• Interesting range of results around
average for the whole ED

•ED – single value 14.6 % (national average around 30%)
•Small Area – Range from 2% to 70% - noticeable internal variation and
good indicator of elderly or poor populations

•Strategic value in comparing datasets
•Important in policy analysis

Mapping invites comparison

Role of GIS
• Data integration
– Tally of cases in small areas requires
data on locations of cases and small
area boundaries
– ‘spatial join’ places small area codes
on the case records
– Counting is a standard database
operation

• Data display

Finally
• GIS encourages you to think about
space
• In a ‘conventional’ map of Ireland
areas (EDs, Counties…) are shown in
proportion to their physical size
• There are alternative ways of
displaying maps…

In a cartogram the areas are drawn
proportional to their populations –
this is a “density-equalised” map
projection

A map of the boundaries of counties and
electoral divisions – the areas are drawn
proportional to their physical size

By comparison, the more deprived
western area of Dublin, northern area
of Cork and south east of Limerick are
clearly apparent in the cartogram.

Haase’s deprivation index suggests that the
most deprived populations are in peripheral
rural areas in the north west.

Redrawing the data on the cartogram
reveals wide variation in the SMR, and also
uncovers the urban dimension which is
hidden in the conventional display

Patient data matched to Electoral Divisions –
hospital admissions for psychiatric problems – the
patchwork effect does not suggest any pattern

The cartogram reveals the urban nature
of the problem – those areas which are
more deprived are also those with
higher levels of illness/disability

Residents aged 15+ who are unable to work
through illness or disability: the map pattern
suggests that might be a largely rural
phenomenon

The roads, rail, and urban areas data have
been transformed into the same space as
the cartogram. Dublin and Cork seem
poorly provided with roads…

Other spatial information can be transformed
to the space of the density equalised
projection. Here is the familiar map of main
roads and towns.

Using a cartogram we see that the
incidence of lung cancer is randomly
spatially distributed

Lung cancer in males in New York State [Gastner & Newman: 2004]

Plotting locations of lung cancer cases, we
obtain a map which follows the underlying
population distribution – there appear to be
“clusters”

What about Ireland?
• Potentially hampered by challenge of
geocoding patient records
• Lack of small area population data
– EDs have an average population of 1150
– Wide variation in size (50-28000)
– Socially inhomogenous

Rural addresses
• A major challenge is non-uniqueness
of addresses in rural areas.
• For instance in Moy ED, 11 properties
share the address
‘Lackamore, Lahinch, Co. Clare’

What’s needed
• Postcoding should provide a
mechanism for geocoding patient data
with the granularity required for
planning the delivery of health care –
this has been the case in the UK for a
for long time
• Census data should be made available
for areas with small and similar
populations (cf: UK Output Areas)

Finally…
• GIS has an important role to play
in health studies
• Health data analysis is a rapidly
developing field
• However, in Ireland we are
hampered by data deficiencies –
now is the time for change

Maynooth: image in Google Earth

Central Dublin: image in Google Earth

Taoiseach’s Office: image in Google Earth

Downing Street, London: image in Google Earth

Maynooth: OSi orthophotograph added locally uisng KML

Sellafield: image in Google Earth

NCU – On Line Atlas of Cancer Mortality

The Philippine Institute
for Development
Studies makes an online atlas of socioeconomic indicators
available – contains
some health measures

The future
• Patient empowerment…

• GIS used to merge information from
regulatory agencies on 19196 landfill sites
• Sites buffered to 2km and postcodes
within buffer identified
• Postcoded live births (8.2x106), stillbirths
(43471) and congenital anomalies
(124,597) between 1982 and 1997
• Poisson regression modelling undertaken
• Evidence found for excess risk of
congenital anomaly and low birthweight

• Distance from each
census ward to
nearest hospital
computed
• Map shows number
of patients waiting
longer than 6
months for elective
inpatient care per
available and
unoccupied bed
within 60 minutes
travel time


Slide 53

GIS and Health
Martin Charlton1
Ronan Foley2
Dennis Pringle2
1National

Centre for Geocomputation
2Department of Geography
NUI Maynooth

Outline
• Preliminary observations on GIS
and health
• Examples of GIS in Health:
research and practice
• Mapping people not places

Patterns in Health Data
• Three main areas:
– Mapping of disease incidence
• Relative risk estimation

– Identification of disease clustering
• Surveillance

– Ecological analysis
• Relationship between incidence and
covariates
(Lawson et al, 2003)

Health Data
• Typically patient data is available
in two forms
• Individual – perhaps anonymised
to postcode/zip code level
• Aggregate – counts are available
for a set of zones
• The nature of the data affects the
nature of the analysis

Geographic Information Systems
• GIS is often confused with mapping
• A range of activities which includes
inter alia mapping and analysis
• Will typically involve several datasets
• May also involve several different
pieces of software

Mapping: EpiINFO
• This is a rather simple analysis and
mapping program
• It can be downloaded from the internet
• It doesn’t provide much in the way of
spatial data manipulation…
• … nor are its analytical capabilities very
advanced
• It is easy to use

Spatial Data Manipulation
• GIS isn’t merely concerned with
data display or query
• It’s useful in bringing together
different spatial datasets in the
first stages of an analysis
• This may well require a range of
different geometrical and
database operations

Analysis with Spatial Data
• Statistical analysis usually involves
some separate software.
• Spatial data have some properties
which mean that SPSS isn’t always
appropriate.
• There is now a range of specialist
software – much of it available for
download

GIS may be ideal for data manipulation, display and query,
but it’s not always ideal for analysis – in epidemiology a
variety of analytical software is available … WinBUGS,
GeoBUGS (MRC Biostatistics Unit, Cambridge)

Software to implement
particular methods – for
example scan statistics - can be
downloaded quickly and easily
from the Internet. Kulldorf’s
SaTScan system implements a
range of different scan
statistical models

GIS in Health in Practice
• Facility location – dialysis units
• Facility location – obs & gyn
• Data integration – schizophrenia
in Cavan
• Data manipulation and display –
GMS card holders in Maynooth

1: Provision of Dialysis Units
• There are 5 dialysis units for renal
patients in a region with a population
of about 3,000,000
• Major population centre has two units
about 5 miles apart
• Where would you locate an extra one?
• What would happen if one was
closed?

Location-allocation
• Location-allocation problems deal
with optimum location of facilities
• Attempt to minimise total
weighted travel time/distance
from users to facilities
• What data is needed?

Data requirements
• Digitised road network with classification
of road types
• Mean travel speeds on each type for
urban/non-urban roads
• Extents of urban areas
• Anonymised postcoded patient records
• Postcode->grid reference lookup
• Locations of hospitals in region (both with
and without dialysis units)

GIS processing
• Data integration
• Determine which segments of road network
are urban
• Determine travel times using road type
urban/non-urban speeds
• Tally numbers of patients at each node in
the road network
• Find nearest nodes in network to each
hospital
• Code hospitals as ‘existing’ or ‘potential’
units

Modelling/Analysis
• Carried out using standard GIS
functions
• Convenient ‘front end’ written
using system’s macro language
(save a lot of typing)
• Explore a range of alternatives

Hospital with
dialysis unit

Study region showing hospital locations and urban areas

Patients requiring dialysis (tallied to road nodes)

Classified road network – includes roads outside study area

•

Some alternative
scenarios:

(a) Optimal allocation
of patients
(b) Provide 1 extra
unit
(c) Provide 2 extra
units

(d) Close 1 unit in
city and reallocate
its patients

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

integrate data
process data
model and analyse
generate alternative scenarios
display the results

2: Facility Location
• Two hospitals both provided facilities
for obstetrics, gynaecology, paediatrics
and a special care baby unit.
• Was there a case for rationalisation and
improving the facilities at one hospital
only?
• Would closure disadvantage any
patients?

Data
• Locations of hospitals in study region
• Anonymised postcoded finished
consultant episodes for each of the
four specialities at each hospital
• Classified road network
• Speeds associated with each road
type

Data preparation
• Postcoded records allocated grid
references using Central Postcode
Directory (POSTZON)
• Vector data converted to raster
• Patient/specialty/hospital counts
produced for each raster
• Passage times computed for each
raster cell based on road
classification and speed estimates

Roads, urban areas, patient locations, hospitals in study area

Processing
• Travel time surfaces computed for
each hospital
• Each raster cell contains time
required to reach hospital in minutes
• Assumed walking speed in ‘non road’
areas

Travel time surface for hospital S – darker = shorter time

Travel time surface for hospital T

Time differences
• If we subtract the travel time surfaces areas
nearer one hospital will have positive
values for the difference, and areas nearer
the other will have negative ones
• Boundary between positive and negative
values is boundary of catchment for each
hospital.
• If you’re on the boundary (difference is
zero), it doesn’t matter which hospital you
choose

Roads, patient locations, hospital catchments

Indifference
• If we identify those rasters where the time
difference (ignoring the sign) is, say, 5 –
people in those areas are in the 5 minute
‘indifference’ zone.
• Patients might not be worried about a 5
minute increase in their travel time, they
might be worried about 30 minutes
• Indifference zones can be computed quite
easily

15-minute indifference zone between the two hospitals

Mean access time
• For each cell in the raster we can
compute the product of the number
of patients and the time required to
reach each hospital
• Dividing this by the number of
patients gives a patient weighted
average time for each hospital

Average travel times for the various specialties. There
isn’t much to chose between specialities with the
exception of paediatric episodes.

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

gather data
integrate data
process data
model data
display results

• All carried out using raster
operations

3: Schizophrenia in Cavan
• In 1996 Pringle, Waddington and
Youssef reported on evidence for the
causes of schizophrenia
• Ecological analysis of variation among
EDs
– Cases varied from 0-11 per ED
– Populations varied from 250-2750

• Poisson probabilities suggested that
one area had an unusually high number
of cases – these can be mapped

Male cases
superimposed on plot
of prevalence by ED

Male cases
superimposed on
rectified Landsat
image with ED
boundaries

4: GMS card holders: Maynooth
•Data on GMS – Medical Card holders
drawn under strict data protection
protocols from DOHC
• Anonymised address lists data matched
with GeoDirectory
•Address matching rate of around 77% in
Maynooth

• Interesting range of results around
average for the whole ED

•ED – single value 14.6 % (national average around 30%)
•Small Area – Range from 2% to 70% - noticeable internal variation and
good indicator of elderly or poor populations

•Strategic value in comparing datasets
•Important in policy analysis

Mapping invites comparison

Role of GIS
• Data integration
– Tally of cases in small areas requires
data on locations of cases and small
area boundaries
– ‘spatial join’ places small area codes
on the case records
– Counting is a standard database
operation

• Data display

Finally
• GIS encourages you to think about
space
• In a ‘conventional’ map of Ireland
areas (EDs, Counties…) are shown in
proportion to their physical size
• There are alternative ways of
displaying maps…

In a cartogram the areas are drawn
proportional to their populations –
this is a “density-equalised” map
projection

A map of the boundaries of counties and
electoral divisions – the areas are drawn
proportional to their physical size

By comparison, the more deprived
western area of Dublin, northern area
of Cork and south east of Limerick are
clearly apparent in the cartogram.

Haase’s deprivation index suggests that the
most deprived populations are in peripheral
rural areas in the north west.

Redrawing the data on the cartogram
reveals wide variation in the SMR, and also
uncovers the urban dimension which is
hidden in the conventional display

Patient data matched to Electoral Divisions –
hospital admissions for psychiatric problems – the
patchwork effect does not suggest any pattern

The cartogram reveals the urban nature
of the problem – those areas which are
more deprived are also those with
higher levels of illness/disability

Residents aged 15+ who are unable to work
through illness or disability: the map pattern
suggests that might be a largely rural
phenomenon

The roads, rail, and urban areas data have
been transformed into the same space as
the cartogram. Dublin and Cork seem
poorly provided with roads…

Other spatial information can be transformed
to the space of the density equalised
projection. Here is the familiar map of main
roads and towns.

Using a cartogram we see that the
incidence of lung cancer is randomly
spatially distributed

Lung cancer in males in New York State [Gastner & Newman: 2004]

Plotting locations of lung cancer cases, we
obtain a map which follows the underlying
population distribution – there appear to be
“clusters”

What about Ireland?
• Potentially hampered by challenge of
geocoding patient records
• Lack of small area population data
– EDs have an average population of 1150
– Wide variation in size (50-28000)
– Socially inhomogenous

Rural addresses
• A major challenge is non-uniqueness
of addresses in rural areas.
• For instance in Moy ED, 11 properties
share the address
‘Lackamore, Lahinch, Co. Clare’

What’s needed
• Postcoding should provide a
mechanism for geocoding patient data
with the granularity required for
planning the delivery of health care –
this has been the case in the UK for a
for long time
• Census data should be made available
for areas with small and similar
populations (cf: UK Output Areas)

Finally…
• GIS has an important role to play
in health studies
• Health data analysis is a rapidly
developing field
• However, in Ireland we are
hampered by data deficiencies –
now is the time for change

Maynooth: image in Google Earth

Central Dublin: image in Google Earth

Taoiseach’s Office: image in Google Earth

Downing Street, London: image in Google Earth

Maynooth: OSi orthophotograph added locally uisng KML

Sellafield: image in Google Earth

NCU – On Line Atlas of Cancer Mortality

The Philippine Institute
for Development
Studies makes an online atlas of socioeconomic indicators
available – contains
some health measures

The future
• Patient empowerment…

• GIS used to merge information from
regulatory agencies on 19196 landfill sites
• Sites buffered to 2km and postcodes
within buffer identified
• Postcoded live births (8.2x106), stillbirths
(43471) and congenital anomalies
(124,597) between 1982 and 1997
• Poisson regression modelling undertaken
• Evidence found for excess risk of
congenital anomaly and low birthweight

• Distance from each
census ward to
nearest hospital
computed
• Map shows number
of patients waiting
longer than 6
months for elective
inpatient care per
available and
unoccupied bed
within 60 minutes
travel time


Slide 54

GIS and Health
Martin Charlton1
Ronan Foley2
Dennis Pringle2
1National

Centre for Geocomputation
2Department of Geography
NUI Maynooth

Outline
• Preliminary observations on GIS
and health
• Examples of GIS in Health:
research and practice
• Mapping people not places

Patterns in Health Data
• Three main areas:
– Mapping of disease incidence
• Relative risk estimation

– Identification of disease clustering
• Surveillance

– Ecological analysis
• Relationship between incidence and
covariates
(Lawson et al, 2003)

Health Data
• Typically patient data is available
in two forms
• Individual – perhaps anonymised
to postcode/zip code level
• Aggregate – counts are available
for a set of zones
• The nature of the data affects the
nature of the analysis

Geographic Information Systems
• GIS is often confused with mapping
• A range of activities which includes
inter alia mapping and analysis
• Will typically involve several datasets
• May also involve several different
pieces of software

Mapping: EpiINFO
• This is a rather simple analysis and
mapping program
• It can be downloaded from the internet
• It doesn’t provide much in the way of
spatial data manipulation…
• … nor are its analytical capabilities very
advanced
• It is easy to use

Spatial Data Manipulation
• GIS isn’t merely concerned with
data display or query
• It’s useful in bringing together
different spatial datasets in the
first stages of an analysis
• This may well require a range of
different geometrical and
database operations

Analysis with Spatial Data
• Statistical analysis usually involves
some separate software.
• Spatial data have some properties
which mean that SPSS isn’t always
appropriate.
• There is now a range of specialist
software – much of it available for
download

GIS may be ideal for data manipulation, display and query,
but it’s not always ideal for analysis – in epidemiology a
variety of analytical software is available … WinBUGS,
GeoBUGS (MRC Biostatistics Unit, Cambridge)

Software to implement
particular methods – for
example scan statistics - can be
downloaded quickly and easily
from the Internet. Kulldorf’s
SaTScan system implements a
range of different scan
statistical models

GIS in Health in Practice
• Facility location – dialysis units
• Facility location – obs & gyn
• Data integration – schizophrenia
in Cavan
• Data manipulation and display –
GMS card holders in Maynooth

1: Provision of Dialysis Units
• There are 5 dialysis units for renal
patients in a region with a population
of about 3,000,000
• Major population centre has two units
about 5 miles apart
• Where would you locate an extra one?
• What would happen if one was
closed?

Location-allocation
• Location-allocation problems deal
with optimum location of facilities
• Attempt to minimise total
weighted travel time/distance
from users to facilities
• What data is needed?

Data requirements
• Digitised road network with classification
of road types
• Mean travel speeds on each type for
urban/non-urban roads
• Extents of urban areas
• Anonymised postcoded patient records
• Postcode->grid reference lookup
• Locations of hospitals in region (both with
and without dialysis units)

GIS processing
• Data integration
• Determine which segments of road network
are urban
• Determine travel times using road type
urban/non-urban speeds
• Tally numbers of patients at each node in
the road network
• Find nearest nodes in network to each
hospital
• Code hospitals as ‘existing’ or ‘potential’
units

Modelling/Analysis
• Carried out using standard GIS
functions
• Convenient ‘front end’ written
using system’s macro language
(save a lot of typing)
• Explore a range of alternatives

Hospital with
dialysis unit

Study region showing hospital locations and urban areas

Patients requiring dialysis (tallied to road nodes)

Classified road network – includes roads outside study area

•

Some alternative
scenarios:

(a) Optimal allocation
of patients
(b) Provide 1 extra
unit
(c) Provide 2 extra
units

(d) Close 1 unit in
city and reallocate
its patients

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

integrate data
process data
model and analyse
generate alternative scenarios
display the results

2: Facility Location
• Two hospitals both provided facilities
for obstetrics, gynaecology, paediatrics
and a special care baby unit.
• Was there a case for rationalisation and
improving the facilities at one hospital
only?
• Would closure disadvantage any
patients?

Data
• Locations of hospitals in study region
• Anonymised postcoded finished
consultant episodes for each of the
four specialities at each hospital
• Classified road network
• Speeds associated with each road
type

Data preparation
• Postcoded records allocated grid
references using Central Postcode
Directory (POSTZON)
• Vector data converted to raster
• Patient/specialty/hospital counts
produced for each raster
• Passage times computed for each
raster cell based on road
classification and speed estimates

Roads, urban areas, patient locations, hospitals in study area

Processing
• Travel time surfaces computed for
each hospital
• Each raster cell contains time
required to reach hospital in minutes
• Assumed walking speed in ‘non road’
areas

Travel time surface for hospital S – darker = shorter time

Travel time surface for hospital T

Time differences
• If we subtract the travel time surfaces areas
nearer one hospital will have positive
values for the difference, and areas nearer
the other will have negative ones
• Boundary between positive and negative
values is boundary of catchment for each
hospital.
• If you’re on the boundary (difference is
zero), it doesn’t matter which hospital you
choose

Roads, patient locations, hospital catchments

Indifference
• If we identify those rasters where the time
difference (ignoring the sign) is, say, 5 –
people in those areas are in the 5 minute
‘indifference’ zone.
• Patients might not be worried about a 5
minute increase in their travel time, they
might be worried about 30 minutes
• Indifference zones can be computed quite
easily

15-minute indifference zone between the two hospitals

Mean access time
• For each cell in the raster we can
compute the product of the number
of patients and the time required to
reach each hospital
• Dividing this by the number of
patients gives a patient weighted
average time for each hospital

Average travel times for the various specialties. There
isn’t much to chose between specialities with the
exception of paediatric episodes.

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

gather data
integrate data
process data
model data
display results

• All carried out using raster
operations

3: Schizophrenia in Cavan
• In 1996 Pringle, Waddington and
Youssef reported on evidence for the
causes of schizophrenia
• Ecological analysis of variation among
EDs
– Cases varied from 0-11 per ED
– Populations varied from 250-2750

• Poisson probabilities suggested that
one area had an unusually high number
of cases – these can be mapped

Male cases
superimposed on plot
of prevalence by ED

Male cases
superimposed on
rectified Landsat
image with ED
boundaries

4: GMS card holders: Maynooth
•Data on GMS – Medical Card holders
drawn under strict data protection
protocols from DOHC
• Anonymised address lists data matched
with GeoDirectory
•Address matching rate of around 77% in
Maynooth

• Interesting range of results around
average for the whole ED

•ED – single value 14.6 % (national average around 30%)
•Small Area – Range from 2% to 70% - noticeable internal variation and
good indicator of elderly or poor populations

•Strategic value in comparing datasets
•Important in policy analysis

Mapping invites comparison

Role of GIS
• Data integration
– Tally of cases in small areas requires
data on locations of cases and small
area boundaries
– ‘spatial join’ places small area codes
on the case records
– Counting is a standard database
operation

• Data display

Finally
• GIS encourages you to think about
space
• In a ‘conventional’ map of Ireland
areas (EDs, Counties…) are shown in
proportion to their physical size
• There are alternative ways of
displaying maps…

In a cartogram the areas are drawn
proportional to their populations –
this is a “density-equalised” map
projection

A map of the boundaries of counties and
electoral divisions – the areas are drawn
proportional to their physical size

By comparison, the more deprived
western area of Dublin, northern area
of Cork and south east of Limerick are
clearly apparent in the cartogram.

Haase’s deprivation index suggests that the
most deprived populations are in peripheral
rural areas in the north west.

Redrawing the data on the cartogram
reveals wide variation in the SMR, and also
uncovers the urban dimension which is
hidden in the conventional display

Patient data matched to Electoral Divisions –
hospital admissions for psychiatric problems – the
patchwork effect does not suggest any pattern

The cartogram reveals the urban nature
of the problem – those areas which are
more deprived are also those with
higher levels of illness/disability

Residents aged 15+ who are unable to work
through illness or disability: the map pattern
suggests that might be a largely rural
phenomenon

The roads, rail, and urban areas data have
been transformed into the same space as
the cartogram. Dublin and Cork seem
poorly provided with roads…

Other spatial information can be transformed
to the space of the density equalised
projection. Here is the familiar map of main
roads and towns.

Using a cartogram we see that the
incidence of lung cancer is randomly
spatially distributed

Lung cancer in males in New York State [Gastner & Newman: 2004]

Plotting locations of lung cancer cases, we
obtain a map which follows the underlying
population distribution – there appear to be
“clusters”

What about Ireland?
• Potentially hampered by challenge of
geocoding patient records
• Lack of small area population data
– EDs have an average population of 1150
– Wide variation in size (50-28000)
– Socially inhomogenous

Rural addresses
• A major challenge is non-uniqueness
of addresses in rural areas.
• For instance in Moy ED, 11 properties
share the address
‘Lackamore, Lahinch, Co. Clare’

What’s needed
• Postcoding should provide a
mechanism for geocoding patient data
with the granularity required for
planning the delivery of health care –
this has been the case in the UK for a
for long time
• Census data should be made available
for areas with small and similar
populations (cf: UK Output Areas)

Finally…
• GIS has an important role to play
in health studies
• Health data analysis is a rapidly
developing field
• However, in Ireland we are
hampered by data deficiencies –
now is the time for change

Maynooth: image in Google Earth

Central Dublin: image in Google Earth

Taoiseach’s Office: image in Google Earth

Downing Street, London: image in Google Earth

Maynooth: OSi orthophotograph added locally uisng KML

Sellafield: image in Google Earth

NCU – On Line Atlas of Cancer Mortality

The Philippine Institute
for Development
Studies makes an online atlas of socioeconomic indicators
available – contains
some health measures

The future
• Patient empowerment…

• GIS used to merge information from
regulatory agencies on 19196 landfill sites
• Sites buffered to 2km and postcodes
within buffer identified
• Postcoded live births (8.2x106), stillbirths
(43471) and congenital anomalies
(124,597) between 1982 and 1997
• Poisson regression modelling undertaken
• Evidence found for excess risk of
congenital anomaly and low birthweight

• Distance from each
census ward to
nearest hospital
computed
• Map shows number
of patients waiting
longer than 6
months for elective
inpatient care per
available and
unoccupied bed
within 60 minutes
travel time


Slide 55

GIS and Health
Martin Charlton1
Ronan Foley2
Dennis Pringle2
1National

Centre for Geocomputation
2Department of Geography
NUI Maynooth

Outline
• Preliminary observations on GIS
and health
• Examples of GIS in Health:
research and practice
• Mapping people not places

Patterns in Health Data
• Three main areas:
– Mapping of disease incidence
• Relative risk estimation

– Identification of disease clustering
• Surveillance

– Ecological analysis
• Relationship between incidence and
covariates
(Lawson et al, 2003)

Health Data
• Typically patient data is available
in two forms
• Individual – perhaps anonymised
to postcode/zip code level
• Aggregate – counts are available
for a set of zones
• The nature of the data affects the
nature of the analysis

Geographic Information Systems
• GIS is often confused with mapping
• A range of activities which includes
inter alia mapping and analysis
• Will typically involve several datasets
• May also involve several different
pieces of software

Mapping: EpiINFO
• This is a rather simple analysis and
mapping program
• It can be downloaded from the internet
• It doesn’t provide much in the way of
spatial data manipulation…
• … nor are its analytical capabilities very
advanced
• It is easy to use

Spatial Data Manipulation
• GIS isn’t merely concerned with
data display or query
• It’s useful in bringing together
different spatial datasets in the
first stages of an analysis
• This may well require a range of
different geometrical and
database operations

Analysis with Spatial Data
• Statistical analysis usually involves
some separate software.
• Spatial data have some properties
which mean that SPSS isn’t always
appropriate.
• There is now a range of specialist
software – much of it available for
download

GIS may be ideal for data manipulation, display and query,
but it’s not always ideal for analysis – in epidemiology a
variety of analytical software is available … WinBUGS,
GeoBUGS (MRC Biostatistics Unit, Cambridge)

Software to implement
particular methods – for
example scan statistics - can be
downloaded quickly and easily
from the Internet. Kulldorf’s
SaTScan system implements a
range of different scan
statistical models

GIS in Health in Practice
• Facility location – dialysis units
• Facility location – obs & gyn
• Data integration – schizophrenia
in Cavan
• Data manipulation and display –
GMS card holders in Maynooth

1: Provision of Dialysis Units
• There are 5 dialysis units for renal
patients in a region with a population
of about 3,000,000
• Major population centre has two units
about 5 miles apart
• Where would you locate an extra one?
• What would happen if one was
closed?

Location-allocation
• Location-allocation problems deal
with optimum location of facilities
• Attempt to minimise total
weighted travel time/distance
from users to facilities
• What data is needed?

Data requirements
• Digitised road network with classification
of road types
• Mean travel speeds on each type for
urban/non-urban roads
• Extents of urban areas
• Anonymised postcoded patient records
• Postcode->grid reference lookup
• Locations of hospitals in region (both with
and without dialysis units)

GIS processing
• Data integration
• Determine which segments of road network
are urban
• Determine travel times using road type
urban/non-urban speeds
• Tally numbers of patients at each node in
the road network
• Find nearest nodes in network to each
hospital
• Code hospitals as ‘existing’ or ‘potential’
units

Modelling/Analysis
• Carried out using standard GIS
functions
• Convenient ‘front end’ written
using system’s macro language
(save a lot of typing)
• Explore a range of alternatives

Hospital with
dialysis unit

Study region showing hospital locations and urban areas

Patients requiring dialysis (tallied to road nodes)

Classified road network – includes roads outside study area

•

Some alternative
scenarios:

(a) Optimal allocation
of patients
(b) Provide 1 extra
unit
(c) Provide 2 extra
units

(d) Close 1 unit in
city and reallocate
its patients

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

integrate data
process data
model and analyse
generate alternative scenarios
display the results

2: Facility Location
• Two hospitals both provided facilities
for obstetrics, gynaecology, paediatrics
and a special care baby unit.
• Was there a case for rationalisation and
improving the facilities at one hospital
only?
• Would closure disadvantage any
patients?

Data
• Locations of hospitals in study region
• Anonymised postcoded finished
consultant episodes for each of the
four specialities at each hospital
• Classified road network
• Speeds associated with each road
type

Data preparation
• Postcoded records allocated grid
references using Central Postcode
Directory (POSTZON)
• Vector data converted to raster
• Patient/specialty/hospital counts
produced for each raster
• Passage times computed for each
raster cell based on road
classification and speed estimates

Roads, urban areas, patient locations, hospitals in study area

Processing
• Travel time surfaces computed for
each hospital
• Each raster cell contains time
required to reach hospital in minutes
• Assumed walking speed in ‘non road’
areas

Travel time surface for hospital S – darker = shorter time

Travel time surface for hospital T

Time differences
• If we subtract the travel time surfaces areas
nearer one hospital will have positive
values for the difference, and areas nearer
the other will have negative ones
• Boundary between positive and negative
values is boundary of catchment for each
hospital.
• If you’re on the boundary (difference is
zero), it doesn’t matter which hospital you
choose

Roads, patient locations, hospital catchments

Indifference
• If we identify those rasters where the time
difference (ignoring the sign) is, say, 5 –
people in those areas are in the 5 minute
‘indifference’ zone.
• Patients might not be worried about a 5
minute increase in their travel time, they
might be worried about 30 minutes
• Indifference zones can be computed quite
easily

15-minute indifference zone between the two hospitals

Mean access time
• For each cell in the raster we can
compute the product of the number
of patients and the time required to
reach each hospital
• Dividing this by the number of
patients gives a patient weighted
average time for each hospital

Average travel times for the various specialties. There
isn’t much to chose between specialities with the
exception of paediatric episodes.

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

gather data
integrate data
process data
model data
display results

• All carried out using raster
operations

3: Schizophrenia in Cavan
• In 1996 Pringle, Waddington and
Youssef reported on evidence for the
causes of schizophrenia
• Ecological analysis of variation among
EDs
– Cases varied from 0-11 per ED
– Populations varied from 250-2750

• Poisson probabilities suggested that
one area had an unusually high number
of cases – these can be mapped

Male cases
superimposed on plot
of prevalence by ED

Male cases
superimposed on
rectified Landsat
image with ED
boundaries

4: GMS card holders: Maynooth
•Data on GMS – Medical Card holders
drawn under strict data protection
protocols from DOHC
• Anonymised address lists data matched
with GeoDirectory
•Address matching rate of around 77% in
Maynooth

• Interesting range of results around
average for the whole ED

•ED – single value 14.6 % (national average around 30%)
•Small Area – Range from 2% to 70% - noticeable internal variation and
good indicator of elderly or poor populations

•Strategic value in comparing datasets
•Important in policy analysis

Mapping invites comparison

Role of GIS
• Data integration
– Tally of cases in small areas requires
data on locations of cases and small
area boundaries
– ‘spatial join’ places small area codes
on the case records
– Counting is a standard database
operation

• Data display

Finally
• GIS encourages you to think about
space
• In a ‘conventional’ map of Ireland
areas (EDs, Counties…) are shown in
proportion to their physical size
• There are alternative ways of
displaying maps…

In a cartogram the areas are drawn
proportional to their populations –
this is a “density-equalised” map
projection

A map of the boundaries of counties and
electoral divisions – the areas are drawn
proportional to their physical size

By comparison, the more deprived
western area of Dublin, northern area
of Cork and south east of Limerick are
clearly apparent in the cartogram.

Haase’s deprivation index suggests that the
most deprived populations are in peripheral
rural areas in the north west.

Redrawing the data on the cartogram
reveals wide variation in the SMR, and also
uncovers the urban dimension which is
hidden in the conventional display

Patient data matched to Electoral Divisions –
hospital admissions for psychiatric problems – the
patchwork effect does not suggest any pattern

The cartogram reveals the urban nature
of the problem – those areas which are
more deprived are also those with
higher levels of illness/disability

Residents aged 15+ who are unable to work
through illness or disability: the map pattern
suggests that might be a largely rural
phenomenon

The roads, rail, and urban areas data have
been transformed into the same space as
the cartogram. Dublin and Cork seem
poorly provided with roads…

Other spatial information can be transformed
to the space of the density equalised
projection. Here is the familiar map of main
roads and towns.

Using a cartogram we see that the
incidence of lung cancer is randomly
spatially distributed

Lung cancer in males in New York State [Gastner & Newman: 2004]

Plotting locations of lung cancer cases, we
obtain a map which follows the underlying
population distribution – there appear to be
“clusters”

What about Ireland?
• Potentially hampered by challenge of
geocoding patient records
• Lack of small area population data
– EDs have an average population of 1150
– Wide variation in size (50-28000)
– Socially inhomogenous

Rural addresses
• A major challenge is non-uniqueness
of addresses in rural areas.
• For instance in Moy ED, 11 properties
share the address
‘Lackamore, Lahinch, Co. Clare’

What’s needed
• Postcoding should provide a
mechanism for geocoding patient data
with the granularity required for
planning the delivery of health care –
this has been the case in the UK for a
for long time
• Census data should be made available
for areas with small and similar
populations (cf: UK Output Areas)

Finally…
• GIS has an important role to play
in health studies
• Health data analysis is a rapidly
developing field
• However, in Ireland we are
hampered by data deficiencies –
now is the time for change

Maynooth: image in Google Earth

Central Dublin: image in Google Earth

Taoiseach’s Office: image in Google Earth

Downing Street, London: image in Google Earth

Maynooth: OSi orthophotograph added locally uisng KML

Sellafield: image in Google Earth

NCU – On Line Atlas of Cancer Mortality

The Philippine Institute
for Development
Studies makes an online atlas of socioeconomic indicators
available – contains
some health measures

The future
• Patient empowerment…

• GIS used to merge information from
regulatory agencies on 19196 landfill sites
• Sites buffered to 2km and postcodes
within buffer identified
• Postcoded live births (8.2x106), stillbirths
(43471) and congenital anomalies
(124,597) between 1982 and 1997
• Poisson regression modelling undertaken
• Evidence found for excess risk of
congenital anomaly and low birthweight

• Distance from each
census ward to
nearest hospital
computed
• Map shows number
of patients waiting
longer than 6
months for elective
inpatient care per
available and
unoccupied bed
within 60 minutes
travel time


Slide 56

GIS and Health
Martin Charlton1
Ronan Foley2
Dennis Pringle2
1National

Centre for Geocomputation
2Department of Geography
NUI Maynooth

Outline
• Preliminary observations on GIS
and health
• Examples of GIS in Health:
research and practice
• Mapping people not places

Patterns in Health Data
• Three main areas:
– Mapping of disease incidence
• Relative risk estimation

– Identification of disease clustering
• Surveillance

– Ecological analysis
• Relationship between incidence and
covariates
(Lawson et al, 2003)

Health Data
• Typically patient data is available
in two forms
• Individual – perhaps anonymised
to postcode/zip code level
• Aggregate – counts are available
for a set of zones
• The nature of the data affects the
nature of the analysis

Geographic Information Systems
• GIS is often confused with mapping
• A range of activities which includes
inter alia mapping and analysis
• Will typically involve several datasets
• May also involve several different
pieces of software

Mapping: EpiINFO
• This is a rather simple analysis and
mapping program
• It can be downloaded from the internet
• It doesn’t provide much in the way of
spatial data manipulation…
• … nor are its analytical capabilities very
advanced
• It is easy to use

Spatial Data Manipulation
• GIS isn’t merely concerned with
data display or query
• It’s useful in bringing together
different spatial datasets in the
first stages of an analysis
• This may well require a range of
different geometrical and
database operations

Analysis with Spatial Data
• Statistical analysis usually involves
some separate software.
• Spatial data have some properties
which mean that SPSS isn’t always
appropriate.
• There is now a range of specialist
software – much of it available for
download

GIS may be ideal for data manipulation, display and query,
but it’s not always ideal for analysis – in epidemiology a
variety of analytical software is available … WinBUGS,
GeoBUGS (MRC Biostatistics Unit, Cambridge)

Software to implement
particular methods – for
example scan statistics - can be
downloaded quickly and easily
from the Internet. Kulldorf’s
SaTScan system implements a
range of different scan
statistical models

GIS in Health in Practice
• Facility location – dialysis units
• Facility location – obs & gyn
• Data integration – schizophrenia
in Cavan
• Data manipulation and display –
GMS card holders in Maynooth

1: Provision of Dialysis Units
• There are 5 dialysis units for renal
patients in a region with a population
of about 3,000,000
• Major population centre has two units
about 5 miles apart
• Where would you locate an extra one?
• What would happen if one was
closed?

Location-allocation
• Location-allocation problems deal
with optimum location of facilities
• Attempt to minimise total
weighted travel time/distance
from users to facilities
• What data is needed?

Data requirements
• Digitised road network with classification
of road types
• Mean travel speeds on each type for
urban/non-urban roads
• Extents of urban areas
• Anonymised postcoded patient records
• Postcode->grid reference lookup
• Locations of hospitals in region (both with
and without dialysis units)

GIS processing
• Data integration
• Determine which segments of road network
are urban
• Determine travel times using road type
urban/non-urban speeds
• Tally numbers of patients at each node in
the road network
• Find nearest nodes in network to each
hospital
• Code hospitals as ‘existing’ or ‘potential’
units

Modelling/Analysis
• Carried out using standard GIS
functions
• Convenient ‘front end’ written
using system’s macro language
(save a lot of typing)
• Explore a range of alternatives

Hospital with
dialysis unit

Study region showing hospital locations and urban areas

Patients requiring dialysis (tallied to road nodes)

Classified road network – includes roads outside study area

•

Some alternative
scenarios:

(a) Optimal allocation
of patients
(b) Provide 1 extra
unit
(c) Provide 2 extra
units

(d) Close 1 unit in
city and reallocate
its patients

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

integrate data
process data
model and analyse
generate alternative scenarios
display the results

2: Facility Location
• Two hospitals both provided facilities
for obstetrics, gynaecology, paediatrics
and a special care baby unit.
• Was there a case for rationalisation and
improving the facilities at one hospital
only?
• Would closure disadvantage any
patients?

Data
• Locations of hospitals in study region
• Anonymised postcoded finished
consultant episodes for each of the
four specialities at each hospital
• Classified road network
• Speeds associated with each road
type

Data preparation
• Postcoded records allocated grid
references using Central Postcode
Directory (POSTZON)
• Vector data converted to raster
• Patient/specialty/hospital counts
produced for each raster
• Passage times computed for each
raster cell based on road
classification and speed estimates

Roads, urban areas, patient locations, hospitals in study area

Processing
• Travel time surfaces computed for
each hospital
• Each raster cell contains time
required to reach hospital in minutes
• Assumed walking speed in ‘non road’
areas

Travel time surface for hospital S – darker = shorter time

Travel time surface for hospital T

Time differences
• If we subtract the travel time surfaces areas
nearer one hospital will have positive
values for the difference, and areas nearer
the other will have negative ones
• Boundary between positive and negative
values is boundary of catchment for each
hospital.
• If you’re on the boundary (difference is
zero), it doesn’t matter which hospital you
choose

Roads, patient locations, hospital catchments

Indifference
• If we identify those rasters where the time
difference (ignoring the sign) is, say, 5 –
people in those areas are in the 5 minute
‘indifference’ zone.
• Patients might not be worried about a 5
minute increase in their travel time, they
might be worried about 30 minutes
• Indifference zones can be computed quite
easily

15-minute indifference zone between the two hospitals

Mean access time
• For each cell in the raster we can
compute the product of the number
of patients and the time required to
reach each hospital
• Dividing this by the number of
patients gives a patient weighted
average time for each hospital

Average travel times for the various specialties. There
isn’t much to chose between specialities with the
exception of paediatric episodes.

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

gather data
integrate data
process data
model data
display results

• All carried out using raster
operations

3: Schizophrenia in Cavan
• In 1996 Pringle, Waddington and
Youssef reported on evidence for the
causes of schizophrenia
• Ecological analysis of variation among
EDs
– Cases varied from 0-11 per ED
– Populations varied from 250-2750

• Poisson probabilities suggested that
one area had an unusually high number
of cases – these can be mapped

Male cases
superimposed on plot
of prevalence by ED

Male cases
superimposed on
rectified Landsat
image with ED
boundaries

4: GMS card holders: Maynooth
•Data on GMS – Medical Card holders
drawn under strict data protection
protocols from DOHC
• Anonymised address lists data matched
with GeoDirectory
•Address matching rate of around 77% in
Maynooth

• Interesting range of results around
average for the whole ED

•ED – single value 14.6 % (national average around 30%)
•Small Area – Range from 2% to 70% - noticeable internal variation and
good indicator of elderly or poor populations

•Strategic value in comparing datasets
•Important in policy analysis

Mapping invites comparison

Role of GIS
• Data integration
– Tally of cases in small areas requires
data on locations of cases and small
area boundaries
– ‘spatial join’ places small area codes
on the case records
– Counting is a standard database
operation

• Data display

Finally
• GIS encourages you to think about
space
• In a ‘conventional’ map of Ireland
areas (EDs, Counties…) are shown in
proportion to their physical size
• There are alternative ways of
displaying maps…

In a cartogram the areas are drawn
proportional to their populations –
this is a “density-equalised” map
projection

A map of the boundaries of counties and
electoral divisions – the areas are drawn
proportional to their physical size

By comparison, the more deprived
western area of Dublin, northern area
of Cork and south east of Limerick are
clearly apparent in the cartogram.

Haase’s deprivation index suggests that the
most deprived populations are in peripheral
rural areas in the north west.

Redrawing the data on the cartogram
reveals wide variation in the SMR, and also
uncovers the urban dimension which is
hidden in the conventional display

Patient data matched to Electoral Divisions –
hospital admissions for psychiatric problems – the
patchwork effect does not suggest any pattern

The cartogram reveals the urban nature
of the problem – those areas which are
more deprived are also those with
higher levels of illness/disability

Residents aged 15+ who are unable to work
through illness or disability: the map pattern
suggests that might be a largely rural
phenomenon

The roads, rail, and urban areas data have
been transformed into the same space as
the cartogram. Dublin and Cork seem
poorly provided with roads…

Other spatial information can be transformed
to the space of the density equalised
projection. Here is the familiar map of main
roads and towns.

Using a cartogram we see that the
incidence of lung cancer is randomly
spatially distributed

Lung cancer in males in New York State [Gastner & Newman: 2004]

Plotting locations of lung cancer cases, we
obtain a map which follows the underlying
population distribution – there appear to be
“clusters”

What about Ireland?
• Potentially hampered by challenge of
geocoding patient records
• Lack of small area population data
– EDs have an average population of 1150
– Wide variation in size (50-28000)
– Socially inhomogenous

Rural addresses
• A major challenge is non-uniqueness
of addresses in rural areas.
• For instance in Moy ED, 11 properties
share the address
‘Lackamore, Lahinch, Co. Clare’

What’s needed
• Postcoding should provide a
mechanism for geocoding patient data
with the granularity required for
planning the delivery of health care –
this has been the case in the UK for a
for long time
• Census data should be made available
for areas with small and similar
populations (cf: UK Output Areas)

Finally…
• GIS has an important role to play
in health studies
• Health data analysis is a rapidly
developing field
• However, in Ireland we are
hampered by data deficiencies –
now is the time for change

Maynooth: image in Google Earth

Central Dublin: image in Google Earth

Taoiseach’s Office: image in Google Earth

Downing Street, London: image in Google Earth

Maynooth: OSi orthophotograph added locally uisng KML

Sellafield: image in Google Earth

NCU – On Line Atlas of Cancer Mortality

The Philippine Institute
for Development
Studies makes an online atlas of socioeconomic indicators
available – contains
some health measures

The future
• Patient empowerment…

• GIS used to merge information from
regulatory agencies on 19196 landfill sites
• Sites buffered to 2km and postcodes
within buffer identified
• Postcoded live births (8.2x106), stillbirths
(43471) and congenital anomalies
(124,597) between 1982 and 1997
• Poisson regression modelling undertaken
• Evidence found for excess risk of
congenital anomaly and low birthweight

• Distance from each
census ward to
nearest hospital
computed
• Map shows number
of patients waiting
longer than 6
months for elective
inpatient care per
available and
unoccupied bed
within 60 minutes
travel time


Slide 57

GIS and Health
Martin Charlton1
Ronan Foley2
Dennis Pringle2
1National

Centre for Geocomputation
2Department of Geography
NUI Maynooth

Outline
• Preliminary observations on GIS
and health
• Examples of GIS in Health:
research and practice
• Mapping people not places

Patterns in Health Data
• Three main areas:
– Mapping of disease incidence
• Relative risk estimation

– Identification of disease clustering
• Surveillance

– Ecological analysis
• Relationship between incidence and
covariates
(Lawson et al, 2003)

Health Data
• Typically patient data is available
in two forms
• Individual – perhaps anonymised
to postcode/zip code level
• Aggregate – counts are available
for a set of zones
• The nature of the data affects the
nature of the analysis

Geographic Information Systems
• GIS is often confused with mapping
• A range of activities which includes
inter alia mapping and analysis
• Will typically involve several datasets
• May also involve several different
pieces of software

Mapping: EpiINFO
• This is a rather simple analysis and
mapping program
• It can be downloaded from the internet
• It doesn’t provide much in the way of
spatial data manipulation…
• … nor are its analytical capabilities very
advanced
• It is easy to use

Spatial Data Manipulation
• GIS isn’t merely concerned with
data display or query
• It’s useful in bringing together
different spatial datasets in the
first stages of an analysis
• This may well require a range of
different geometrical and
database operations

Analysis with Spatial Data
• Statistical analysis usually involves
some separate software.
• Spatial data have some properties
which mean that SPSS isn’t always
appropriate.
• There is now a range of specialist
software – much of it available for
download

GIS may be ideal for data manipulation, display and query,
but it’s not always ideal for analysis – in epidemiology a
variety of analytical software is available … WinBUGS,
GeoBUGS (MRC Biostatistics Unit, Cambridge)

Software to implement
particular methods – for
example scan statistics - can be
downloaded quickly and easily
from the Internet. Kulldorf’s
SaTScan system implements a
range of different scan
statistical models

GIS in Health in Practice
• Facility location – dialysis units
• Facility location – obs & gyn
• Data integration – schizophrenia
in Cavan
• Data manipulation and display –
GMS card holders in Maynooth

1: Provision of Dialysis Units
• There are 5 dialysis units for renal
patients in a region with a population
of about 3,000,000
• Major population centre has two units
about 5 miles apart
• Where would you locate an extra one?
• What would happen if one was
closed?

Location-allocation
• Location-allocation problems deal
with optimum location of facilities
• Attempt to minimise total
weighted travel time/distance
from users to facilities
• What data is needed?

Data requirements
• Digitised road network with classification
of road types
• Mean travel speeds on each type for
urban/non-urban roads
• Extents of urban areas
• Anonymised postcoded patient records
• Postcode->grid reference lookup
• Locations of hospitals in region (both with
and without dialysis units)

GIS processing
• Data integration
• Determine which segments of road network
are urban
• Determine travel times using road type
urban/non-urban speeds
• Tally numbers of patients at each node in
the road network
• Find nearest nodes in network to each
hospital
• Code hospitals as ‘existing’ or ‘potential’
units

Modelling/Analysis
• Carried out using standard GIS
functions
• Convenient ‘front end’ written
using system’s macro language
(save a lot of typing)
• Explore a range of alternatives

Hospital with
dialysis unit

Study region showing hospital locations and urban areas

Patients requiring dialysis (tallied to road nodes)

Classified road network – includes roads outside study area

•

Some alternative
scenarios:

(a) Optimal allocation
of patients
(b) Provide 1 extra
unit
(c) Provide 2 extra
units

(d) Close 1 unit in
city and reallocate
its patients

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

integrate data
process data
model and analyse
generate alternative scenarios
display the results

2: Facility Location
• Two hospitals both provided facilities
for obstetrics, gynaecology, paediatrics
and a special care baby unit.
• Was there a case for rationalisation and
improving the facilities at one hospital
only?
• Would closure disadvantage any
patients?

Data
• Locations of hospitals in study region
• Anonymised postcoded finished
consultant episodes for each of the
four specialities at each hospital
• Classified road network
• Speeds associated with each road
type

Data preparation
• Postcoded records allocated grid
references using Central Postcode
Directory (POSTZON)
• Vector data converted to raster
• Patient/specialty/hospital counts
produced for each raster
• Passage times computed for each
raster cell based on road
classification and speed estimates

Roads, urban areas, patient locations, hospitals in study area

Processing
• Travel time surfaces computed for
each hospital
• Each raster cell contains time
required to reach hospital in minutes
• Assumed walking speed in ‘non road’
areas

Travel time surface for hospital S – darker = shorter time

Travel time surface for hospital T

Time differences
• If we subtract the travel time surfaces areas
nearer one hospital will have positive
values for the difference, and areas nearer
the other will have negative ones
• Boundary between positive and negative
values is boundary of catchment for each
hospital.
• If you’re on the boundary (difference is
zero), it doesn’t matter which hospital you
choose

Roads, patient locations, hospital catchments

Indifference
• If we identify those rasters where the time
difference (ignoring the sign) is, say, 5 –
people in those areas are in the 5 minute
‘indifference’ zone.
• Patients might not be worried about a 5
minute increase in their travel time, they
might be worried about 30 minutes
• Indifference zones can be computed quite
easily

15-minute indifference zone between the two hospitals

Mean access time
• For each cell in the raster we can
compute the product of the number
of patients and the time required to
reach each hospital
• Dividing this by the number of
patients gives a patient weighted
average time for each hospital

Average travel times for the various specialties. There
isn’t much to chose between specialities with the
exception of paediatric episodes.

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

gather data
integrate data
process data
model data
display results

• All carried out using raster
operations

3: Schizophrenia in Cavan
• In 1996 Pringle, Waddington and
Youssef reported on evidence for the
causes of schizophrenia
• Ecological analysis of variation among
EDs
– Cases varied from 0-11 per ED
– Populations varied from 250-2750

• Poisson probabilities suggested that
one area had an unusually high number
of cases – these can be mapped

Male cases
superimposed on plot
of prevalence by ED

Male cases
superimposed on
rectified Landsat
image with ED
boundaries

4: GMS card holders: Maynooth
•Data on GMS – Medical Card holders
drawn under strict data protection
protocols from DOHC
• Anonymised address lists data matched
with GeoDirectory
•Address matching rate of around 77% in
Maynooth

• Interesting range of results around
average for the whole ED

•ED – single value 14.6 % (national average around 30%)
•Small Area – Range from 2% to 70% - noticeable internal variation and
good indicator of elderly or poor populations

•Strategic value in comparing datasets
•Important in policy analysis

Mapping invites comparison

Role of GIS
• Data integration
– Tally of cases in small areas requires
data on locations of cases and small
area boundaries
– ‘spatial join’ places small area codes
on the case records
– Counting is a standard database
operation

• Data display

Finally
• GIS encourages you to think about
space
• In a ‘conventional’ map of Ireland
areas (EDs, Counties…) are shown in
proportion to their physical size
• There are alternative ways of
displaying maps…

In a cartogram the areas are drawn
proportional to their populations –
this is a “density-equalised” map
projection

A map of the boundaries of counties and
electoral divisions – the areas are drawn
proportional to their physical size

By comparison, the more deprived
western area of Dublin, northern area
of Cork and south east of Limerick are
clearly apparent in the cartogram.

Haase’s deprivation index suggests that the
most deprived populations are in peripheral
rural areas in the north west.

Redrawing the data on the cartogram
reveals wide variation in the SMR, and also
uncovers the urban dimension which is
hidden in the conventional display

Patient data matched to Electoral Divisions –
hospital admissions for psychiatric problems – the
patchwork effect does not suggest any pattern

The cartogram reveals the urban nature
of the problem – those areas which are
more deprived are also those with
higher levels of illness/disability

Residents aged 15+ who are unable to work
through illness or disability: the map pattern
suggests that might be a largely rural
phenomenon

The roads, rail, and urban areas data have
been transformed into the same space as
the cartogram. Dublin and Cork seem
poorly provided with roads…

Other spatial information can be transformed
to the space of the density equalised
projection. Here is the familiar map of main
roads and towns.

Using a cartogram we see that the
incidence of lung cancer is randomly
spatially distributed

Lung cancer in males in New York State [Gastner & Newman: 2004]

Plotting locations of lung cancer cases, we
obtain a map which follows the underlying
population distribution – there appear to be
“clusters”

What about Ireland?
• Potentially hampered by challenge of
geocoding patient records
• Lack of small area population data
– EDs have an average population of 1150
– Wide variation in size (50-28000)
– Socially inhomogenous

Rural addresses
• A major challenge is non-uniqueness
of addresses in rural areas.
• For instance in Moy ED, 11 properties
share the address
‘Lackamore, Lahinch, Co. Clare’

What’s needed
• Postcoding should provide a
mechanism for geocoding patient data
with the granularity required for
planning the delivery of health care –
this has been the case in the UK for a
for long time
• Census data should be made available
for areas with small and similar
populations (cf: UK Output Areas)

Finally…
• GIS has an important role to play
in health studies
• Health data analysis is a rapidly
developing field
• However, in Ireland we are
hampered by data deficiencies –
now is the time for change

Maynooth: image in Google Earth

Central Dublin: image in Google Earth

Taoiseach’s Office: image in Google Earth

Downing Street, London: image in Google Earth

Maynooth: OSi orthophotograph added locally uisng KML

Sellafield: image in Google Earth

NCU – On Line Atlas of Cancer Mortality

The Philippine Institute
for Development
Studies makes an online atlas of socioeconomic indicators
available – contains
some health measures

The future
• Patient empowerment…

• GIS used to merge information from
regulatory agencies on 19196 landfill sites
• Sites buffered to 2km and postcodes
within buffer identified
• Postcoded live births (8.2x106), stillbirths
(43471) and congenital anomalies
(124,597) between 1982 and 1997
• Poisson regression modelling undertaken
• Evidence found for excess risk of
congenital anomaly and low birthweight

• Distance from each
census ward to
nearest hospital
computed
• Map shows number
of patients waiting
longer than 6
months for elective
inpatient care per
available and
unoccupied bed
within 60 minutes
travel time


Slide 58

GIS and Health
Martin Charlton1
Ronan Foley2
Dennis Pringle2
1National

Centre for Geocomputation
2Department of Geography
NUI Maynooth

Outline
• Preliminary observations on GIS
and health
• Examples of GIS in Health:
research and practice
• Mapping people not places

Patterns in Health Data
• Three main areas:
– Mapping of disease incidence
• Relative risk estimation

– Identification of disease clustering
• Surveillance

– Ecological analysis
• Relationship between incidence and
covariates
(Lawson et al, 2003)

Health Data
• Typically patient data is available
in two forms
• Individual – perhaps anonymised
to postcode/zip code level
• Aggregate – counts are available
for a set of zones
• The nature of the data affects the
nature of the analysis

Geographic Information Systems
• GIS is often confused with mapping
• A range of activities which includes
inter alia mapping and analysis
• Will typically involve several datasets
• May also involve several different
pieces of software

Mapping: EpiINFO
• This is a rather simple analysis and
mapping program
• It can be downloaded from the internet
• It doesn’t provide much in the way of
spatial data manipulation…
• … nor are its analytical capabilities very
advanced
• It is easy to use

Spatial Data Manipulation
• GIS isn’t merely concerned with
data display or query
• It’s useful in bringing together
different spatial datasets in the
first stages of an analysis
• This may well require a range of
different geometrical and
database operations

Analysis with Spatial Data
• Statistical analysis usually involves
some separate software.
• Spatial data have some properties
which mean that SPSS isn’t always
appropriate.
• There is now a range of specialist
software – much of it available for
download

GIS may be ideal for data manipulation, display and query,
but it’s not always ideal for analysis – in epidemiology a
variety of analytical software is available … WinBUGS,
GeoBUGS (MRC Biostatistics Unit, Cambridge)

Software to implement
particular methods – for
example scan statistics - can be
downloaded quickly and easily
from the Internet. Kulldorf’s
SaTScan system implements a
range of different scan
statistical models

GIS in Health in Practice
• Facility location – dialysis units
• Facility location – obs & gyn
• Data integration – schizophrenia
in Cavan
• Data manipulation and display –
GMS card holders in Maynooth

1: Provision of Dialysis Units
• There are 5 dialysis units for renal
patients in a region with a population
of about 3,000,000
• Major population centre has two units
about 5 miles apart
• Where would you locate an extra one?
• What would happen if one was
closed?

Location-allocation
• Location-allocation problems deal
with optimum location of facilities
• Attempt to minimise total
weighted travel time/distance
from users to facilities
• What data is needed?

Data requirements
• Digitised road network with classification
of road types
• Mean travel speeds on each type for
urban/non-urban roads
• Extents of urban areas
• Anonymised postcoded patient records
• Postcode->grid reference lookup
• Locations of hospitals in region (both with
and without dialysis units)

GIS processing
• Data integration
• Determine which segments of road network
are urban
• Determine travel times using road type
urban/non-urban speeds
• Tally numbers of patients at each node in
the road network
• Find nearest nodes in network to each
hospital
• Code hospitals as ‘existing’ or ‘potential’
units

Modelling/Analysis
• Carried out using standard GIS
functions
• Convenient ‘front end’ written
using system’s macro language
(save a lot of typing)
• Explore a range of alternatives

Hospital with
dialysis unit

Study region showing hospital locations and urban areas

Patients requiring dialysis (tallied to road nodes)

Classified road network – includes roads outside study area

•

Some alternative
scenarios:

(a) Optimal allocation
of patients
(b) Provide 1 extra
unit
(c) Provide 2 extra
units

(d) Close 1 unit in
city and reallocate
its patients

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

integrate data
process data
model and analyse
generate alternative scenarios
display the results

2: Facility Location
• Two hospitals both provided facilities
for obstetrics, gynaecology, paediatrics
and a special care baby unit.
• Was there a case for rationalisation and
improving the facilities at one hospital
only?
• Would closure disadvantage any
patients?

Data
• Locations of hospitals in study region
• Anonymised postcoded finished
consultant episodes for each of the
four specialities at each hospital
• Classified road network
• Speeds associated with each road
type

Data preparation
• Postcoded records allocated grid
references using Central Postcode
Directory (POSTZON)
• Vector data converted to raster
• Patient/specialty/hospital counts
produced for each raster
• Passage times computed for each
raster cell based on road
classification and speed estimates

Roads, urban areas, patient locations, hospitals in study area

Processing
• Travel time surfaces computed for
each hospital
• Each raster cell contains time
required to reach hospital in minutes
• Assumed walking speed in ‘non road’
areas

Travel time surface for hospital S – darker = shorter time

Travel time surface for hospital T

Time differences
• If we subtract the travel time surfaces areas
nearer one hospital will have positive
values for the difference, and areas nearer
the other will have negative ones
• Boundary between positive and negative
values is boundary of catchment for each
hospital.
• If you’re on the boundary (difference is
zero), it doesn’t matter which hospital you
choose

Roads, patient locations, hospital catchments

Indifference
• If we identify those rasters where the time
difference (ignoring the sign) is, say, 5 –
people in those areas are in the 5 minute
‘indifference’ zone.
• Patients might not be worried about a 5
minute increase in their travel time, they
might be worried about 30 minutes
• Indifference zones can be computed quite
easily

15-minute indifference zone between the two hospitals

Mean access time
• For each cell in the raster we can
compute the product of the number
of patients and the time required to
reach each hospital
• Dividing this by the number of
patients gives a patient weighted
average time for each hospital

Average travel times for the various specialties. There
isn’t much to chose between specialities with the
exception of paediatric episodes.

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

gather data
integrate data
process data
model data
display results

• All carried out using raster
operations

3: Schizophrenia in Cavan
• In 1996 Pringle, Waddington and
Youssef reported on evidence for the
causes of schizophrenia
• Ecological analysis of variation among
EDs
– Cases varied from 0-11 per ED
– Populations varied from 250-2750

• Poisson probabilities suggested that
one area had an unusually high number
of cases – these can be mapped

Male cases
superimposed on plot
of prevalence by ED

Male cases
superimposed on
rectified Landsat
image with ED
boundaries

4: GMS card holders: Maynooth
•Data on GMS – Medical Card holders
drawn under strict data protection
protocols from DOHC
• Anonymised address lists data matched
with GeoDirectory
•Address matching rate of around 77% in
Maynooth

• Interesting range of results around
average for the whole ED

•ED – single value 14.6 % (national average around 30%)
•Small Area – Range from 2% to 70% - noticeable internal variation and
good indicator of elderly or poor populations

•Strategic value in comparing datasets
•Important in policy analysis

Mapping invites comparison

Role of GIS
• Data integration
– Tally of cases in small areas requires
data on locations of cases and small
area boundaries
– ‘spatial join’ places small area codes
on the case records
– Counting is a standard database
operation

• Data display

Finally
• GIS encourages you to think about
space
• In a ‘conventional’ map of Ireland
areas (EDs, Counties…) are shown in
proportion to their physical size
• There are alternative ways of
displaying maps…

In a cartogram the areas are drawn
proportional to their populations –
this is a “density-equalised” map
projection

A map of the boundaries of counties and
electoral divisions – the areas are drawn
proportional to their physical size

By comparison, the more deprived
western area of Dublin, northern area
of Cork and south east of Limerick are
clearly apparent in the cartogram.

Haase’s deprivation index suggests that the
most deprived populations are in peripheral
rural areas in the north west.

Redrawing the data on the cartogram
reveals wide variation in the SMR, and also
uncovers the urban dimension which is
hidden in the conventional display

Patient data matched to Electoral Divisions –
hospital admissions for psychiatric problems – the
patchwork effect does not suggest any pattern

The cartogram reveals the urban nature
of the problem – those areas which are
more deprived are also those with
higher levels of illness/disability

Residents aged 15+ who are unable to work
through illness or disability: the map pattern
suggests that might be a largely rural
phenomenon

The roads, rail, and urban areas data have
been transformed into the same space as
the cartogram. Dublin and Cork seem
poorly provided with roads…

Other spatial information can be transformed
to the space of the density equalised
projection. Here is the familiar map of main
roads and towns.

Using a cartogram we see that the
incidence of lung cancer is randomly
spatially distributed

Lung cancer in males in New York State [Gastner & Newman: 2004]

Plotting locations of lung cancer cases, we
obtain a map which follows the underlying
population distribution – there appear to be
“clusters”

What about Ireland?
• Potentially hampered by challenge of
geocoding patient records
• Lack of small area population data
– EDs have an average population of 1150
– Wide variation in size (50-28000)
– Socially inhomogenous

Rural addresses
• A major challenge is non-uniqueness
of addresses in rural areas.
• For instance in Moy ED, 11 properties
share the address
‘Lackamore, Lahinch, Co. Clare’

What’s needed
• Postcoding should provide a
mechanism for geocoding patient data
with the granularity required for
planning the delivery of health care –
this has been the case in the UK for a
for long time
• Census data should be made available
for areas with small and similar
populations (cf: UK Output Areas)

Finally…
• GIS has an important role to play
in health studies
• Health data analysis is a rapidly
developing field
• However, in Ireland we are
hampered by data deficiencies –
now is the time for change

Maynooth: image in Google Earth

Central Dublin: image in Google Earth

Taoiseach’s Office: image in Google Earth

Downing Street, London: image in Google Earth

Maynooth: OSi orthophotograph added locally uisng KML

Sellafield: image in Google Earth

NCU – On Line Atlas of Cancer Mortality

The Philippine Institute
for Development
Studies makes an online atlas of socioeconomic indicators
available – contains
some health measures

The future
• Patient empowerment…

• GIS used to merge information from
regulatory agencies on 19196 landfill sites
• Sites buffered to 2km and postcodes
within buffer identified
• Postcoded live births (8.2x106), stillbirths
(43471) and congenital anomalies
(124,597) between 1982 and 1997
• Poisson regression modelling undertaken
• Evidence found for excess risk of
congenital anomaly and low birthweight

• Distance from each
census ward to
nearest hospital
computed
• Map shows number
of patients waiting
longer than 6
months for elective
inpatient care per
available and
unoccupied bed
within 60 minutes
travel time


Slide 59

GIS and Health
Martin Charlton1
Ronan Foley2
Dennis Pringle2
1National

Centre for Geocomputation
2Department of Geography
NUI Maynooth

Outline
• Preliminary observations on GIS
and health
• Examples of GIS in Health:
research and practice
• Mapping people not places

Patterns in Health Data
• Three main areas:
– Mapping of disease incidence
• Relative risk estimation

– Identification of disease clustering
• Surveillance

– Ecological analysis
• Relationship between incidence and
covariates
(Lawson et al, 2003)

Health Data
• Typically patient data is available
in two forms
• Individual – perhaps anonymised
to postcode/zip code level
• Aggregate – counts are available
for a set of zones
• The nature of the data affects the
nature of the analysis

Geographic Information Systems
• GIS is often confused with mapping
• A range of activities which includes
inter alia mapping and analysis
• Will typically involve several datasets
• May also involve several different
pieces of software

Mapping: EpiINFO
• This is a rather simple analysis and
mapping program
• It can be downloaded from the internet
• It doesn’t provide much in the way of
spatial data manipulation…
• … nor are its analytical capabilities very
advanced
• It is easy to use

Spatial Data Manipulation
• GIS isn’t merely concerned with
data display or query
• It’s useful in bringing together
different spatial datasets in the
first stages of an analysis
• This may well require a range of
different geometrical and
database operations

Analysis with Spatial Data
• Statistical analysis usually involves
some separate software.
• Spatial data have some properties
which mean that SPSS isn’t always
appropriate.
• There is now a range of specialist
software – much of it available for
download

GIS may be ideal for data manipulation, display and query,
but it’s not always ideal for analysis – in epidemiology a
variety of analytical software is available … WinBUGS,
GeoBUGS (MRC Biostatistics Unit, Cambridge)

Software to implement
particular methods – for
example scan statistics - can be
downloaded quickly and easily
from the Internet. Kulldorf’s
SaTScan system implements a
range of different scan
statistical models

GIS in Health in Practice
• Facility location – dialysis units
• Facility location – obs & gyn
• Data integration – schizophrenia
in Cavan
• Data manipulation and display –
GMS card holders in Maynooth

1: Provision of Dialysis Units
• There are 5 dialysis units for renal
patients in a region with a population
of about 3,000,000
• Major population centre has two units
about 5 miles apart
• Where would you locate an extra one?
• What would happen if one was
closed?

Location-allocation
• Location-allocation problems deal
with optimum location of facilities
• Attempt to minimise total
weighted travel time/distance
from users to facilities
• What data is needed?

Data requirements
• Digitised road network with classification
of road types
• Mean travel speeds on each type for
urban/non-urban roads
• Extents of urban areas
• Anonymised postcoded patient records
• Postcode->grid reference lookup
• Locations of hospitals in region (both with
and without dialysis units)

GIS processing
• Data integration
• Determine which segments of road network
are urban
• Determine travel times using road type
urban/non-urban speeds
• Tally numbers of patients at each node in
the road network
• Find nearest nodes in network to each
hospital
• Code hospitals as ‘existing’ or ‘potential’
units

Modelling/Analysis
• Carried out using standard GIS
functions
• Convenient ‘front end’ written
using system’s macro language
(save a lot of typing)
• Explore a range of alternatives

Hospital with
dialysis unit

Study region showing hospital locations and urban areas

Patients requiring dialysis (tallied to road nodes)

Classified road network – includes roads outside study area

•

Some alternative
scenarios:

(a) Optimal allocation
of patients
(b) Provide 1 extra
unit
(c) Provide 2 extra
units

(d) Close 1 unit in
city and reallocate
its patients

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

integrate data
process data
model and analyse
generate alternative scenarios
display the results

2: Facility Location
• Two hospitals both provided facilities
for obstetrics, gynaecology, paediatrics
and a special care baby unit.
• Was there a case for rationalisation and
improving the facilities at one hospital
only?
• Would closure disadvantage any
patients?

Data
• Locations of hospitals in study region
• Anonymised postcoded finished
consultant episodes for each of the
four specialities at each hospital
• Classified road network
• Speeds associated with each road
type

Data preparation
• Postcoded records allocated grid
references using Central Postcode
Directory (POSTZON)
• Vector data converted to raster
• Patient/specialty/hospital counts
produced for each raster
• Passage times computed for each
raster cell based on road
classification and speed estimates

Roads, urban areas, patient locations, hospitals in study area

Processing
• Travel time surfaces computed for
each hospital
• Each raster cell contains time
required to reach hospital in minutes
• Assumed walking speed in ‘non road’
areas

Travel time surface for hospital S – darker = shorter time

Travel time surface for hospital T

Time differences
• If we subtract the travel time surfaces areas
nearer one hospital will have positive
values for the difference, and areas nearer
the other will have negative ones
• Boundary between positive and negative
values is boundary of catchment for each
hospital.
• If you’re on the boundary (difference is
zero), it doesn’t matter which hospital you
choose

Roads, patient locations, hospital catchments

Indifference
• If we identify those rasters where the time
difference (ignoring the sign) is, say, 5 –
people in those areas are in the 5 minute
‘indifference’ zone.
• Patients might not be worried about a 5
minute increase in their travel time, they
might be worried about 30 minutes
• Indifference zones can be computed quite
easily

15-minute indifference zone between the two hospitals

Mean access time
• For each cell in the raster we can
compute the product of the number
of patients and the time required to
reach each hospital
• Dividing this by the number of
patients gives a patient weighted
average time for each hospital

Average travel times for the various specialties. There
isn’t much to chose between specialities with the
exception of paediatric episodes.

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

gather data
integrate data
process data
model data
display results

• All carried out using raster
operations

3: Schizophrenia in Cavan
• In 1996 Pringle, Waddington and
Youssef reported on evidence for the
causes of schizophrenia
• Ecological analysis of variation among
EDs
– Cases varied from 0-11 per ED
– Populations varied from 250-2750

• Poisson probabilities suggested that
one area had an unusually high number
of cases – these can be mapped

Male cases
superimposed on plot
of prevalence by ED

Male cases
superimposed on
rectified Landsat
image with ED
boundaries

4: GMS card holders: Maynooth
•Data on GMS – Medical Card holders
drawn under strict data protection
protocols from DOHC
• Anonymised address lists data matched
with GeoDirectory
•Address matching rate of around 77% in
Maynooth

• Interesting range of results around
average for the whole ED

•ED – single value 14.6 % (national average around 30%)
•Small Area – Range from 2% to 70% - noticeable internal variation and
good indicator of elderly or poor populations

•Strategic value in comparing datasets
•Important in policy analysis

Mapping invites comparison

Role of GIS
• Data integration
– Tally of cases in small areas requires
data on locations of cases and small
area boundaries
– ‘spatial join’ places small area codes
on the case records
– Counting is a standard database
operation

• Data display

Finally
• GIS encourages you to think about
space
• In a ‘conventional’ map of Ireland
areas (EDs, Counties…) are shown in
proportion to their physical size
• There are alternative ways of
displaying maps…

In a cartogram the areas are drawn
proportional to their populations –
this is a “density-equalised” map
projection

A map of the boundaries of counties and
electoral divisions – the areas are drawn
proportional to their physical size

By comparison, the more deprived
western area of Dublin, northern area
of Cork and south east of Limerick are
clearly apparent in the cartogram.

Haase’s deprivation index suggests that the
most deprived populations are in peripheral
rural areas in the north west.

Redrawing the data on the cartogram
reveals wide variation in the SMR, and also
uncovers the urban dimension which is
hidden in the conventional display

Patient data matched to Electoral Divisions –
hospital admissions for psychiatric problems – the
patchwork effect does not suggest any pattern

The cartogram reveals the urban nature
of the problem – those areas which are
more deprived are also those with
higher levels of illness/disability

Residents aged 15+ who are unable to work
through illness or disability: the map pattern
suggests that might be a largely rural
phenomenon

The roads, rail, and urban areas data have
been transformed into the same space as
the cartogram. Dublin and Cork seem
poorly provided with roads…

Other spatial information can be transformed
to the space of the density equalised
projection. Here is the familiar map of main
roads and towns.

Using a cartogram we see that the
incidence of lung cancer is randomly
spatially distributed

Lung cancer in males in New York State [Gastner & Newman: 2004]

Plotting locations of lung cancer cases, we
obtain a map which follows the underlying
population distribution – there appear to be
“clusters”

What about Ireland?
• Potentially hampered by challenge of
geocoding patient records
• Lack of small area population data
– EDs have an average population of 1150
– Wide variation in size (50-28000)
– Socially inhomogenous

Rural addresses
• A major challenge is non-uniqueness
of addresses in rural areas.
• For instance in Moy ED, 11 properties
share the address
‘Lackamore, Lahinch, Co. Clare’

What’s needed
• Postcoding should provide a
mechanism for geocoding patient data
with the granularity required for
planning the delivery of health care –
this has been the case in the UK for a
for long time
• Census data should be made available
for areas with small and similar
populations (cf: UK Output Areas)

Finally…
• GIS has an important role to play
in health studies
• Health data analysis is a rapidly
developing field
• However, in Ireland we are
hampered by data deficiencies –
now is the time for change

Maynooth: image in Google Earth

Central Dublin: image in Google Earth

Taoiseach’s Office: image in Google Earth

Downing Street, London: image in Google Earth

Maynooth: OSi orthophotograph added locally uisng KML

Sellafield: image in Google Earth

NCU – On Line Atlas of Cancer Mortality

The Philippine Institute
for Development
Studies makes an online atlas of socioeconomic indicators
available – contains
some health measures

The future
• Patient empowerment…

• GIS used to merge information from
regulatory agencies on 19196 landfill sites
• Sites buffered to 2km and postcodes
within buffer identified
• Postcoded live births (8.2x106), stillbirths
(43471) and congenital anomalies
(124,597) between 1982 and 1997
• Poisson regression modelling undertaken
• Evidence found for excess risk of
congenital anomaly and low birthweight

• Distance from each
census ward to
nearest hospital
computed
• Map shows number
of patients waiting
longer than 6
months for elective
inpatient care per
available and
unoccupied bed
within 60 minutes
travel time


Slide 60

GIS and Health
Martin Charlton1
Ronan Foley2
Dennis Pringle2
1National

Centre for Geocomputation
2Department of Geography
NUI Maynooth

Outline
• Preliminary observations on GIS
and health
• Examples of GIS in Health:
research and practice
• Mapping people not places

Patterns in Health Data
• Three main areas:
– Mapping of disease incidence
• Relative risk estimation

– Identification of disease clustering
• Surveillance

– Ecological analysis
• Relationship between incidence and
covariates
(Lawson et al, 2003)

Health Data
• Typically patient data is available
in two forms
• Individual – perhaps anonymised
to postcode/zip code level
• Aggregate – counts are available
for a set of zones
• The nature of the data affects the
nature of the analysis

Geographic Information Systems
• GIS is often confused with mapping
• A range of activities which includes
inter alia mapping and analysis
• Will typically involve several datasets
• May also involve several different
pieces of software

Mapping: EpiINFO
• This is a rather simple analysis and
mapping program
• It can be downloaded from the internet
• It doesn’t provide much in the way of
spatial data manipulation…
• … nor are its analytical capabilities very
advanced
• It is easy to use

Spatial Data Manipulation
• GIS isn’t merely concerned with
data display or query
• It’s useful in bringing together
different spatial datasets in the
first stages of an analysis
• This may well require a range of
different geometrical and
database operations

Analysis with Spatial Data
• Statistical analysis usually involves
some separate software.
• Spatial data have some properties
which mean that SPSS isn’t always
appropriate.
• There is now a range of specialist
software – much of it available for
download

GIS may be ideal for data manipulation, display and query,
but it’s not always ideal for analysis – in epidemiology a
variety of analytical software is available … WinBUGS,
GeoBUGS (MRC Biostatistics Unit, Cambridge)

Software to implement
particular methods – for
example scan statistics - can be
downloaded quickly and easily
from the Internet. Kulldorf’s
SaTScan system implements a
range of different scan
statistical models

GIS in Health in Practice
• Facility location – dialysis units
• Facility location – obs & gyn
• Data integration – schizophrenia
in Cavan
• Data manipulation and display –
GMS card holders in Maynooth

1: Provision of Dialysis Units
• There are 5 dialysis units for renal
patients in a region with a population
of about 3,000,000
• Major population centre has two units
about 5 miles apart
• Where would you locate an extra one?
• What would happen if one was
closed?

Location-allocation
• Location-allocation problems deal
with optimum location of facilities
• Attempt to minimise total
weighted travel time/distance
from users to facilities
• What data is needed?

Data requirements
• Digitised road network with classification
of road types
• Mean travel speeds on each type for
urban/non-urban roads
• Extents of urban areas
• Anonymised postcoded patient records
• Postcode->grid reference lookup
• Locations of hospitals in region (both with
and without dialysis units)

GIS processing
• Data integration
• Determine which segments of road network
are urban
• Determine travel times using road type
urban/non-urban speeds
• Tally numbers of patients at each node in
the road network
• Find nearest nodes in network to each
hospital
• Code hospitals as ‘existing’ or ‘potential’
units

Modelling/Analysis
• Carried out using standard GIS
functions
• Convenient ‘front end’ written
using system’s macro language
(save a lot of typing)
• Explore a range of alternatives

Hospital with
dialysis unit

Study region showing hospital locations and urban areas

Patients requiring dialysis (tallied to road nodes)

Classified road network – includes roads outside study area

•

Some alternative
scenarios:

(a) Optimal allocation
of patients
(b) Provide 1 extra
unit
(c) Provide 2 extra
units

(d) Close 1 unit in
city and reallocate
its patients

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

integrate data
process data
model and analyse
generate alternative scenarios
display the results

2: Facility Location
• Two hospitals both provided facilities
for obstetrics, gynaecology, paediatrics
and a special care baby unit.
• Was there a case for rationalisation and
improving the facilities at one hospital
only?
• Would closure disadvantage any
patients?

Data
• Locations of hospitals in study region
• Anonymised postcoded finished
consultant episodes for each of the
four specialities at each hospital
• Classified road network
• Speeds associated with each road
type

Data preparation
• Postcoded records allocated grid
references using Central Postcode
Directory (POSTZON)
• Vector data converted to raster
• Patient/specialty/hospital counts
produced for each raster
• Passage times computed for each
raster cell based on road
classification and speed estimates

Roads, urban areas, patient locations, hospitals in study area

Processing
• Travel time surfaces computed for
each hospital
• Each raster cell contains time
required to reach hospital in minutes
• Assumed walking speed in ‘non road’
areas

Travel time surface for hospital S – darker = shorter time

Travel time surface for hospital T

Time differences
• If we subtract the travel time surfaces areas
nearer one hospital will have positive
values for the difference, and areas nearer
the other will have negative ones
• Boundary between positive and negative
values is boundary of catchment for each
hospital.
• If you’re on the boundary (difference is
zero), it doesn’t matter which hospital you
choose

Roads, patient locations, hospital catchments

Indifference
• If we identify those rasters where the time
difference (ignoring the sign) is, say, 5 –
people in those areas are in the 5 minute
‘indifference’ zone.
• Patients might not be worried about a 5
minute increase in their travel time, they
might be worried about 30 minutes
• Indifference zones can be computed quite
easily

15-minute indifference zone between the two hospitals

Mean access time
• For each cell in the raster we can
compute the product of the number
of patients and the time required to
reach each hospital
• Dividing this by the number of
patients gives a patient weighted
average time for each hospital

Average travel times for the various specialties. There
isn’t much to chose between specialities with the
exception of paediatric episodes.

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

gather data
integrate data
process data
model data
display results

• All carried out using raster
operations

3: Schizophrenia in Cavan
• In 1996 Pringle, Waddington and
Youssef reported on evidence for the
causes of schizophrenia
• Ecological analysis of variation among
EDs
– Cases varied from 0-11 per ED
– Populations varied from 250-2750

• Poisson probabilities suggested that
one area had an unusually high number
of cases – these can be mapped

Male cases
superimposed on plot
of prevalence by ED

Male cases
superimposed on
rectified Landsat
image with ED
boundaries

4: GMS card holders: Maynooth
•Data on GMS – Medical Card holders
drawn under strict data protection
protocols from DOHC
• Anonymised address lists data matched
with GeoDirectory
•Address matching rate of around 77% in
Maynooth

• Interesting range of results around
average for the whole ED

•ED – single value 14.6 % (national average around 30%)
•Small Area – Range from 2% to 70% - noticeable internal variation and
good indicator of elderly or poor populations

•Strategic value in comparing datasets
•Important in policy analysis

Mapping invites comparison

Role of GIS
• Data integration
– Tally of cases in small areas requires
data on locations of cases and small
area boundaries
– ‘spatial join’ places small area codes
on the case records
– Counting is a standard database
operation

• Data display

Finally
• GIS encourages you to think about
space
• In a ‘conventional’ map of Ireland
areas (EDs, Counties…) are shown in
proportion to their physical size
• There are alternative ways of
displaying maps…

In a cartogram the areas are drawn
proportional to their populations –
this is a “density-equalised” map
projection

A map of the boundaries of counties and
electoral divisions – the areas are drawn
proportional to their physical size

By comparison, the more deprived
western area of Dublin, northern area
of Cork and south east of Limerick are
clearly apparent in the cartogram.

Haase’s deprivation index suggests that the
most deprived populations are in peripheral
rural areas in the north west.

Redrawing the data on the cartogram
reveals wide variation in the SMR, and also
uncovers the urban dimension which is
hidden in the conventional display

Patient data matched to Electoral Divisions –
hospital admissions for psychiatric problems – the
patchwork effect does not suggest any pattern

The cartogram reveals the urban nature
of the problem – those areas which are
more deprived are also those with
higher levels of illness/disability

Residents aged 15+ who are unable to work
through illness or disability: the map pattern
suggests that might be a largely rural
phenomenon

The roads, rail, and urban areas data have
been transformed into the same space as
the cartogram. Dublin and Cork seem
poorly provided with roads…

Other spatial information can be transformed
to the space of the density equalised
projection. Here is the familiar map of main
roads and towns.

Using a cartogram we see that the
incidence of lung cancer is randomly
spatially distributed

Lung cancer in males in New York State [Gastner & Newman: 2004]

Plotting locations of lung cancer cases, we
obtain a map which follows the underlying
population distribution – there appear to be
“clusters”

What about Ireland?
• Potentially hampered by challenge of
geocoding patient records
• Lack of small area population data
– EDs have an average population of 1150
– Wide variation in size (50-28000)
– Socially inhomogenous

Rural addresses
• A major challenge is non-uniqueness
of addresses in rural areas.
• For instance in Moy ED, 11 properties
share the address
‘Lackamore, Lahinch, Co. Clare’

What’s needed
• Postcoding should provide a
mechanism for geocoding patient data
with the granularity required for
planning the delivery of health care –
this has been the case in the UK for a
for long time
• Census data should be made available
for areas with small and similar
populations (cf: UK Output Areas)

Finally…
• GIS has an important role to play
in health studies
• Health data analysis is a rapidly
developing field
• However, in Ireland we are
hampered by data deficiencies –
now is the time for change

Maynooth: image in Google Earth

Central Dublin: image in Google Earth

Taoiseach’s Office: image in Google Earth

Downing Street, London: image in Google Earth

Maynooth: OSi orthophotograph added locally uisng KML

Sellafield: image in Google Earth

NCU – On Line Atlas of Cancer Mortality

The Philippine Institute
for Development
Studies makes an online atlas of socioeconomic indicators
available – contains
some health measures

The future
• Patient empowerment…

• GIS used to merge information from
regulatory agencies on 19196 landfill sites
• Sites buffered to 2km and postcodes
within buffer identified
• Postcoded live births (8.2x106), stillbirths
(43471) and congenital anomalies
(124,597) between 1982 and 1997
• Poisson regression modelling undertaken
• Evidence found for excess risk of
congenital anomaly and low birthweight

• Distance from each
census ward to
nearest hospital
computed
• Map shows number
of patients waiting
longer than 6
months for elective
inpatient care per
available and
unoccupied bed
within 60 minutes
travel time


Slide 61

GIS and Health
Martin Charlton1
Ronan Foley2
Dennis Pringle2
1National

Centre for Geocomputation
2Department of Geography
NUI Maynooth

Outline
• Preliminary observations on GIS
and health
• Examples of GIS in Health:
research and practice
• Mapping people not places

Patterns in Health Data
• Three main areas:
– Mapping of disease incidence
• Relative risk estimation

– Identification of disease clustering
• Surveillance

– Ecological analysis
• Relationship between incidence and
covariates
(Lawson et al, 2003)

Health Data
• Typically patient data is available
in two forms
• Individual – perhaps anonymised
to postcode/zip code level
• Aggregate – counts are available
for a set of zones
• The nature of the data affects the
nature of the analysis

Geographic Information Systems
• GIS is often confused with mapping
• A range of activities which includes
inter alia mapping and analysis
• Will typically involve several datasets
• May also involve several different
pieces of software

Mapping: EpiINFO
• This is a rather simple analysis and
mapping program
• It can be downloaded from the internet
• It doesn’t provide much in the way of
spatial data manipulation…
• … nor are its analytical capabilities very
advanced
• It is easy to use

Spatial Data Manipulation
• GIS isn’t merely concerned with
data display or query
• It’s useful in bringing together
different spatial datasets in the
first stages of an analysis
• This may well require a range of
different geometrical and
database operations

Analysis with Spatial Data
• Statistical analysis usually involves
some separate software.
• Spatial data have some properties
which mean that SPSS isn’t always
appropriate.
• There is now a range of specialist
software – much of it available for
download

GIS may be ideal for data manipulation, display and query,
but it’s not always ideal for analysis – in epidemiology a
variety of analytical software is available … WinBUGS,
GeoBUGS (MRC Biostatistics Unit, Cambridge)

Software to implement
particular methods – for
example scan statistics - can be
downloaded quickly and easily
from the Internet. Kulldorf’s
SaTScan system implements a
range of different scan
statistical models

GIS in Health in Practice
• Facility location – dialysis units
• Facility location – obs & gyn
• Data integration – schizophrenia
in Cavan
• Data manipulation and display –
GMS card holders in Maynooth

1: Provision of Dialysis Units
• There are 5 dialysis units for renal
patients in a region with a population
of about 3,000,000
• Major population centre has two units
about 5 miles apart
• Where would you locate an extra one?
• What would happen if one was
closed?

Location-allocation
• Location-allocation problems deal
with optimum location of facilities
• Attempt to minimise total
weighted travel time/distance
from users to facilities
• What data is needed?

Data requirements
• Digitised road network with classification
of road types
• Mean travel speeds on each type for
urban/non-urban roads
• Extents of urban areas
• Anonymised postcoded patient records
• Postcode->grid reference lookup
• Locations of hospitals in region (both with
and without dialysis units)

GIS processing
• Data integration
• Determine which segments of road network
are urban
• Determine travel times using road type
urban/non-urban speeds
• Tally numbers of patients at each node in
the road network
• Find nearest nodes in network to each
hospital
• Code hospitals as ‘existing’ or ‘potential’
units

Modelling/Analysis
• Carried out using standard GIS
functions
• Convenient ‘front end’ written
using system’s macro language
(save a lot of typing)
• Explore a range of alternatives

Hospital with
dialysis unit

Study region showing hospital locations and urban areas

Patients requiring dialysis (tallied to road nodes)

Classified road network – includes roads outside study area

•

Some alternative
scenarios:

(a) Optimal allocation
of patients
(b) Provide 1 extra
unit
(c) Provide 2 extra
units

(d) Close 1 unit in
city and reallocate
its patients

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

integrate data
process data
model and analyse
generate alternative scenarios
display the results

2: Facility Location
• Two hospitals both provided facilities
for obstetrics, gynaecology, paediatrics
and a special care baby unit.
• Was there a case for rationalisation and
improving the facilities at one hospital
only?
• Would closure disadvantage any
patients?

Data
• Locations of hospitals in study region
• Anonymised postcoded finished
consultant episodes for each of the
four specialities at each hospital
• Classified road network
• Speeds associated with each road
type

Data preparation
• Postcoded records allocated grid
references using Central Postcode
Directory (POSTZON)
• Vector data converted to raster
• Patient/specialty/hospital counts
produced for each raster
• Passage times computed for each
raster cell based on road
classification and speed estimates

Roads, urban areas, patient locations, hospitals in study area

Processing
• Travel time surfaces computed for
each hospital
• Each raster cell contains time
required to reach hospital in minutes
• Assumed walking speed in ‘non road’
areas

Travel time surface for hospital S – darker = shorter time

Travel time surface for hospital T

Time differences
• If we subtract the travel time surfaces areas
nearer one hospital will have positive
values for the difference, and areas nearer
the other will have negative ones
• Boundary between positive and negative
values is boundary of catchment for each
hospital.
• If you’re on the boundary (difference is
zero), it doesn’t matter which hospital you
choose

Roads, patient locations, hospital catchments

Indifference
• If we identify those rasters where the time
difference (ignoring the sign) is, say, 5 –
people in those areas are in the 5 minute
‘indifference’ zone.
• Patients might not be worried about a 5
minute increase in their travel time, they
might be worried about 30 minutes
• Indifference zones can be computed quite
easily

15-minute indifference zone between the two hospitals

Mean access time
• For each cell in the raster we can
compute the product of the number
of patients and the time required to
reach each hospital
• Dividing this by the number of
patients gives a patient weighted
average time for each hospital

Average travel times for the various specialties. There
isn’t much to chose between specialities with the
exception of paediatric episodes.

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

gather data
integrate data
process data
model data
display results

• All carried out using raster
operations

3: Schizophrenia in Cavan
• In 1996 Pringle, Waddington and
Youssef reported on evidence for the
causes of schizophrenia
• Ecological analysis of variation among
EDs
– Cases varied from 0-11 per ED
– Populations varied from 250-2750

• Poisson probabilities suggested that
one area had an unusually high number
of cases – these can be mapped

Male cases
superimposed on plot
of prevalence by ED

Male cases
superimposed on
rectified Landsat
image with ED
boundaries

4: GMS card holders: Maynooth
•Data on GMS – Medical Card holders
drawn under strict data protection
protocols from DOHC
• Anonymised address lists data matched
with GeoDirectory
•Address matching rate of around 77% in
Maynooth

• Interesting range of results around
average for the whole ED

•ED – single value 14.6 % (national average around 30%)
•Small Area – Range from 2% to 70% - noticeable internal variation and
good indicator of elderly or poor populations

•Strategic value in comparing datasets
•Important in policy analysis

Mapping invites comparison

Role of GIS
• Data integration
– Tally of cases in small areas requires
data on locations of cases and small
area boundaries
– ‘spatial join’ places small area codes
on the case records
– Counting is a standard database
operation

• Data display

Finally
• GIS encourages you to think about
space
• In a ‘conventional’ map of Ireland
areas (EDs, Counties…) are shown in
proportion to their physical size
• There are alternative ways of
displaying maps…

In a cartogram the areas are drawn
proportional to their populations –
this is a “density-equalised” map
projection

A map of the boundaries of counties and
electoral divisions – the areas are drawn
proportional to their physical size

By comparison, the more deprived
western area of Dublin, northern area
of Cork and south east of Limerick are
clearly apparent in the cartogram.

Haase’s deprivation index suggests that the
most deprived populations are in peripheral
rural areas in the north west.

Redrawing the data on the cartogram
reveals wide variation in the SMR, and also
uncovers the urban dimension which is
hidden in the conventional display

Patient data matched to Electoral Divisions –
hospital admissions for psychiatric problems – the
patchwork effect does not suggest any pattern

The cartogram reveals the urban nature
of the problem – those areas which are
more deprived are also those with
higher levels of illness/disability

Residents aged 15+ who are unable to work
through illness or disability: the map pattern
suggests that might be a largely rural
phenomenon

The roads, rail, and urban areas data have
been transformed into the same space as
the cartogram. Dublin and Cork seem
poorly provided with roads…

Other spatial information can be transformed
to the space of the density equalised
projection. Here is the familiar map of main
roads and towns.

Using a cartogram we see that the
incidence of lung cancer is randomly
spatially distributed

Lung cancer in males in New York State [Gastner & Newman: 2004]

Plotting locations of lung cancer cases, we
obtain a map which follows the underlying
population distribution – there appear to be
“clusters”

What about Ireland?
• Potentially hampered by challenge of
geocoding patient records
• Lack of small area population data
– EDs have an average population of 1150
– Wide variation in size (50-28000)
– Socially inhomogenous

Rural addresses
• A major challenge is non-uniqueness
of addresses in rural areas.
• For instance in Moy ED, 11 properties
share the address
‘Lackamore, Lahinch, Co. Clare’

What’s needed
• Postcoding should provide a
mechanism for geocoding patient data
with the granularity required for
planning the delivery of health care –
this has been the case in the UK for a
for long time
• Census data should be made available
for areas with small and similar
populations (cf: UK Output Areas)

Finally…
• GIS has an important role to play
in health studies
• Health data analysis is a rapidly
developing field
• However, in Ireland we are
hampered by data deficiencies –
now is the time for change

Maynooth: image in Google Earth

Central Dublin: image in Google Earth

Taoiseach’s Office: image in Google Earth

Downing Street, London: image in Google Earth

Maynooth: OSi orthophotograph added locally uisng KML

Sellafield: image in Google Earth

NCU – On Line Atlas of Cancer Mortality

The Philippine Institute
for Development
Studies makes an online atlas of socioeconomic indicators
available – contains
some health measures

The future
• Patient empowerment…

• GIS used to merge information from
regulatory agencies on 19196 landfill sites
• Sites buffered to 2km and postcodes
within buffer identified
• Postcoded live births (8.2x106), stillbirths
(43471) and congenital anomalies
(124,597) between 1982 and 1997
• Poisson regression modelling undertaken
• Evidence found for excess risk of
congenital anomaly and low birthweight

• Distance from each
census ward to
nearest hospital
computed
• Map shows number
of patients waiting
longer than 6
months for elective
inpatient care per
available and
unoccupied bed
within 60 minutes
travel time


Slide 62

GIS and Health
Martin Charlton1
Ronan Foley2
Dennis Pringle2
1National

Centre for Geocomputation
2Department of Geography
NUI Maynooth

Outline
• Preliminary observations on GIS
and health
• Examples of GIS in Health:
research and practice
• Mapping people not places

Patterns in Health Data
• Three main areas:
– Mapping of disease incidence
• Relative risk estimation

– Identification of disease clustering
• Surveillance

– Ecological analysis
• Relationship between incidence and
covariates
(Lawson et al, 2003)

Health Data
• Typically patient data is available
in two forms
• Individual – perhaps anonymised
to postcode/zip code level
• Aggregate – counts are available
for a set of zones
• The nature of the data affects the
nature of the analysis

Geographic Information Systems
• GIS is often confused with mapping
• A range of activities which includes
inter alia mapping and analysis
• Will typically involve several datasets
• May also involve several different
pieces of software

Mapping: EpiINFO
• This is a rather simple analysis and
mapping program
• It can be downloaded from the internet
• It doesn’t provide much in the way of
spatial data manipulation…
• … nor are its analytical capabilities very
advanced
• It is easy to use

Spatial Data Manipulation
• GIS isn’t merely concerned with
data display or query
• It’s useful in bringing together
different spatial datasets in the
first stages of an analysis
• This may well require a range of
different geometrical and
database operations

Analysis with Spatial Data
• Statistical analysis usually involves
some separate software.
• Spatial data have some properties
which mean that SPSS isn’t always
appropriate.
• There is now a range of specialist
software – much of it available for
download

GIS may be ideal for data manipulation, display and query,
but it’s not always ideal for analysis – in epidemiology a
variety of analytical software is available … WinBUGS,
GeoBUGS (MRC Biostatistics Unit, Cambridge)

Software to implement
particular methods – for
example scan statistics - can be
downloaded quickly and easily
from the Internet. Kulldorf’s
SaTScan system implements a
range of different scan
statistical models

GIS in Health in Practice
• Facility location – dialysis units
• Facility location – obs & gyn
• Data integration – schizophrenia
in Cavan
• Data manipulation and display –
GMS card holders in Maynooth

1: Provision of Dialysis Units
• There are 5 dialysis units for renal
patients in a region with a population
of about 3,000,000
• Major population centre has two units
about 5 miles apart
• Where would you locate an extra one?
• What would happen if one was
closed?

Location-allocation
• Location-allocation problems deal
with optimum location of facilities
• Attempt to minimise total
weighted travel time/distance
from users to facilities
• What data is needed?

Data requirements
• Digitised road network with classification
of road types
• Mean travel speeds on each type for
urban/non-urban roads
• Extents of urban areas
• Anonymised postcoded patient records
• Postcode->grid reference lookup
• Locations of hospitals in region (both with
and without dialysis units)

GIS processing
• Data integration
• Determine which segments of road network
are urban
• Determine travel times using road type
urban/non-urban speeds
• Tally numbers of patients at each node in
the road network
• Find nearest nodes in network to each
hospital
• Code hospitals as ‘existing’ or ‘potential’
units

Modelling/Analysis
• Carried out using standard GIS
functions
• Convenient ‘front end’ written
using system’s macro language
(save a lot of typing)
• Explore a range of alternatives

Hospital with
dialysis unit

Study region showing hospital locations and urban areas

Patients requiring dialysis (tallied to road nodes)

Classified road network – includes roads outside study area

•

Some alternative
scenarios:

(a) Optimal allocation
of patients
(b) Provide 1 extra
unit
(c) Provide 2 extra
units

(d) Close 1 unit in
city and reallocate
its patients

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

integrate data
process data
model and analyse
generate alternative scenarios
display the results

2: Facility Location
• Two hospitals both provided facilities
for obstetrics, gynaecology, paediatrics
and a special care baby unit.
• Was there a case for rationalisation and
improving the facilities at one hospital
only?
• Would closure disadvantage any
patients?

Data
• Locations of hospitals in study region
• Anonymised postcoded finished
consultant episodes for each of the
four specialities at each hospital
• Classified road network
• Speeds associated with each road
type

Data preparation
• Postcoded records allocated grid
references using Central Postcode
Directory (POSTZON)
• Vector data converted to raster
• Patient/specialty/hospital counts
produced for each raster
• Passage times computed for each
raster cell based on road
classification and speed estimates

Roads, urban areas, patient locations, hospitals in study area

Processing
• Travel time surfaces computed for
each hospital
• Each raster cell contains time
required to reach hospital in minutes
• Assumed walking speed in ‘non road’
areas

Travel time surface for hospital S – darker = shorter time

Travel time surface for hospital T

Time differences
• If we subtract the travel time surfaces areas
nearer one hospital will have positive
values for the difference, and areas nearer
the other will have negative ones
• Boundary between positive and negative
values is boundary of catchment for each
hospital.
• If you’re on the boundary (difference is
zero), it doesn’t matter which hospital you
choose

Roads, patient locations, hospital catchments

Indifference
• If we identify those rasters where the time
difference (ignoring the sign) is, say, 5 –
people in those areas are in the 5 minute
‘indifference’ zone.
• Patients might not be worried about a 5
minute increase in their travel time, they
might be worried about 30 minutes
• Indifference zones can be computed quite
easily

15-minute indifference zone between the two hospitals

Mean access time
• For each cell in the raster we can
compute the product of the number
of patients and the time required to
reach each hospital
• Dividing this by the number of
patients gives a patient weighted
average time for each hospital

Average travel times for the various specialties. There
isn’t much to chose between specialities with the
exception of paediatric episodes.

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

gather data
integrate data
process data
model data
display results

• All carried out using raster
operations

3: Schizophrenia in Cavan
• In 1996 Pringle, Waddington and
Youssef reported on evidence for the
causes of schizophrenia
• Ecological analysis of variation among
EDs
– Cases varied from 0-11 per ED
– Populations varied from 250-2750

• Poisson probabilities suggested that
one area had an unusually high number
of cases – these can be mapped

Male cases
superimposed on plot
of prevalence by ED

Male cases
superimposed on
rectified Landsat
image with ED
boundaries

4: GMS card holders: Maynooth
•Data on GMS – Medical Card holders
drawn under strict data protection
protocols from DOHC
• Anonymised address lists data matched
with GeoDirectory
•Address matching rate of around 77% in
Maynooth

• Interesting range of results around
average for the whole ED

•ED – single value 14.6 % (national average around 30%)
•Small Area – Range from 2% to 70% - noticeable internal variation and
good indicator of elderly or poor populations

•Strategic value in comparing datasets
•Important in policy analysis

Mapping invites comparison

Role of GIS
• Data integration
– Tally of cases in small areas requires
data on locations of cases and small
area boundaries
– ‘spatial join’ places small area codes
on the case records
– Counting is a standard database
operation

• Data display

Finally
• GIS encourages you to think about
space
• In a ‘conventional’ map of Ireland
areas (EDs, Counties…) are shown in
proportion to their physical size
• There are alternative ways of
displaying maps…

In a cartogram the areas are drawn
proportional to their populations –
this is a “density-equalised” map
projection

A map of the boundaries of counties and
electoral divisions – the areas are drawn
proportional to their physical size

By comparison, the more deprived
western area of Dublin, northern area
of Cork and south east of Limerick are
clearly apparent in the cartogram.

Haase’s deprivation index suggests that the
most deprived populations are in peripheral
rural areas in the north west.

Redrawing the data on the cartogram
reveals wide variation in the SMR, and also
uncovers the urban dimension which is
hidden in the conventional display

Patient data matched to Electoral Divisions –
hospital admissions for psychiatric problems – the
patchwork effect does not suggest any pattern

The cartogram reveals the urban nature
of the problem – those areas which are
more deprived are also those with
higher levels of illness/disability

Residents aged 15+ who are unable to work
through illness or disability: the map pattern
suggests that might be a largely rural
phenomenon

The roads, rail, and urban areas data have
been transformed into the same space as
the cartogram. Dublin and Cork seem
poorly provided with roads…

Other spatial information can be transformed
to the space of the density equalised
projection. Here is the familiar map of main
roads and towns.

Using a cartogram we see that the
incidence of lung cancer is randomly
spatially distributed

Lung cancer in males in New York State [Gastner & Newman: 2004]

Plotting locations of lung cancer cases, we
obtain a map which follows the underlying
population distribution – there appear to be
“clusters”

What about Ireland?
• Potentially hampered by challenge of
geocoding patient records
• Lack of small area population data
– EDs have an average population of 1150
– Wide variation in size (50-28000)
– Socially inhomogenous

Rural addresses
• A major challenge is non-uniqueness
of addresses in rural areas.
• For instance in Moy ED, 11 properties
share the address
‘Lackamore, Lahinch, Co. Clare’

What’s needed
• Postcoding should provide a
mechanism for geocoding patient data
with the granularity required for
planning the delivery of health care –
this has been the case in the UK for a
for long time
• Census data should be made available
for areas with small and similar
populations (cf: UK Output Areas)

Finally…
• GIS has an important role to play
in health studies
• Health data analysis is a rapidly
developing field
• However, in Ireland we are
hampered by data deficiencies –
now is the time for change

Maynooth: image in Google Earth

Central Dublin: image in Google Earth

Taoiseach’s Office: image in Google Earth

Downing Street, London: image in Google Earth

Maynooth: OSi orthophotograph added locally uisng KML

Sellafield: image in Google Earth

NCU – On Line Atlas of Cancer Mortality

The Philippine Institute
for Development
Studies makes an online atlas of socioeconomic indicators
available – contains
some health measures

The future
• Patient empowerment…

• GIS used to merge information from
regulatory agencies on 19196 landfill sites
• Sites buffered to 2km and postcodes
within buffer identified
• Postcoded live births (8.2x106), stillbirths
(43471) and congenital anomalies
(124,597) between 1982 and 1997
• Poisson regression modelling undertaken
• Evidence found for excess risk of
congenital anomaly and low birthweight

• Distance from each
census ward to
nearest hospital
computed
• Map shows number
of patients waiting
longer than 6
months for elective
inpatient care per
available and
unoccupied bed
within 60 minutes
travel time


Slide 63

GIS and Health
Martin Charlton1
Ronan Foley2
Dennis Pringle2
1National

Centre for Geocomputation
2Department of Geography
NUI Maynooth

Outline
• Preliminary observations on GIS
and health
• Examples of GIS in Health:
research and practice
• Mapping people not places

Patterns in Health Data
• Three main areas:
– Mapping of disease incidence
• Relative risk estimation

– Identification of disease clustering
• Surveillance

– Ecological analysis
• Relationship between incidence and
covariates
(Lawson et al, 2003)

Health Data
• Typically patient data is available
in two forms
• Individual – perhaps anonymised
to postcode/zip code level
• Aggregate – counts are available
for a set of zones
• The nature of the data affects the
nature of the analysis

Geographic Information Systems
• GIS is often confused with mapping
• A range of activities which includes
inter alia mapping and analysis
• Will typically involve several datasets
• May also involve several different
pieces of software

Mapping: EpiINFO
• This is a rather simple analysis and
mapping program
• It can be downloaded from the internet
• It doesn’t provide much in the way of
spatial data manipulation…
• … nor are its analytical capabilities very
advanced
• It is easy to use

Spatial Data Manipulation
• GIS isn’t merely concerned with
data display or query
• It’s useful in bringing together
different spatial datasets in the
first stages of an analysis
• This may well require a range of
different geometrical and
database operations

Analysis with Spatial Data
• Statistical analysis usually involves
some separate software.
• Spatial data have some properties
which mean that SPSS isn’t always
appropriate.
• There is now a range of specialist
software – much of it available for
download

GIS may be ideal for data manipulation, display and query,
but it’s not always ideal for analysis – in epidemiology a
variety of analytical software is available … WinBUGS,
GeoBUGS (MRC Biostatistics Unit, Cambridge)

Software to implement
particular methods – for
example scan statistics - can be
downloaded quickly and easily
from the Internet. Kulldorf’s
SaTScan system implements a
range of different scan
statistical models

GIS in Health in Practice
• Facility location – dialysis units
• Facility location – obs & gyn
• Data integration – schizophrenia
in Cavan
• Data manipulation and display –
GMS card holders in Maynooth

1: Provision of Dialysis Units
• There are 5 dialysis units for renal
patients in a region with a population
of about 3,000,000
• Major population centre has two units
about 5 miles apart
• Where would you locate an extra one?
• What would happen if one was
closed?

Location-allocation
• Location-allocation problems deal
with optimum location of facilities
• Attempt to minimise total
weighted travel time/distance
from users to facilities
• What data is needed?

Data requirements
• Digitised road network with classification
of road types
• Mean travel speeds on each type for
urban/non-urban roads
• Extents of urban areas
• Anonymised postcoded patient records
• Postcode->grid reference lookup
• Locations of hospitals in region (both with
and without dialysis units)

GIS processing
• Data integration
• Determine which segments of road network
are urban
• Determine travel times using road type
urban/non-urban speeds
• Tally numbers of patients at each node in
the road network
• Find nearest nodes in network to each
hospital
• Code hospitals as ‘existing’ or ‘potential’
units

Modelling/Analysis
• Carried out using standard GIS
functions
• Convenient ‘front end’ written
using system’s macro language
(save a lot of typing)
• Explore a range of alternatives

Hospital with
dialysis unit

Study region showing hospital locations and urban areas

Patients requiring dialysis (tallied to road nodes)

Classified road network – includes roads outside study area

•

Some alternative
scenarios:

(a) Optimal allocation
of patients
(b) Provide 1 extra
unit
(c) Provide 2 extra
units

(d) Close 1 unit in
city and reallocate
its patients

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

integrate data
process data
model and analyse
generate alternative scenarios
display the results

2: Facility Location
• Two hospitals both provided facilities
for obstetrics, gynaecology, paediatrics
and a special care baby unit.
• Was there a case for rationalisation and
improving the facilities at one hospital
only?
• Would closure disadvantage any
patients?

Data
• Locations of hospitals in study region
• Anonymised postcoded finished
consultant episodes for each of the
four specialities at each hospital
• Classified road network
• Speeds associated with each road
type

Data preparation
• Postcoded records allocated grid
references using Central Postcode
Directory (POSTZON)
• Vector data converted to raster
• Patient/specialty/hospital counts
produced for each raster
• Passage times computed for each
raster cell based on road
classification and speed estimates

Roads, urban areas, patient locations, hospitals in study area

Processing
• Travel time surfaces computed for
each hospital
• Each raster cell contains time
required to reach hospital in minutes
• Assumed walking speed in ‘non road’
areas

Travel time surface for hospital S – darker = shorter time

Travel time surface for hospital T

Time differences
• If we subtract the travel time surfaces areas
nearer one hospital will have positive
values for the difference, and areas nearer
the other will have negative ones
• Boundary between positive and negative
values is boundary of catchment for each
hospital.
• If you’re on the boundary (difference is
zero), it doesn’t matter which hospital you
choose

Roads, patient locations, hospital catchments

Indifference
• If we identify those rasters where the time
difference (ignoring the sign) is, say, 5 –
people in those areas are in the 5 minute
‘indifference’ zone.
• Patients might not be worried about a 5
minute increase in their travel time, they
might be worried about 30 minutes
• Indifference zones can be computed quite
easily

15-minute indifference zone between the two hospitals

Mean access time
• For each cell in the raster we can
compute the product of the number
of patients and the time required to
reach each hospital
• Dividing this by the number of
patients gives a patient weighted
average time for each hospital

Average travel times for the various specialties. There
isn’t much to chose between specialities with the
exception of paediatric episodes.

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

gather data
integrate data
process data
model data
display results

• All carried out using raster
operations

3: Schizophrenia in Cavan
• In 1996 Pringle, Waddington and
Youssef reported on evidence for the
causes of schizophrenia
• Ecological analysis of variation among
EDs
– Cases varied from 0-11 per ED
– Populations varied from 250-2750

• Poisson probabilities suggested that
one area had an unusually high number
of cases – these can be mapped

Male cases
superimposed on plot
of prevalence by ED

Male cases
superimposed on
rectified Landsat
image with ED
boundaries

4: GMS card holders: Maynooth
•Data on GMS – Medical Card holders
drawn under strict data protection
protocols from DOHC
• Anonymised address lists data matched
with GeoDirectory
•Address matching rate of around 77% in
Maynooth

• Interesting range of results around
average for the whole ED

•ED – single value 14.6 % (national average around 30%)
•Small Area – Range from 2% to 70% - noticeable internal variation and
good indicator of elderly or poor populations

•Strategic value in comparing datasets
•Important in policy analysis

Mapping invites comparison

Role of GIS
• Data integration
– Tally of cases in small areas requires
data on locations of cases and small
area boundaries
– ‘spatial join’ places small area codes
on the case records
– Counting is a standard database
operation

• Data display

Finally
• GIS encourages you to think about
space
• In a ‘conventional’ map of Ireland
areas (EDs, Counties…) are shown in
proportion to their physical size
• There are alternative ways of
displaying maps…

In a cartogram the areas are drawn
proportional to their populations –
this is a “density-equalised” map
projection

A map of the boundaries of counties and
electoral divisions – the areas are drawn
proportional to their physical size

By comparison, the more deprived
western area of Dublin, northern area
of Cork and south east of Limerick are
clearly apparent in the cartogram.

Haase’s deprivation index suggests that the
most deprived populations are in peripheral
rural areas in the north west.

Redrawing the data on the cartogram
reveals wide variation in the SMR, and also
uncovers the urban dimension which is
hidden in the conventional display

Patient data matched to Electoral Divisions –
hospital admissions for psychiatric problems – the
patchwork effect does not suggest any pattern

The cartogram reveals the urban nature
of the problem – those areas which are
more deprived are also those with
higher levels of illness/disability

Residents aged 15+ who are unable to work
through illness or disability: the map pattern
suggests that might be a largely rural
phenomenon

The roads, rail, and urban areas data have
been transformed into the same space as
the cartogram. Dublin and Cork seem
poorly provided with roads…

Other spatial information can be transformed
to the space of the density equalised
projection. Here is the familiar map of main
roads and towns.

Using a cartogram we see that the
incidence of lung cancer is randomly
spatially distributed

Lung cancer in males in New York State [Gastner & Newman: 2004]

Plotting locations of lung cancer cases, we
obtain a map which follows the underlying
population distribution – there appear to be
“clusters”

What about Ireland?
• Potentially hampered by challenge of
geocoding patient records
• Lack of small area population data
– EDs have an average population of 1150
– Wide variation in size (50-28000)
– Socially inhomogenous

Rural addresses
• A major challenge is non-uniqueness
of addresses in rural areas.
• For instance in Moy ED, 11 properties
share the address
‘Lackamore, Lahinch, Co. Clare’

What’s needed
• Postcoding should provide a
mechanism for geocoding patient data
with the granularity required for
planning the delivery of health care –
this has been the case in the UK for a
for long time
• Census data should be made available
for areas with small and similar
populations (cf: UK Output Areas)

Finally…
• GIS has an important role to play
in health studies
• Health data analysis is a rapidly
developing field
• However, in Ireland we are
hampered by data deficiencies –
now is the time for change

Maynooth: image in Google Earth

Central Dublin: image in Google Earth

Taoiseach’s Office: image in Google Earth

Downing Street, London: image in Google Earth

Maynooth: OSi orthophotograph added locally uisng KML

Sellafield: image in Google Earth

NCU – On Line Atlas of Cancer Mortality

The Philippine Institute
for Development
Studies makes an online atlas of socioeconomic indicators
available – contains
some health measures

The future
• Patient empowerment…

• GIS used to merge information from
regulatory agencies on 19196 landfill sites
• Sites buffered to 2km and postcodes
within buffer identified
• Postcoded live births (8.2x106), stillbirths
(43471) and congenital anomalies
(124,597) between 1982 and 1997
• Poisson regression modelling undertaken
• Evidence found for excess risk of
congenital anomaly and low birthweight

• Distance from each
census ward to
nearest hospital
computed
• Map shows number
of patients waiting
longer than 6
months for elective
inpatient care per
available and
unoccupied bed
within 60 minutes
travel time


Slide 64

GIS and Health
Martin Charlton1
Ronan Foley2
Dennis Pringle2
1National

Centre for Geocomputation
2Department of Geography
NUI Maynooth

Outline
• Preliminary observations on GIS
and health
• Examples of GIS in Health:
research and practice
• Mapping people not places

Patterns in Health Data
• Three main areas:
– Mapping of disease incidence
• Relative risk estimation

– Identification of disease clustering
• Surveillance

– Ecological analysis
• Relationship between incidence and
covariates
(Lawson et al, 2003)

Health Data
• Typically patient data is available
in two forms
• Individual – perhaps anonymised
to postcode/zip code level
• Aggregate – counts are available
for a set of zones
• The nature of the data affects the
nature of the analysis

Geographic Information Systems
• GIS is often confused with mapping
• A range of activities which includes
inter alia mapping and analysis
• Will typically involve several datasets
• May also involve several different
pieces of software

Mapping: EpiINFO
• This is a rather simple analysis and
mapping program
• It can be downloaded from the internet
• It doesn’t provide much in the way of
spatial data manipulation…
• … nor are its analytical capabilities very
advanced
• It is easy to use

Spatial Data Manipulation
• GIS isn’t merely concerned with
data display or query
• It’s useful in bringing together
different spatial datasets in the
first stages of an analysis
• This may well require a range of
different geometrical and
database operations

Analysis with Spatial Data
• Statistical analysis usually involves
some separate software.
• Spatial data have some properties
which mean that SPSS isn’t always
appropriate.
• There is now a range of specialist
software – much of it available for
download

GIS may be ideal for data manipulation, display and query,
but it’s not always ideal for analysis – in epidemiology a
variety of analytical software is available … WinBUGS,
GeoBUGS (MRC Biostatistics Unit, Cambridge)

Software to implement
particular methods – for
example scan statistics - can be
downloaded quickly and easily
from the Internet. Kulldorf’s
SaTScan system implements a
range of different scan
statistical models

GIS in Health in Practice
• Facility location – dialysis units
• Facility location – obs & gyn
• Data integration – schizophrenia
in Cavan
• Data manipulation and display –
GMS card holders in Maynooth

1: Provision of Dialysis Units
• There are 5 dialysis units for renal
patients in a region with a population
of about 3,000,000
• Major population centre has two units
about 5 miles apart
• Where would you locate an extra one?
• What would happen if one was
closed?

Location-allocation
• Location-allocation problems deal
with optimum location of facilities
• Attempt to minimise total
weighted travel time/distance
from users to facilities
• What data is needed?

Data requirements
• Digitised road network with classification
of road types
• Mean travel speeds on each type for
urban/non-urban roads
• Extents of urban areas
• Anonymised postcoded patient records
• Postcode->grid reference lookup
• Locations of hospitals in region (both with
and without dialysis units)

GIS processing
• Data integration
• Determine which segments of road network
are urban
• Determine travel times using road type
urban/non-urban speeds
• Tally numbers of patients at each node in
the road network
• Find nearest nodes in network to each
hospital
• Code hospitals as ‘existing’ or ‘potential’
units

Modelling/Analysis
• Carried out using standard GIS
functions
• Convenient ‘front end’ written
using system’s macro language
(save a lot of typing)
• Explore a range of alternatives

Hospital with
dialysis unit

Study region showing hospital locations and urban areas

Patients requiring dialysis (tallied to road nodes)

Classified road network – includes roads outside study area

•

Some alternative
scenarios:

(a) Optimal allocation
of patients
(b) Provide 1 extra
unit
(c) Provide 2 extra
units

(d) Close 1 unit in
city and reallocate
its patients

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

integrate data
process data
model and analyse
generate alternative scenarios
display the results

2: Facility Location
• Two hospitals both provided facilities
for obstetrics, gynaecology, paediatrics
and a special care baby unit.
• Was there a case for rationalisation and
improving the facilities at one hospital
only?
• Would closure disadvantage any
patients?

Data
• Locations of hospitals in study region
• Anonymised postcoded finished
consultant episodes for each of the
four specialities at each hospital
• Classified road network
• Speeds associated with each road
type

Data preparation
• Postcoded records allocated grid
references using Central Postcode
Directory (POSTZON)
• Vector data converted to raster
• Patient/specialty/hospital counts
produced for each raster
• Passage times computed for each
raster cell based on road
classification and speed estimates

Roads, urban areas, patient locations, hospitals in study area

Processing
• Travel time surfaces computed for
each hospital
• Each raster cell contains time
required to reach hospital in minutes
• Assumed walking speed in ‘non road’
areas

Travel time surface for hospital S – darker = shorter time

Travel time surface for hospital T

Time differences
• If we subtract the travel time surfaces areas
nearer one hospital will have positive
values for the difference, and areas nearer
the other will have negative ones
• Boundary between positive and negative
values is boundary of catchment for each
hospital.
• If you’re on the boundary (difference is
zero), it doesn’t matter which hospital you
choose

Roads, patient locations, hospital catchments

Indifference
• If we identify those rasters where the time
difference (ignoring the sign) is, say, 5 –
people in those areas are in the 5 minute
‘indifference’ zone.
• Patients might not be worried about a 5
minute increase in their travel time, they
might be worried about 30 minutes
• Indifference zones can be computed quite
easily

15-minute indifference zone between the two hospitals

Mean access time
• For each cell in the raster we can
compute the product of the number
of patients and the time required to
reach each hospital
• Dividing this by the number of
patients gives a patient weighted
average time for each hospital

Average travel times for the various specialties. There
isn’t much to chose between specialities with the
exception of paediatric episodes.

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

gather data
integrate data
process data
model data
display results

• All carried out using raster
operations

3: Schizophrenia in Cavan
• In 1996 Pringle, Waddington and
Youssef reported on evidence for the
causes of schizophrenia
• Ecological analysis of variation among
EDs
– Cases varied from 0-11 per ED
– Populations varied from 250-2750

• Poisson probabilities suggested that
one area had an unusually high number
of cases – these can be mapped

Male cases
superimposed on plot
of prevalence by ED

Male cases
superimposed on
rectified Landsat
image with ED
boundaries

4: GMS card holders: Maynooth
•Data on GMS – Medical Card holders
drawn under strict data protection
protocols from DOHC
• Anonymised address lists data matched
with GeoDirectory
•Address matching rate of around 77% in
Maynooth

• Interesting range of results around
average for the whole ED

•ED – single value 14.6 % (national average around 30%)
•Small Area – Range from 2% to 70% - noticeable internal variation and
good indicator of elderly or poor populations

•Strategic value in comparing datasets
•Important in policy analysis

Mapping invites comparison

Role of GIS
• Data integration
– Tally of cases in small areas requires
data on locations of cases and small
area boundaries
– ‘spatial join’ places small area codes
on the case records
– Counting is a standard database
operation

• Data display

Finally
• GIS encourages you to think about
space
• In a ‘conventional’ map of Ireland
areas (EDs, Counties…) are shown in
proportion to their physical size
• There are alternative ways of
displaying maps…

In a cartogram the areas are drawn
proportional to their populations –
this is a “density-equalised” map
projection

A map of the boundaries of counties and
electoral divisions – the areas are drawn
proportional to their physical size

By comparison, the more deprived
western area of Dublin, northern area
of Cork and south east of Limerick are
clearly apparent in the cartogram.

Haase’s deprivation index suggests that the
most deprived populations are in peripheral
rural areas in the north west.

Redrawing the data on the cartogram
reveals wide variation in the SMR, and also
uncovers the urban dimension which is
hidden in the conventional display

Patient data matched to Electoral Divisions –
hospital admissions for psychiatric problems – the
patchwork effect does not suggest any pattern

The cartogram reveals the urban nature
of the problem – those areas which are
more deprived are also those with
higher levels of illness/disability

Residents aged 15+ who are unable to work
through illness or disability: the map pattern
suggests that might be a largely rural
phenomenon

The roads, rail, and urban areas data have
been transformed into the same space as
the cartogram. Dublin and Cork seem
poorly provided with roads…

Other spatial information can be transformed
to the space of the density equalised
projection. Here is the familiar map of main
roads and towns.

Using a cartogram we see that the
incidence of lung cancer is randomly
spatially distributed

Lung cancer in males in New York State [Gastner & Newman: 2004]

Plotting locations of lung cancer cases, we
obtain a map which follows the underlying
population distribution – there appear to be
“clusters”

What about Ireland?
• Potentially hampered by challenge of
geocoding patient records
• Lack of small area population data
– EDs have an average population of 1150
– Wide variation in size (50-28000)
– Socially inhomogenous

Rural addresses
• A major challenge is non-uniqueness
of addresses in rural areas.
• For instance in Moy ED, 11 properties
share the address
‘Lackamore, Lahinch, Co. Clare’

What’s needed
• Postcoding should provide a
mechanism for geocoding patient data
with the granularity required for
planning the delivery of health care –
this has been the case in the UK for a
for long time
• Census data should be made available
for areas with small and similar
populations (cf: UK Output Areas)

Finally…
• GIS has an important role to play
in health studies
• Health data analysis is a rapidly
developing field
• However, in Ireland we are
hampered by data deficiencies –
now is the time for change

Maynooth: image in Google Earth

Central Dublin: image in Google Earth

Taoiseach’s Office: image in Google Earth

Downing Street, London: image in Google Earth

Maynooth: OSi orthophotograph added locally uisng KML

Sellafield: image in Google Earth

NCU – On Line Atlas of Cancer Mortality

The Philippine Institute
for Development
Studies makes an online atlas of socioeconomic indicators
available – contains
some health measures

The future
• Patient empowerment…

• GIS used to merge information from
regulatory agencies on 19196 landfill sites
• Sites buffered to 2km and postcodes
within buffer identified
• Postcoded live births (8.2x106), stillbirths
(43471) and congenital anomalies
(124,597) between 1982 and 1997
• Poisson regression modelling undertaken
• Evidence found for excess risk of
congenital anomaly and low birthweight

• Distance from each
census ward to
nearest hospital
computed
• Map shows number
of patients waiting
longer than 6
months for elective
inpatient care per
available and
unoccupied bed
within 60 minutes
travel time


Slide 65

GIS and Health
Martin Charlton1
Ronan Foley2
Dennis Pringle2
1National

Centre for Geocomputation
2Department of Geography
NUI Maynooth

Outline
• Preliminary observations on GIS
and health
• Examples of GIS in Health:
research and practice
• Mapping people not places

Patterns in Health Data
• Three main areas:
– Mapping of disease incidence
• Relative risk estimation

– Identification of disease clustering
• Surveillance

– Ecological analysis
• Relationship between incidence and
covariates
(Lawson et al, 2003)

Health Data
• Typically patient data is available
in two forms
• Individual – perhaps anonymised
to postcode/zip code level
• Aggregate – counts are available
for a set of zones
• The nature of the data affects the
nature of the analysis

Geographic Information Systems
• GIS is often confused with mapping
• A range of activities which includes
inter alia mapping and analysis
• Will typically involve several datasets
• May also involve several different
pieces of software

Mapping: EpiINFO
• This is a rather simple analysis and
mapping program
• It can be downloaded from the internet
• It doesn’t provide much in the way of
spatial data manipulation…
• … nor are its analytical capabilities very
advanced
• It is easy to use

Spatial Data Manipulation
• GIS isn’t merely concerned with
data display or query
• It’s useful in bringing together
different spatial datasets in the
first stages of an analysis
• This may well require a range of
different geometrical and
database operations

Analysis with Spatial Data
• Statistical analysis usually involves
some separate software.
• Spatial data have some properties
which mean that SPSS isn’t always
appropriate.
• There is now a range of specialist
software – much of it available for
download

GIS may be ideal for data manipulation, display and query,
but it’s not always ideal for analysis – in epidemiology a
variety of analytical software is available … WinBUGS,
GeoBUGS (MRC Biostatistics Unit, Cambridge)

Software to implement
particular methods – for
example scan statistics - can be
downloaded quickly and easily
from the Internet. Kulldorf’s
SaTScan system implements a
range of different scan
statistical models

GIS in Health in Practice
• Facility location – dialysis units
• Facility location – obs & gyn
• Data integration – schizophrenia
in Cavan
• Data manipulation and display –
GMS card holders in Maynooth

1: Provision of Dialysis Units
• There are 5 dialysis units for renal
patients in a region with a population
of about 3,000,000
• Major population centre has two units
about 5 miles apart
• Where would you locate an extra one?
• What would happen if one was
closed?

Location-allocation
• Location-allocation problems deal
with optimum location of facilities
• Attempt to minimise total
weighted travel time/distance
from users to facilities
• What data is needed?

Data requirements
• Digitised road network with classification
of road types
• Mean travel speeds on each type for
urban/non-urban roads
• Extents of urban areas
• Anonymised postcoded patient records
• Postcode->grid reference lookup
• Locations of hospitals in region (both with
and without dialysis units)

GIS processing
• Data integration
• Determine which segments of road network
are urban
• Determine travel times using road type
urban/non-urban speeds
• Tally numbers of patients at each node in
the road network
• Find nearest nodes in network to each
hospital
• Code hospitals as ‘existing’ or ‘potential’
units

Modelling/Analysis
• Carried out using standard GIS
functions
• Convenient ‘front end’ written
using system’s macro language
(save a lot of typing)
• Explore a range of alternatives

Hospital with
dialysis unit

Study region showing hospital locations and urban areas

Patients requiring dialysis (tallied to road nodes)

Classified road network – includes roads outside study area

•

Some alternative
scenarios:

(a) Optimal allocation
of patients
(b) Provide 1 extra
unit
(c) Provide 2 extra
units

(d) Close 1 unit in
city and reallocate
its patients

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

integrate data
process data
model and analyse
generate alternative scenarios
display the results

2: Facility Location
• Two hospitals both provided facilities
for obstetrics, gynaecology, paediatrics
and a special care baby unit.
• Was there a case for rationalisation and
improving the facilities at one hospital
only?
• Would closure disadvantage any
patients?

Data
• Locations of hospitals in study region
• Anonymised postcoded finished
consultant episodes for each of the
four specialities at each hospital
• Classified road network
• Speeds associated with each road
type

Data preparation
• Postcoded records allocated grid
references using Central Postcode
Directory (POSTZON)
• Vector data converted to raster
• Patient/specialty/hospital counts
produced for each raster
• Passage times computed for each
raster cell based on road
classification and speed estimates

Roads, urban areas, patient locations, hospitals in study area

Processing
• Travel time surfaces computed for
each hospital
• Each raster cell contains time
required to reach hospital in minutes
• Assumed walking speed in ‘non road’
areas

Travel time surface for hospital S – darker = shorter time

Travel time surface for hospital T

Time differences
• If we subtract the travel time surfaces areas
nearer one hospital will have positive
values for the difference, and areas nearer
the other will have negative ones
• Boundary between positive and negative
values is boundary of catchment for each
hospital.
• If you’re on the boundary (difference is
zero), it doesn’t matter which hospital you
choose

Roads, patient locations, hospital catchments

Indifference
• If we identify those rasters where the time
difference (ignoring the sign) is, say, 5 –
people in those areas are in the 5 minute
‘indifference’ zone.
• Patients might not be worried about a 5
minute increase in their travel time, they
might be worried about 30 minutes
• Indifference zones can be computed quite
easily

15-minute indifference zone between the two hospitals

Mean access time
• For each cell in the raster we can
compute the product of the number
of patients and the time required to
reach each hospital
• Dividing this by the number of
patients gives a patient weighted
average time for each hospital

Average travel times for the various specialties. There
isn’t much to chose between specialities with the
exception of paediatric episodes.

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

gather data
integrate data
process data
model data
display results

• All carried out using raster
operations

3: Schizophrenia in Cavan
• In 1996 Pringle, Waddington and
Youssef reported on evidence for the
causes of schizophrenia
• Ecological analysis of variation among
EDs
– Cases varied from 0-11 per ED
– Populations varied from 250-2750

• Poisson probabilities suggested that
one area had an unusually high number
of cases – these can be mapped

Male cases
superimposed on plot
of prevalence by ED

Male cases
superimposed on
rectified Landsat
image with ED
boundaries

4: GMS card holders: Maynooth
•Data on GMS – Medical Card holders
drawn under strict data protection
protocols from DOHC
• Anonymised address lists data matched
with GeoDirectory
•Address matching rate of around 77% in
Maynooth

• Interesting range of results around
average for the whole ED

•ED – single value 14.6 % (national average around 30%)
•Small Area – Range from 2% to 70% - noticeable internal variation and
good indicator of elderly or poor populations

•Strategic value in comparing datasets
•Important in policy analysis

Mapping invites comparison

Role of GIS
• Data integration
– Tally of cases in small areas requires
data on locations of cases and small
area boundaries
– ‘spatial join’ places small area codes
on the case records
– Counting is a standard database
operation

• Data display

Finally
• GIS encourages you to think about
space
• In a ‘conventional’ map of Ireland
areas (EDs, Counties…) are shown in
proportion to their physical size
• There are alternative ways of
displaying maps…

In a cartogram the areas are drawn
proportional to their populations –
this is a “density-equalised” map
projection

A map of the boundaries of counties and
electoral divisions – the areas are drawn
proportional to their physical size

By comparison, the more deprived
western area of Dublin, northern area
of Cork and south east of Limerick are
clearly apparent in the cartogram.

Haase’s deprivation index suggests that the
most deprived populations are in peripheral
rural areas in the north west.

Redrawing the data on the cartogram
reveals wide variation in the SMR, and also
uncovers the urban dimension which is
hidden in the conventional display

Patient data matched to Electoral Divisions –
hospital admissions for psychiatric problems – the
patchwork effect does not suggest any pattern

The cartogram reveals the urban nature
of the problem – those areas which are
more deprived are also those with
higher levels of illness/disability

Residents aged 15+ who are unable to work
through illness or disability: the map pattern
suggests that might be a largely rural
phenomenon

The roads, rail, and urban areas data have
been transformed into the same space as
the cartogram. Dublin and Cork seem
poorly provided with roads…

Other spatial information can be transformed
to the space of the density equalised
projection. Here is the familiar map of main
roads and towns.

Using a cartogram we see that the
incidence of lung cancer is randomly
spatially distributed

Lung cancer in males in New York State [Gastner & Newman: 2004]

Plotting locations of lung cancer cases, we
obtain a map which follows the underlying
population distribution – there appear to be
“clusters”

What about Ireland?
• Potentially hampered by challenge of
geocoding patient records
• Lack of small area population data
– EDs have an average population of 1150
– Wide variation in size (50-28000)
– Socially inhomogenous

Rural addresses
• A major challenge is non-uniqueness
of addresses in rural areas.
• For instance in Moy ED, 11 properties
share the address
‘Lackamore, Lahinch, Co. Clare’

What’s needed
• Postcoding should provide a
mechanism for geocoding patient data
with the granularity required for
planning the delivery of health care –
this has been the case in the UK for a
for long time
• Census data should be made available
for areas with small and similar
populations (cf: UK Output Areas)

Finally…
• GIS has an important role to play
in health studies
• Health data analysis is a rapidly
developing field
• However, in Ireland we are
hampered by data deficiencies –
now is the time for change

Maynooth: image in Google Earth

Central Dublin: image in Google Earth

Taoiseach’s Office: image in Google Earth

Downing Street, London: image in Google Earth

Maynooth: OSi orthophotograph added locally uisng KML

Sellafield: image in Google Earth

NCU – On Line Atlas of Cancer Mortality

The Philippine Institute
for Development
Studies makes an online atlas of socioeconomic indicators
available – contains
some health measures

The future
• Patient empowerment…

• GIS used to merge information from
regulatory agencies on 19196 landfill sites
• Sites buffered to 2km and postcodes
within buffer identified
• Postcoded live births (8.2x106), stillbirths
(43471) and congenital anomalies
(124,597) between 1982 and 1997
• Poisson regression modelling undertaken
• Evidence found for excess risk of
congenital anomaly and low birthweight

• Distance from each
census ward to
nearest hospital
computed
• Map shows number
of patients waiting
longer than 6
months for elective
inpatient care per
available and
unoccupied bed
within 60 minutes
travel time


Slide 66

GIS and Health
Martin Charlton1
Ronan Foley2
Dennis Pringle2
1National

Centre for Geocomputation
2Department of Geography
NUI Maynooth

Outline
• Preliminary observations on GIS
and health
• Examples of GIS in Health:
research and practice
• Mapping people not places

Patterns in Health Data
• Three main areas:
– Mapping of disease incidence
• Relative risk estimation

– Identification of disease clustering
• Surveillance

– Ecological analysis
• Relationship between incidence and
covariates
(Lawson et al, 2003)

Health Data
• Typically patient data is available
in two forms
• Individual – perhaps anonymised
to postcode/zip code level
• Aggregate – counts are available
for a set of zones
• The nature of the data affects the
nature of the analysis

Geographic Information Systems
• GIS is often confused with mapping
• A range of activities which includes
inter alia mapping and analysis
• Will typically involve several datasets
• May also involve several different
pieces of software

Mapping: EpiINFO
• This is a rather simple analysis and
mapping program
• It can be downloaded from the internet
• It doesn’t provide much in the way of
spatial data manipulation…
• … nor are its analytical capabilities very
advanced
• It is easy to use

Spatial Data Manipulation
• GIS isn’t merely concerned with
data display or query
• It’s useful in bringing together
different spatial datasets in the
first stages of an analysis
• This may well require a range of
different geometrical and
database operations

Analysis with Spatial Data
• Statistical analysis usually involves
some separate software.
• Spatial data have some properties
which mean that SPSS isn’t always
appropriate.
• There is now a range of specialist
software – much of it available for
download

GIS may be ideal for data manipulation, display and query,
but it’s not always ideal for analysis – in epidemiology a
variety of analytical software is available … WinBUGS,
GeoBUGS (MRC Biostatistics Unit, Cambridge)

Software to implement
particular methods – for
example scan statistics - can be
downloaded quickly and easily
from the Internet. Kulldorf’s
SaTScan system implements a
range of different scan
statistical models

GIS in Health in Practice
• Facility location – dialysis units
• Facility location – obs & gyn
• Data integration – schizophrenia
in Cavan
• Data manipulation and display –
GMS card holders in Maynooth

1: Provision of Dialysis Units
• There are 5 dialysis units for renal
patients in a region with a population
of about 3,000,000
• Major population centre has two units
about 5 miles apart
• Where would you locate an extra one?
• What would happen if one was
closed?

Location-allocation
• Location-allocation problems deal
with optimum location of facilities
• Attempt to minimise total
weighted travel time/distance
from users to facilities
• What data is needed?

Data requirements
• Digitised road network with classification
of road types
• Mean travel speeds on each type for
urban/non-urban roads
• Extents of urban areas
• Anonymised postcoded patient records
• Postcode->grid reference lookup
• Locations of hospitals in region (both with
and without dialysis units)

GIS processing
• Data integration
• Determine which segments of road network
are urban
• Determine travel times using road type
urban/non-urban speeds
• Tally numbers of patients at each node in
the road network
• Find nearest nodes in network to each
hospital
• Code hospitals as ‘existing’ or ‘potential’
units

Modelling/Analysis
• Carried out using standard GIS
functions
• Convenient ‘front end’ written
using system’s macro language
(save a lot of typing)
• Explore a range of alternatives

Hospital with
dialysis unit

Study region showing hospital locations and urban areas

Patients requiring dialysis (tallied to road nodes)

Classified road network – includes roads outside study area

•

Some alternative
scenarios:

(a) Optimal allocation
of patients
(b) Provide 1 extra
unit
(c) Provide 2 extra
units

(d) Close 1 unit in
city and reallocate
its patients

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

integrate data
process data
model and analyse
generate alternative scenarios
display the results

2: Facility Location
• Two hospitals both provided facilities
for obstetrics, gynaecology, paediatrics
and a special care baby unit.
• Was there a case for rationalisation and
improving the facilities at one hospital
only?
• Would closure disadvantage any
patients?

Data
• Locations of hospitals in study region
• Anonymised postcoded finished
consultant episodes for each of the
four specialities at each hospital
• Classified road network
• Speeds associated with each road
type

Data preparation
• Postcoded records allocated grid
references using Central Postcode
Directory (POSTZON)
• Vector data converted to raster
• Patient/specialty/hospital counts
produced for each raster
• Passage times computed for each
raster cell based on road
classification and speed estimates

Roads, urban areas, patient locations, hospitals in study area

Processing
• Travel time surfaces computed for
each hospital
• Each raster cell contains time
required to reach hospital in minutes
• Assumed walking speed in ‘non road’
areas

Travel time surface for hospital S – darker = shorter time

Travel time surface for hospital T

Time differences
• If we subtract the travel time surfaces areas
nearer one hospital will have positive
values for the difference, and areas nearer
the other will have negative ones
• Boundary between positive and negative
values is boundary of catchment for each
hospital.
• If you’re on the boundary (difference is
zero), it doesn’t matter which hospital you
choose

Roads, patient locations, hospital catchments

Indifference
• If we identify those rasters where the time
difference (ignoring the sign) is, say, 5 –
people in those areas are in the 5 minute
‘indifference’ zone.
• Patients might not be worried about a 5
minute increase in their travel time, they
might be worried about 30 minutes
• Indifference zones can be computed quite
easily

15-minute indifference zone between the two hospitals

Mean access time
• For each cell in the raster we can
compute the product of the number
of patients and the time required to
reach each hospital
• Dividing this by the number of
patients gives a patient weighted
average time for each hospital

Average travel times for the various specialties. There
isn’t much to chose between specialities with the
exception of paediatric episodes.

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

gather data
integrate data
process data
model data
display results

• All carried out using raster
operations

3: Schizophrenia in Cavan
• In 1996 Pringle, Waddington and
Youssef reported on evidence for the
causes of schizophrenia
• Ecological analysis of variation among
EDs
– Cases varied from 0-11 per ED
– Populations varied from 250-2750

• Poisson probabilities suggested that
one area had an unusually high number
of cases – these can be mapped

Male cases
superimposed on plot
of prevalence by ED

Male cases
superimposed on
rectified Landsat
image with ED
boundaries

4: GMS card holders: Maynooth
•Data on GMS – Medical Card holders
drawn under strict data protection
protocols from DOHC
• Anonymised address lists data matched
with GeoDirectory
•Address matching rate of around 77% in
Maynooth

• Interesting range of results around
average for the whole ED

•ED – single value 14.6 % (national average around 30%)
•Small Area – Range from 2% to 70% - noticeable internal variation and
good indicator of elderly or poor populations

•Strategic value in comparing datasets
•Important in policy analysis

Mapping invites comparison

Role of GIS
• Data integration
– Tally of cases in small areas requires
data on locations of cases and small
area boundaries
– ‘spatial join’ places small area codes
on the case records
– Counting is a standard database
operation

• Data display

Finally
• GIS encourages you to think about
space
• In a ‘conventional’ map of Ireland
areas (EDs, Counties…) are shown in
proportion to their physical size
• There are alternative ways of
displaying maps…

In a cartogram the areas are drawn
proportional to their populations –
this is a “density-equalised” map
projection

A map of the boundaries of counties and
electoral divisions – the areas are drawn
proportional to their physical size

By comparison, the more deprived
western area of Dublin, northern area
of Cork and south east of Limerick are
clearly apparent in the cartogram.

Haase’s deprivation index suggests that the
most deprived populations are in peripheral
rural areas in the north west.

Redrawing the data on the cartogram
reveals wide variation in the SMR, and also
uncovers the urban dimension which is
hidden in the conventional display

Patient data matched to Electoral Divisions –
hospital admissions for psychiatric problems – the
patchwork effect does not suggest any pattern

The cartogram reveals the urban nature
of the problem – those areas which are
more deprived are also those with
higher levels of illness/disability

Residents aged 15+ who are unable to work
through illness or disability: the map pattern
suggests that might be a largely rural
phenomenon

The roads, rail, and urban areas data have
been transformed into the same space as
the cartogram. Dublin and Cork seem
poorly provided with roads…

Other spatial information can be transformed
to the space of the density equalised
projection. Here is the familiar map of main
roads and towns.

Using a cartogram we see that the
incidence of lung cancer is randomly
spatially distributed

Lung cancer in males in New York State [Gastner & Newman: 2004]

Plotting locations of lung cancer cases, we
obtain a map which follows the underlying
population distribution – there appear to be
“clusters”

What about Ireland?
• Potentially hampered by challenge of
geocoding patient records
• Lack of small area population data
– EDs have an average population of 1150
– Wide variation in size (50-28000)
– Socially inhomogenous

Rural addresses
• A major challenge is non-uniqueness
of addresses in rural areas.
• For instance in Moy ED, 11 properties
share the address
‘Lackamore, Lahinch, Co. Clare’

What’s needed
• Postcoding should provide a
mechanism for geocoding patient data
with the granularity required for
planning the delivery of health care –
this has been the case in the UK for a
for long time
• Census data should be made available
for areas with small and similar
populations (cf: UK Output Areas)

Finally…
• GIS has an important role to play
in health studies
• Health data analysis is a rapidly
developing field
• However, in Ireland we are
hampered by data deficiencies –
now is the time for change

Maynooth: image in Google Earth

Central Dublin: image in Google Earth

Taoiseach’s Office: image in Google Earth

Downing Street, London: image in Google Earth

Maynooth: OSi orthophotograph added locally uisng KML

Sellafield: image in Google Earth

NCU – On Line Atlas of Cancer Mortality

The Philippine Institute
for Development
Studies makes an online atlas of socioeconomic indicators
available – contains
some health measures

The future
• Patient empowerment…

• GIS used to merge information from
regulatory agencies on 19196 landfill sites
• Sites buffered to 2km and postcodes
within buffer identified
• Postcoded live births (8.2x106), stillbirths
(43471) and congenital anomalies
(124,597) between 1982 and 1997
• Poisson regression modelling undertaken
• Evidence found for excess risk of
congenital anomaly and low birthweight

• Distance from each
census ward to
nearest hospital
computed
• Map shows number
of patients waiting
longer than 6
months for elective
inpatient care per
available and
unoccupied bed
within 60 minutes
travel time


Slide 67

GIS and Health
Martin Charlton1
Ronan Foley2
Dennis Pringle2
1National

Centre for Geocomputation
2Department of Geography
NUI Maynooth

Outline
• Preliminary observations on GIS
and health
• Examples of GIS in Health:
research and practice
• Mapping people not places

Patterns in Health Data
• Three main areas:
– Mapping of disease incidence
• Relative risk estimation

– Identification of disease clustering
• Surveillance

– Ecological analysis
• Relationship between incidence and
covariates
(Lawson et al, 2003)

Health Data
• Typically patient data is available
in two forms
• Individual – perhaps anonymised
to postcode/zip code level
• Aggregate – counts are available
for a set of zones
• The nature of the data affects the
nature of the analysis

Geographic Information Systems
• GIS is often confused with mapping
• A range of activities which includes
inter alia mapping and analysis
• Will typically involve several datasets
• May also involve several different
pieces of software

Mapping: EpiINFO
• This is a rather simple analysis and
mapping program
• It can be downloaded from the internet
• It doesn’t provide much in the way of
spatial data manipulation…
• … nor are its analytical capabilities very
advanced
• It is easy to use

Spatial Data Manipulation
• GIS isn’t merely concerned with
data display or query
• It’s useful in bringing together
different spatial datasets in the
first stages of an analysis
• This may well require a range of
different geometrical and
database operations

Analysis with Spatial Data
• Statistical analysis usually involves
some separate software.
• Spatial data have some properties
which mean that SPSS isn’t always
appropriate.
• There is now a range of specialist
software – much of it available for
download

GIS may be ideal for data manipulation, display and query,
but it’s not always ideal for analysis – in epidemiology a
variety of analytical software is available … WinBUGS,
GeoBUGS (MRC Biostatistics Unit, Cambridge)

Software to implement
particular methods – for
example scan statistics - can be
downloaded quickly and easily
from the Internet. Kulldorf’s
SaTScan system implements a
range of different scan
statistical models

GIS in Health in Practice
• Facility location – dialysis units
• Facility location – obs & gyn
• Data integration – schizophrenia
in Cavan
• Data manipulation and display –
GMS card holders in Maynooth

1: Provision of Dialysis Units
• There are 5 dialysis units for renal
patients in a region with a population
of about 3,000,000
• Major population centre has two units
about 5 miles apart
• Where would you locate an extra one?
• What would happen if one was
closed?

Location-allocation
• Location-allocation problems deal
with optimum location of facilities
• Attempt to minimise total
weighted travel time/distance
from users to facilities
• What data is needed?

Data requirements
• Digitised road network with classification
of road types
• Mean travel speeds on each type for
urban/non-urban roads
• Extents of urban areas
• Anonymised postcoded patient records
• Postcode->grid reference lookup
• Locations of hospitals in region (both with
and without dialysis units)

GIS processing
• Data integration
• Determine which segments of road network
are urban
• Determine travel times using road type
urban/non-urban speeds
• Tally numbers of patients at each node in
the road network
• Find nearest nodes in network to each
hospital
• Code hospitals as ‘existing’ or ‘potential’
units

Modelling/Analysis
• Carried out using standard GIS
functions
• Convenient ‘front end’ written
using system’s macro language
(save a lot of typing)
• Explore a range of alternatives

Hospital with
dialysis unit

Study region showing hospital locations and urban areas

Patients requiring dialysis (tallied to road nodes)

Classified road network – includes roads outside study area

•

Some alternative
scenarios:

(a) Optimal allocation
of patients
(b) Provide 1 extra
unit
(c) Provide 2 extra
units

(d) Close 1 unit in
city and reallocate
its patients

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

integrate data
process data
model and analyse
generate alternative scenarios
display the results

2: Facility Location
• Two hospitals both provided facilities
for obstetrics, gynaecology, paediatrics
and a special care baby unit.
• Was there a case for rationalisation and
improving the facilities at one hospital
only?
• Would closure disadvantage any
patients?

Data
• Locations of hospitals in study region
• Anonymised postcoded finished
consultant episodes for each of the
four specialities at each hospital
• Classified road network
• Speeds associated with each road
type

Data preparation
• Postcoded records allocated grid
references using Central Postcode
Directory (POSTZON)
• Vector data converted to raster
• Patient/specialty/hospital counts
produced for each raster
• Passage times computed for each
raster cell based on road
classification and speed estimates

Roads, urban areas, patient locations, hospitals in study area

Processing
• Travel time surfaces computed for
each hospital
• Each raster cell contains time
required to reach hospital in minutes
• Assumed walking speed in ‘non road’
areas

Travel time surface for hospital S – darker = shorter time

Travel time surface for hospital T

Time differences
• If we subtract the travel time surfaces areas
nearer one hospital will have positive
values for the difference, and areas nearer
the other will have negative ones
• Boundary between positive and negative
values is boundary of catchment for each
hospital.
• If you’re on the boundary (difference is
zero), it doesn’t matter which hospital you
choose

Roads, patient locations, hospital catchments

Indifference
• If we identify those rasters where the time
difference (ignoring the sign) is, say, 5 –
people in those areas are in the 5 minute
‘indifference’ zone.
• Patients might not be worried about a 5
minute increase in their travel time, they
might be worried about 30 minutes
• Indifference zones can be computed quite
easily

15-minute indifference zone between the two hospitals

Mean access time
• For each cell in the raster we can
compute the product of the number
of patients and the time required to
reach each hospital
• Dividing this by the number of
patients gives a patient weighted
average time for each hospital

Average travel times for the various specialties. There
isn’t much to chose between specialities with the
exception of paediatric episodes.

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

gather data
integrate data
process data
model data
display results

• All carried out using raster
operations

3: Schizophrenia in Cavan
• In 1996 Pringle, Waddington and
Youssef reported on evidence for the
causes of schizophrenia
• Ecological analysis of variation among
EDs
– Cases varied from 0-11 per ED
– Populations varied from 250-2750

• Poisson probabilities suggested that
one area had an unusually high number
of cases – these can be mapped

Male cases
superimposed on plot
of prevalence by ED

Male cases
superimposed on
rectified Landsat
image with ED
boundaries

4: GMS card holders: Maynooth
•Data on GMS – Medical Card holders
drawn under strict data protection
protocols from DOHC
• Anonymised address lists data matched
with GeoDirectory
•Address matching rate of around 77% in
Maynooth

• Interesting range of results around
average for the whole ED

•ED – single value 14.6 % (national average around 30%)
•Small Area – Range from 2% to 70% - noticeable internal variation and
good indicator of elderly or poor populations

•Strategic value in comparing datasets
•Important in policy analysis

Mapping invites comparison

Role of GIS
• Data integration
– Tally of cases in small areas requires
data on locations of cases and small
area boundaries
– ‘spatial join’ places small area codes
on the case records
– Counting is a standard database
operation

• Data display

Finally
• GIS encourages you to think about
space
• In a ‘conventional’ map of Ireland
areas (EDs, Counties…) are shown in
proportion to their physical size
• There are alternative ways of
displaying maps…

In a cartogram the areas are drawn
proportional to their populations –
this is a “density-equalised” map
projection

A map of the boundaries of counties and
electoral divisions – the areas are drawn
proportional to their physical size

By comparison, the more deprived
western area of Dublin, northern area
of Cork and south east of Limerick are
clearly apparent in the cartogram.

Haase’s deprivation index suggests that the
most deprived populations are in peripheral
rural areas in the north west.

Redrawing the data on the cartogram
reveals wide variation in the SMR, and also
uncovers the urban dimension which is
hidden in the conventional display

Patient data matched to Electoral Divisions –
hospital admissions for psychiatric problems – the
patchwork effect does not suggest any pattern

The cartogram reveals the urban nature
of the problem – those areas which are
more deprived are also those with
higher levels of illness/disability

Residents aged 15+ who are unable to work
through illness or disability: the map pattern
suggests that might be a largely rural
phenomenon

The roads, rail, and urban areas data have
been transformed into the same space as
the cartogram. Dublin and Cork seem
poorly provided with roads…

Other spatial information can be transformed
to the space of the density equalised
projection. Here is the familiar map of main
roads and towns.

Using a cartogram we see that the
incidence of lung cancer is randomly
spatially distributed

Lung cancer in males in New York State [Gastner & Newman: 2004]

Plotting locations of lung cancer cases, we
obtain a map which follows the underlying
population distribution – there appear to be
“clusters”

What about Ireland?
• Potentially hampered by challenge of
geocoding patient records
• Lack of small area population data
– EDs have an average population of 1150
– Wide variation in size (50-28000)
– Socially inhomogenous

Rural addresses
• A major challenge is non-uniqueness
of addresses in rural areas.
• For instance in Moy ED, 11 properties
share the address
‘Lackamore, Lahinch, Co. Clare’

What’s needed
• Postcoding should provide a
mechanism for geocoding patient data
with the granularity required for
planning the delivery of health care –
this has been the case in the UK for a
for long time
• Census data should be made available
for areas with small and similar
populations (cf: UK Output Areas)

Finally…
• GIS has an important role to play
in health studies
• Health data analysis is a rapidly
developing field
• However, in Ireland we are
hampered by data deficiencies –
now is the time for change

Maynooth: image in Google Earth

Central Dublin: image in Google Earth

Taoiseach’s Office: image in Google Earth

Downing Street, London: image in Google Earth

Maynooth: OSi orthophotograph added locally uisng KML

Sellafield: image in Google Earth

NCU – On Line Atlas of Cancer Mortality

The Philippine Institute
for Development
Studies makes an online atlas of socioeconomic indicators
available – contains
some health measures

The future
• Patient empowerment…

• GIS used to merge information from
regulatory agencies on 19196 landfill sites
• Sites buffered to 2km and postcodes
within buffer identified
• Postcoded live births (8.2x106), stillbirths
(43471) and congenital anomalies
(124,597) between 1982 and 1997
• Poisson regression modelling undertaken
• Evidence found for excess risk of
congenital anomaly and low birthweight

• Distance from each
census ward to
nearest hospital
computed
• Map shows number
of patients waiting
longer than 6
months for elective
inpatient care per
available and
unoccupied bed
within 60 minutes
travel time


Slide 68

GIS and Health
Martin Charlton1
Ronan Foley2
Dennis Pringle2
1National

Centre for Geocomputation
2Department of Geography
NUI Maynooth

Outline
• Preliminary observations on GIS
and health
• Examples of GIS in Health:
research and practice
• Mapping people not places

Patterns in Health Data
• Three main areas:
– Mapping of disease incidence
• Relative risk estimation

– Identification of disease clustering
• Surveillance

– Ecological analysis
• Relationship between incidence and
covariates
(Lawson et al, 2003)

Health Data
• Typically patient data is available
in two forms
• Individual – perhaps anonymised
to postcode/zip code level
• Aggregate – counts are available
for a set of zones
• The nature of the data affects the
nature of the analysis

Geographic Information Systems
• GIS is often confused with mapping
• A range of activities which includes
inter alia mapping and analysis
• Will typically involve several datasets
• May also involve several different
pieces of software

Mapping: EpiINFO
• This is a rather simple analysis and
mapping program
• It can be downloaded from the internet
• It doesn’t provide much in the way of
spatial data manipulation…
• … nor are its analytical capabilities very
advanced
• It is easy to use

Spatial Data Manipulation
• GIS isn’t merely concerned with
data display or query
• It’s useful in bringing together
different spatial datasets in the
first stages of an analysis
• This may well require a range of
different geometrical and
database operations

Analysis with Spatial Data
• Statistical analysis usually involves
some separate software.
• Spatial data have some properties
which mean that SPSS isn’t always
appropriate.
• There is now a range of specialist
software – much of it available for
download

GIS may be ideal for data manipulation, display and query,
but it’s not always ideal for analysis – in epidemiology a
variety of analytical software is available … WinBUGS,
GeoBUGS (MRC Biostatistics Unit, Cambridge)

Software to implement
particular methods – for
example scan statistics - can be
downloaded quickly and easily
from the Internet. Kulldorf’s
SaTScan system implements a
range of different scan
statistical models

GIS in Health in Practice
• Facility location – dialysis units
• Facility location – obs & gyn
• Data integration – schizophrenia
in Cavan
• Data manipulation and display –
GMS card holders in Maynooth

1: Provision of Dialysis Units
• There are 5 dialysis units for renal
patients in a region with a population
of about 3,000,000
• Major population centre has two units
about 5 miles apart
• Where would you locate an extra one?
• What would happen if one was
closed?

Location-allocation
• Location-allocation problems deal
with optimum location of facilities
• Attempt to minimise total
weighted travel time/distance
from users to facilities
• What data is needed?

Data requirements
• Digitised road network with classification
of road types
• Mean travel speeds on each type for
urban/non-urban roads
• Extents of urban areas
• Anonymised postcoded patient records
• Postcode->grid reference lookup
• Locations of hospitals in region (both with
and without dialysis units)

GIS processing
• Data integration
• Determine which segments of road network
are urban
• Determine travel times using road type
urban/non-urban speeds
• Tally numbers of patients at each node in
the road network
• Find nearest nodes in network to each
hospital
• Code hospitals as ‘existing’ or ‘potential’
units

Modelling/Analysis
• Carried out using standard GIS
functions
• Convenient ‘front end’ written
using system’s macro language
(save a lot of typing)
• Explore a range of alternatives

Hospital with
dialysis unit

Study region showing hospital locations and urban areas

Patients requiring dialysis (tallied to road nodes)

Classified road network – includes roads outside study area

•

Some alternative
scenarios:

(a) Optimal allocation
of patients
(b) Provide 1 extra
unit
(c) Provide 2 extra
units

(d) Close 1 unit in
city and reallocate
its patients

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

integrate data
process data
model and analyse
generate alternative scenarios
display the results

2: Facility Location
• Two hospitals both provided facilities
for obstetrics, gynaecology, paediatrics
and a special care baby unit.
• Was there a case for rationalisation and
improving the facilities at one hospital
only?
• Would closure disadvantage any
patients?

Data
• Locations of hospitals in study region
• Anonymised postcoded finished
consultant episodes for each of the
four specialities at each hospital
• Classified road network
• Speeds associated with each road
type

Data preparation
• Postcoded records allocated grid
references using Central Postcode
Directory (POSTZON)
• Vector data converted to raster
• Patient/specialty/hospital counts
produced for each raster
• Passage times computed for each
raster cell based on road
classification and speed estimates

Roads, urban areas, patient locations, hospitals in study area

Processing
• Travel time surfaces computed for
each hospital
• Each raster cell contains time
required to reach hospital in minutes
• Assumed walking speed in ‘non road’
areas

Travel time surface for hospital S – darker = shorter time

Travel time surface for hospital T

Time differences
• If we subtract the travel time surfaces areas
nearer one hospital will have positive
values for the difference, and areas nearer
the other will have negative ones
• Boundary between positive and negative
values is boundary of catchment for each
hospital.
• If you’re on the boundary (difference is
zero), it doesn’t matter which hospital you
choose

Roads, patient locations, hospital catchments

Indifference
• If we identify those rasters where the time
difference (ignoring the sign) is, say, 5 –
people in those areas are in the 5 minute
‘indifference’ zone.
• Patients might not be worried about a 5
minute increase in their travel time, they
might be worried about 30 minutes
• Indifference zones can be computed quite
easily

15-minute indifference zone between the two hospitals

Mean access time
• For each cell in the raster we can
compute the product of the number
of patients and the time required to
reach each hospital
• Dividing this by the number of
patients gives a patient weighted
average time for each hospital

Average travel times for the various specialties. There
isn’t much to chose between specialities with the
exception of paediatric episodes.

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

gather data
integrate data
process data
model data
display results

• All carried out using raster
operations

3: Schizophrenia in Cavan
• In 1996 Pringle, Waddington and
Youssef reported on evidence for the
causes of schizophrenia
• Ecological analysis of variation among
EDs
– Cases varied from 0-11 per ED
– Populations varied from 250-2750

• Poisson probabilities suggested that
one area had an unusually high number
of cases – these can be mapped

Male cases
superimposed on plot
of prevalence by ED

Male cases
superimposed on
rectified Landsat
image with ED
boundaries

4: GMS card holders: Maynooth
•Data on GMS – Medical Card holders
drawn under strict data protection
protocols from DOHC
• Anonymised address lists data matched
with GeoDirectory
•Address matching rate of around 77% in
Maynooth

• Interesting range of results around
average for the whole ED

•ED – single value 14.6 % (national average around 30%)
•Small Area – Range from 2% to 70% - noticeable internal variation and
good indicator of elderly or poor populations

•Strategic value in comparing datasets
•Important in policy analysis

Mapping invites comparison

Role of GIS
• Data integration
– Tally of cases in small areas requires
data on locations of cases and small
area boundaries
– ‘spatial join’ places small area codes
on the case records
– Counting is a standard database
operation

• Data display

Finally
• GIS encourages you to think about
space
• In a ‘conventional’ map of Ireland
areas (EDs, Counties…) are shown in
proportion to their physical size
• There are alternative ways of
displaying maps…

In a cartogram the areas are drawn
proportional to their populations –
this is a “density-equalised” map
projection

A map of the boundaries of counties and
electoral divisions – the areas are drawn
proportional to their physical size

By comparison, the more deprived
western area of Dublin, northern area
of Cork and south east of Limerick are
clearly apparent in the cartogram.

Haase’s deprivation index suggests that the
most deprived populations are in peripheral
rural areas in the north west.

Redrawing the data on the cartogram
reveals wide variation in the SMR, and also
uncovers the urban dimension which is
hidden in the conventional display

Patient data matched to Electoral Divisions –
hospital admissions for psychiatric problems – the
patchwork effect does not suggest any pattern

The cartogram reveals the urban nature
of the problem – those areas which are
more deprived are also those with
higher levels of illness/disability

Residents aged 15+ who are unable to work
through illness or disability: the map pattern
suggests that might be a largely rural
phenomenon

The roads, rail, and urban areas data have
been transformed into the same space as
the cartogram. Dublin and Cork seem
poorly provided with roads…

Other spatial information can be transformed
to the space of the density equalised
projection. Here is the familiar map of main
roads and towns.

Using a cartogram we see that the
incidence of lung cancer is randomly
spatially distributed

Lung cancer in males in New York State [Gastner & Newman: 2004]

Plotting locations of lung cancer cases, we
obtain a map which follows the underlying
population distribution – there appear to be
“clusters”

What about Ireland?
• Potentially hampered by challenge of
geocoding patient records
• Lack of small area population data
– EDs have an average population of 1150
– Wide variation in size (50-28000)
– Socially inhomogenous

Rural addresses
• A major challenge is non-uniqueness
of addresses in rural areas.
• For instance in Moy ED, 11 properties
share the address
‘Lackamore, Lahinch, Co. Clare’

What’s needed
• Postcoding should provide a
mechanism for geocoding patient data
with the granularity required for
planning the delivery of health care –
this has been the case in the UK for a
for long time
• Census data should be made available
for areas with small and similar
populations (cf: UK Output Areas)

Finally…
• GIS has an important role to play
in health studies
• Health data analysis is a rapidly
developing field
• However, in Ireland we are
hampered by data deficiencies –
now is the time for change

Maynooth: image in Google Earth

Central Dublin: image in Google Earth

Taoiseach’s Office: image in Google Earth

Downing Street, London: image in Google Earth

Maynooth: OSi orthophotograph added locally uisng KML

Sellafield: image in Google Earth

NCU – On Line Atlas of Cancer Mortality

The Philippine Institute
for Development
Studies makes an online atlas of socioeconomic indicators
available – contains
some health measures

The future
• Patient empowerment…

• GIS used to merge information from
regulatory agencies on 19196 landfill sites
• Sites buffered to 2km and postcodes
within buffer identified
• Postcoded live births (8.2x106), stillbirths
(43471) and congenital anomalies
(124,597) between 1982 and 1997
• Poisson regression modelling undertaken
• Evidence found for excess risk of
congenital anomaly and low birthweight

• Distance from each
census ward to
nearest hospital
computed
• Map shows number
of patients waiting
longer than 6
months for elective
inpatient care per
available and
unoccupied bed
within 60 minutes
travel time


Slide 69

GIS and Health
Martin Charlton1
Ronan Foley2
Dennis Pringle2
1National

Centre for Geocomputation
2Department of Geography
NUI Maynooth

Outline
• Preliminary observations on GIS
and health
• Examples of GIS in Health:
research and practice
• Mapping people not places

Patterns in Health Data
• Three main areas:
– Mapping of disease incidence
• Relative risk estimation

– Identification of disease clustering
• Surveillance

– Ecological analysis
• Relationship between incidence and
covariates
(Lawson et al, 2003)

Health Data
• Typically patient data is available
in two forms
• Individual – perhaps anonymised
to postcode/zip code level
• Aggregate – counts are available
for a set of zones
• The nature of the data affects the
nature of the analysis

Geographic Information Systems
• GIS is often confused with mapping
• A range of activities which includes
inter alia mapping and analysis
• Will typically involve several datasets
• May also involve several different
pieces of software

Mapping: EpiINFO
• This is a rather simple analysis and
mapping program
• It can be downloaded from the internet
• It doesn’t provide much in the way of
spatial data manipulation…
• … nor are its analytical capabilities very
advanced
• It is easy to use

Spatial Data Manipulation
• GIS isn’t merely concerned with
data display or query
• It’s useful in bringing together
different spatial datasets in the
first stages of an analysis
• This may well require a range of
different geometrical and
database operations

Analysis with Spatial Data
• Statistical analysis usually involves
some separate software.
• Spatial data have some properties
which mean that SPSS isn’t always
appropriate.
• There is now a range of specialist
software – much of it available for
download

GIS may be ideal for data manipulation, display and query,
but it’s not always ideal for analysis – in epidemiology a
variety of analytical software is available … WinBUGS,
GeoBUGS (MRC Biostatistics Unit, Cambridge)

Software to implement
particular methods – for
example scan statistics - can be
downloaded quickly and easily
from the Internet. Kulldorf’s
SaTScan system implements a
range of different scan
statistical models

GIS in Health in Practice
• Facility location – dialysis units
• Facility location – obs & gyn
• Data integration – schizophrenia
in Cavan
• Data manipulation and display –
GMS card holders in Maynooth

1: Provision of Dialysis Units
• There are 5 dialysis units for renal
patients in a region with a population
of about 3,000,000
• Major population centre has two units
about 5 miles apart
• Where would you locate an extra one?
• What would happen if one was
closed?

Location-allocation
• Location-allocation problems deal
with optimum location of facilities
• Attempt to minimise total
weighted travel time/distance
from users to facilities
• What data is needed?

Data requirements
• Digitised road network with classification
of road types
• Mean travel speeds on each type for
urban/non-urban roads
• Extents of urban areas
• Anonymised postcoded patient records
• Postcode->grid reference lookup
• Locations of hospitals in region (both with
and without dialysis units)

GIS processing
• Data integration
• Determine which segments of road network
are urban
• Determine travel times using road type
urban/non-urban speeds
• Tally numbers of patients at each node in
the road network
• Find nearest nodes in network to each
hospital
• Code hospitals as ‘existing’ or ‘potential’
units

Modelling/Analysis
• Carried out using standard GIS
functions
• Convenient ‘front end’ written
using system’s macro language
(save a lot of typing)
• Explore a range of alternatives

Hospital with
dialysis unit

Study region showing hospital locations and urban areas

Patients requiring dialysis (tallied to road nodes)

Classified road network – includes roads outside study area

•

Some alternative
scenarios:

(a) Optimal allocation
of patients
(b) Provide 1 extra
unit
(c) Provide 2 extra
units

(d) Close 1 unit in
city and reallocate
its patients

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

integrate data
process data
model and analyse
generate alternative scenarios
display the results

2: Facility Location
• Two hospitals both provided facilities
for obstetrics, gynaecology, paediatrics
and a special care baby unit.
• Was there a case for rationalisation and
improving the facilities at one hospital
only?
• Would closure disadvantage any
patients?

Data
• Locations of hospitals in study region
• Anonymised postcoded finished
consultant episodes for each of the
four specialities at each hospital
• Classified road network
• Speeds associated with each road
type

Data preparation
• Postcoded records allocated grid
references using Central Postcode
Directory (POSTZON)
• Vector data converted to raster
• Patient/specialty/hospital counts
produced for each raster
• Passage times computed for each
raster cell based on road
classification and speed estimates

Roads, urban areas, patient locations, hospitals in study area

Processing
• Travel time surfaces computed for
each hospital
• Each raster cell contains time
required to reach hospital in minutes
• Assumed walking speed in ‘non road’
areas

Travel time surface for hospital S – darker = shorter time

Travel time surface for hospital T

Time differences
• If we subtract the travel time surfaces areas
nearer one hospital will have positive
values for the difference, and areas nearer
the other will have negative ones
• Boundary between positive and negative
values is boundary of catchment for each
hospital.
• If you’re on the boundary (difference is
zero), it doesn’t matter which hospital you
choose

Roads, patient locations, hospital catchments

Indifference
• If we identify those rasters where the time
difference (ignoring the sign) is, say, 5 –
people in those areas are in the 5 minute
‘indifference’ zone.
• Patients might not be worried about a 5
minute increase in their travel time, they
might be worried about 30 minutes
• Indifference zones can be computed quite
easily

15-minute indifference zone between the two hospitals

Mean access time
• For each cell in the raster we can
compute the product of the number
of patients and the time required to
reach each hospital
• Dividing this by the number of
patients gives a patient weighted
average time for each hospital

Average travel times for the various specialties. There
isn’t much to chose between specialities with the
exception of paediatric episodes.

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

gather data
integrate data
process data
model data
display results

• All carried out using raster
operations

3: Schizophrenia in Cavan
• In 1996 Pringle, Waddington and
Youssef reported on evidence for the
causes of schizophrenia
• Ecological analysis of variation among
EDs
– Cases varied from 0-11 per ED
– Populations varied from 250-2750

• Poisson probabilities suggested that
one area had an unusually high number
of cases – these can be mapped

Male cases
superimposed on plot
of prevalence by ED

Male cases
superimposed on
rectified Landsat
image with ED
boundaries

4: GMS card holders: Maynooth
•Data on GMS – Medical Card holders
drawn under strict data protection
protocols from DOHC
• Anonymised address lists data matched
with GeoDirectory
•Address matching rate of around 77% in
Maynooth

• Interesting range of results around
average for the whole ED

•ED – single value 14.6 % (national average around 30%)
•Small Area – Range from 2% to 70% - noticeable internal variation and
good indicator of elderly or poor populations

•Strategic value in comparing datasets
•Important in policy analysis

Mapping invites comparison

Role of GIS
• Data integration
– Tally of cases in small areas requires
data on locations of cases and small
area boundaries
– ‘spatial join’ places small area codes
on the case records
– Counting is a standard database
operation

• Data display

Finally
• GIS encourages you to think about
space
• In a ‘conventional’ map of Ireland
areas (EDs, Counties…) are shown in
proportion to their physical size
• There are alternative ways of
displaying maps…

In a cartogram the areas are drawn
proportional to their populations –
this is a “density-equalised” map
projection

A map of the boundaries of counties and
electoral divisions – the areas are drawn
proportional to their physical size

By comparison, the more deprived
western area of Dublin, northern area
of Cork and south east of Limerick are
clearly apparent in the cartogram.

Haase’s deprivation index suggests that the
most deprived populations are in peripheral
rural areas in the north west.

Redrawing the data on the cartogram
reveals wide variation in the SMR, and also
uncovers the urban dimension which is
hidden in the conventional display

Patient data matched to Electoral Divisions –
hospital admissions for psychiatric problems – the
patchwork effect does not suggest any pattern

The cartogram reveals the urban nature
of the problem – those areas which are
more deprived are also those with
higher levels of illness/disability

Residents aged 15+ who are unable to work
through illness or disability: the map pattern
suggests that might be a largely rural
phenomenon

The roads, rail, and urban areas data have
been transformed into the same space as
the cartogram. Dublin and Cork seem
poorly provided with roads…

Other spatial information can be transformed
to the space of the density equalised
projection. Here is the familiar map of main
roads and towns.

Using a cartogram we see that the
incidence of lung cancer is randomly
spatially distributed

Lung cancer in males in New York State [Gastner & Newman: 2004]

Plotting locations of lung cancer cases, we
obtain a map which follows the underlying
population distribution – there appear to be
“clusters”

What about Ireland?
• Potentially hampered by challenge of
geocoding patient records
• Lack of small area population data
– EDs have an average population of 1150
– Wide variation in size (50-28000)
– Socially inhomogenous

Rural addresses
• A major challenge is non-uniqueness
of addresses in rural areas.
• For instance in Moy ED, 11 properties
share the address
‘Lackamore, Lahinch, Co. Clare’

What’s needed
• Postcoding should provide a
mechanism for geocoding patient data
with the granularity required for
planning the delivery of health care –
this has been the case in the UK for a
for long time
• Census data should be made available
for areas with small and similar
populations (cf: UK Output Areas)

Finally…
• GIS has an important role to play
in health studies
• Health data analysis is a rapidly
developing field
• However, in Ireland we are
hampered by data deficiencies –
now is the time for change

Maynooth: image in Google Earth

Central Dublin: image in Google Earth

Taoiseach’s Office: image in Google Earth

Downing Street, London: image in Google Earth

Maynooth: OSi orthophotograph added locally uisng KML

Sellafield: image in Google Earth

NCU – On Line Atlas of Cancer Mortality

The Philippine Institute
for Development
Studies makes an online atlas of socioeconomic indicators
available – contains
some health measures

The future
• Patient empowerment…

• GIS used to merge information from
regulatory agencies on 19196 landfill sites
• Sites buffered to 2km and postcodes
within buffer identified
• Postcoded live births (8.2x106), stillbirths
(43471) and congenital anomalies
(124,597) between 1982 and 1997
• Poisson regression modelling undertaken
• Evidence found for excess risk of
congenital anomaly and low birthweight

• Distance from each
census ward to
nearest hospital
computed
• Map shows number
of patients waiting
longer than 6
months for elective
inpatient care per
available and
unoccupied bed
within 60 minutes
travel time


Slide 70

GIS and Health
Martin Charlton1
Ronan Foley2
Dennis Pringle2
1National

Centre for Geocomputation
2Department of Geography
NUI Maynooth

Outline
• Preliminary observations on GIS
and health
• Examples of GIS in Health:
research and practice
• Mapping people not places

Patterns in Health Data
• Three main areas:
– Mapping of disease incidence
• Relative risk estimation

– Identification of disease clustering
• Surveillance

– Ecological analysis
• Relationship between incidence and
covariates
(Lawson et al, 2003)

Health Data
• Typically patient data is available
in two forms
• Individual – perhaps anonymised
to postcode/zip code level
• Aggregate – counts are available
for a set of zones
• The nature of the data affects the
nature of the analysis

Geographic Information Systems
• GIS is often confused with mapping
• A range of activities which includes
inter alia mapping and analysis
• Will typically involve several datasets
• May also involve several different
pieces of software

Mapping: EpiINFO
• This is a rather simple analysis and
mapping program
• It can be downloaded from the internet
• It doesn’t provide much in the way of
spatial data manipulation…
• … nor are its analytical capabilities very
advanced
• It is easy to use

Spatial Data Manipulation
• GIS isn’t merely concerned with
data display or query
• It’s useful in bringing together
different spatial datasets in the
first stages of an analysis
• This may well require a range of
different geometrical and
database operations

Analysis with Spatial Data
• Statistical analysis usually involves
some separate software.
• Spatial data have some properties
which mean that SPSS isn’t always
appropriate.
• There is now a range of specialist
software – much of it available for
download

GIS may be ideal for data manipulation, display and query,
but it’s not always ideal for analysis – in epidemiology a
variety of analytical software is available … WinBUGS,
GeoBUGS (MRC Biostatistics Unit, Cambridge)

Software to implement
particular methods – for
example scan statistics - can be
downloaded quickly and easily
from the Internet. Kulldorf’s
SaTScan system implements a
range of different scan
statistical models

GIS in Health in Practice
• Facility location – dialysis units
• Facility location – obs & gyn
• Data integration – schizophrenia
in Cavan
• Data manipulation and display –
GMS card holders in Maynooth

1: Provision of Dialysis Units
• There are 5 dialysis units for renal
patients in a region with a population
of about 3,000,000
• Major population centre has two units
about 5 miles apart
• Where would you locate an extra one?
• What would happen if one was
closed?

Location-allocation
• Location-allocation problems deal
with optimum location of facilities
• Attempt to minimise total
weighted travel time/distance
from users to facilities
• What data is needed?

Data requirements
• Digitised road network with classification
of road types
• Mean travel speeds on each type for
urban/non-urban roads
• Extents of urban areas
• Anonymised postcoded patient records
• Postcode->grid reference lookup
• Locations of hospitals in region (both with
and without dialysis units)

GIS processing
• Data integration
• Determine which segments of road network
are urban
• Determine travel times using road type
urban/non-urban speeds
• Tally numbers of patients at each node in
the road network
• Find nearest nodes in network to each
hospital
• Code hospitals as ‘existing’ or ‘potential’
units

Modelling/Analysis
• Carried out using standard GIS
functions
• Convenient ‘front end’ written
using system’s macro language
(save a lot of typing)
• Explore a range of alternatives

Hospital with
dialysis unit

Study region showing hospital locations and urban areas

Patients requiring dialysis (tallied to road nodes)

Classified road network – includes roads outside study area

•

Some alternative
scenarios:

(a) Optimal allocation
of patients
(b) Provide 1 extra
unit
(c) Provide 2 extra
units

(d) Close 1 unit in
city and reallocate
its patients

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

integrate data
process data
model and analyse
generate alternative scenarios
display the results

2: Facility Location
• Two hospitals both provided facilities
for obstetrics, gynaecology, paediatrics
and a special care baby unit.
• Was there a case for rationalisation and
improving the facilities at one hospital
only?
• Would closure disadvantage any
patients?

Data
• Locations of hospitals in study region
• Anonymised postcoded finished
consultant episodes for each of the
four specialities at each hospital
• Classified road network
• Speeds associated with each road
type

Data preparation
• Postcoded records allocated grid
references using Central Postcode
Directory (POSTZON)
• Vector data converted to raster
• Patient/specialty/hospital counts
produced for each raster
• Passage times computed for each
raster cell based on road
classification and speed estimates

Roads, urban areas, patient locations, hospitals in study area

Processing
• Travel time surfaces computed for
each hospital
• Each raster cell contains time
required to reach hospital in minutes
• Assumed walking speed in ‘non road’
areas

Travel time surface for hospital S – darker = shorter time

Travel time surface for hospital T

Time differences
• If we subtract the travel time surfaces areas
nearer one hospital will have positive
values for the difference, and areas nearer
the other will have negative ones
• Boundary between positive and negative
values is boundary of catchment for each
hospital.
• If you’re on the boundary (difference is
zero), it doesn’t matter which hospital you
choose

Roads, patient locations, hospital catchments

Indifference
• If we identify those rasters where the time
difference (ignoring the sign) is, say, 5 –
people in those areas are in the 5 minute
‘indifference’ zone.
• Patients might not be worried about a 5
minute increase in their travel time, they
might be worried about 30 minutes
• Indifference zones can be computed quite
easily

15-minute indifference zone between the two hospitals

Mean access time
• For each cell in the raster we can
compute the product of the number
of patients and the time required to
reach each hospital
• Dividing this by the number of
patients gives a patient weighted
average time for each hospital

Average travel times for the various specialties. There
isn’t much to chose between specialities with the
exception of paediatric episodes.

Role of GIS
• Used
• Used
• Used
• Used
• Used

to
to
to
to
to

gather data
integrate data
process data
model data
display results

• All carried out using raster
operations

3: Schizophrenia in Cavan
• In 1996 Pringle, Waddington and
Youssef reported on evidence for the
causes of schizophrenia
• Ecological analysis of variation among
EDs
– Cases varied from 0-11 per ED
– Populations varied from 250-2750

• Poisson probabilities suggested that
one area had an unusually high number
of cases – these can be mapped

Male cases
superimposed on plot
of prevalence by ED

Male cases
superimposed on
rectified Landsat
image with ED
boundaries

4: GMS card holders: Maynooth
•Data on GMS – Medical Card holders
drawn under strict data protection
protocols from DOHC
• Anonymised address lists data matched
with GeoDirectory
•Address matching rate of around 77% in
Maynooth

• Interesting range of results around
average for the whole ED

•ED – single value 14.6 % (national average around 30%)
•Small Area – Range from 2% to 70% - noticeable internal variation and
good indicator of elderly or poor populations

•Strategic value in comparing datasets
•Important in policy analysis

Mapping invites comparison

Role of GIS
• Data integration
– Tally of cases in small areas requires
data on locations of cases and small
area boundaries
– ‘spatial join’ places small area codes
on the case records
– Counting is a standard database
operation

• Data display

Finally
• GIS encourages you to think about
space
• In a ‘conventional’ map of Ireland
areas (EDs, Counties…) are shown in
proportion to their physical size
• There are alternative ways of
displaying maps…

In a cartogram the areas are drawn
proportional to their populations –
this is a “density-equalised” map
projection

A map of the boundaries of counties and
electoral divisions – the areas are drawn
proportional to their physical size

By comparison, the more deprived
western area of Dublin, northern area
of Cork and south east of Limerick are
clearly apparent in the cartogram.

Haase’s deprivation index suggests that the
most deprived populations are in peripheral
rural areas in the north west.

Redrawing the data on the cartogram
reveals wide variation in the SMR, and also
uncovers the urban dimension which is
hidden in the conventional display

Patient data matched to Electoral Divisions –
hospital admissions for psychiatric problems – the
patchwork effect does not suggest any pattern

The cartogram reveals the urban nature
of the problem – those areas which are
more deprived are also those with
higher levels of illness/disability

Residents aged 15+ who are unable to work
through illness or disability: the map pattern
suggests that might be a largely rural
phenomenon

The roads, rail, and urban areas data have
been transformed into the same space as
the cartogram. Dublin and Cork seem
poorly provided with roads…

Other spatial information can be transformed
to the space of the density equalised
projection. Here is the familiar map of main
roads and towns.

Using a cartogram we see that the
incidence of lung cancer is randomly
spatially distributed

Lung cancer in males in New York State [Gastner & Newman: 2004]

Plotting locations of lung cancer cases, we
obtain a map which follows the underlying
population distribution – there appear to be
“clusters”

What about Ireland?
• Potentially hampered by challenge of
geocoding patient records
• Lack of small area population data
– EDs have an average population of 1150
– Wide variation in size (50-28000)
– Socially inhomogenous

Rural addresses
• A major challenge is non-uniqueness
of addresses in rural areas.
• For instance in Moy ED, 11 properties
share the address
‘Lackamore, Lahinch, Co. Clare’

What’s needed
• Postcoding should provide a
mechanism for geocoding patient data
with the granularity required for
planning the delivery of health care –
this has been the case in the UK for a
for long time
• Census data should be made available
for areas with small and similar
populations (cf: UK Output Areas)

Finally…
• GIS has an important role to play
in health studies
• Health data analysis is a rapidly
developing field
• However, in Ireland we are
hampered by data deficiencies –
now is the time for change

Maynooth: image in Google Earth

Central Dublin: image in Google Earth

Taoiseach’s Office: image in Google Earth

Downing Street, London: image in Google Earth

Maynooth: OSi orthophotograph added locally uisng KML

Sellafield: image in Google Earth

NCU – On Line Atlas of Cancer Mortality

The Philippine Institute
for Development
Studies makes an online atlas of socioeconomic indicators
available – contains
some health measures

The future
• Patient empowerment…

• GIS used to merge information from
regulatory agencies on 19196 landfill sites
• Sites buffered to 2km and postcodes
within buffer identified
• Postcoded live births (8.2x106), stillbirths
(43471) and congenital anomalies
(124,597) between 1982 and 1997
• Poisson regression modelling undertaken
• Evidence found for excess risk of
congenital anomaly and low birthweight

• Distance from each
census ward to
nearest hospital
computed
• Map shows number
of patients waiting
longer than 6
months for elective
inpatient care per
available and
unoccupied bed
within 60 minutes
travel time