Transcript Introduction to the Geography of Health

An Introduction to the Geography of Health
Chapter 10: Health and GIS
Data Source: World
Resources Institute (2010)
Cartography by Peter Anthamatten
GIS Concepts and Terminology
A GIS is “a computer system that can manipulate, store and
analyze data that is referenced by location” (USGS 2007).
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All spatial information in a GIS
must be georeferenced to
known features in some way.
In a geographic coordinate system,
locations of features are described in
terms of their angle from imaginary
planes on earth, most commonly using
latitude and longitude coordinates.
Cartography by Peter Anthamatten
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UTM coordinates
In projected coordinate systems,
location is defined by its distance
from an established x- or y-axis on
a planar surface.
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Spatial data in a GIS are stored primarily
using one of two spatial models.
Each of these models is designed to
represent the complicated nature of
reality in a form that can be stored,
manipulated, and analyzed in a GIS.
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A vector data model is built from a
series of spatial coordinates that define
the locations of features on the map.
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An “attribute table” is connected to
each feature and contains data related
to that feature. This example contains
attribute data on infant mortality rate
and percent overweight.
In this vector data GIS, country
boundaries are represented by a
polygon, which is comprised of points,
whose location are specified in the
database by geographic coordinates.
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In raster data models cells are used to represent a particular area on
earth, in this case one square kilometer; the space represented by
each cell defines the resolution of the model. Raster models work
well with continuous data such as elevation and temperature.
The map is comprised of a grid;
each cell has a value (an
“attribute”) representing elevation
in meters above sea level.
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Sometimes translating between
data models can produce some
novel forms of visualization.
In this example of density
analysis, for instance, the brown
shading on the map (raster data)
represents the density of grocery
stores (vector data).
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One especially useful feature of GIS is its
ability to combine multiple layers of data,
enabling the discovery and exploration of
spatial patterns in different phenomena.
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Data Source: Colorado
Department of Public
Health and the
Environment (2010)
If we are interested in exploring heart disease mortality patterns in
Colorado, we could use a GIS to link a table containing the number
of age-adjusted deaths from heart disease in each county to a file
of county boundaries in order to produce a map.
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Does heart disease appear
to bear any relationship to
urban areas?
Data Source: Colorado
Department of Public
Health and the
Environment (2010),
Nationalatlas.gov
(2010)
Other spatial data can then be overlaid to see if there is
any relation between the patterns. The red areas on the
map are urban areas, as defined by the US Census.
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What other data
might be worth
exploring?
Data Source: Colorado
Department of Public
Health and the
Environment (2010),
Nationalatlas.gov
(2010), USGSEROS
(2010)
Additional layers of data can be added or removed as we consider
different ideas. The hatched area on this map shows areas of
elevation greater than 2000 meters above sea level.
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A query is a way to examine
a subset of features of a GIS.
In this example, a query has been
performed to identify all districts
that are within five kilometers of
a major airfield in Kenya in order
to estimate the number of people
that would be served by clinics
attached to the airfields.
Data Source: WRI (2010)
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Remote sensing is the measurement of
electromagnetic radiation reflected from the
earth’s surface in order to record geographic
information such as vegetation cover, land
use, rainfall, or atmospheric conditions.
This satellite image
shows smoke and haze
in northern India.
Source: NASA/GSFC, MODIS Rapid Response (2006)
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Why is remote sensing
particularly useful for studies
in low-income contexts?
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GIS Applications in Health Studies
The ability of GIS to link information on the basis of location has
enabled or enhanced many geographic studies of health.
GIS has been used in health studies in a variety of ways including:
1) Exposure assessment
2) Disease surveillance
3) Cluster Analysis
4) Analyzing accessibility and utilization of healthcare facilities
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1) Exposure Assessment
GIS can greatly facilitate exposure assessment:
estimation of the amount of a toxin that people are
exposed to on the basis of available information such
as data from pollution measuring stations.
Photo by Shane Houdek (2009)
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Pollution in Beijing
An Introduction to the Geography of Health
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In an example of a
geographic exposure
assessment, Brown et al.
(1984) conducted a casecontrol study to determine
whether proximity to
industrial facilities was
associated with increased
risk for lung cancer.
Reprinted from Environmental Research, vol. 34, Brown, L, Pottern, L., and W. Blot, “Lung cancer in relation to
environmental pollutants emitted from industrial sources”, page 251, © 1984, with permission from Elsevier.
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Reprinted from Environmental Research, vol. 34, Brown, L,
Pottern, L., and W. Blot, “Lung cancer in relation to
environmental pollutants emitted from industrial sources”,
page 251, © 1984, with permission from Elsevier.
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The study area was divided into a
grid of one-by-one kilometer
cells. Each cell was then assigned
an exposure value based on
proximity to a polluting stack and
results from soil samples.
An Introduction to the Geography of Health
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Basic information on the
direction and speed of wind
was also taken into
consideration in building the
exposure assessments.
The authors concluded that
there was a weak association
between cancer risk and
proximity to industrial sources.
Reprinted from Environmental Research, vol. 34, Brown, L, Pottern, L., and W. Blot,
“Lung cancer in relation to environmental pollutants emitted from industrial sources”,
page 251, © 1984, with permission from Elsevier.
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In another study, Dubnov et al.
estimated children’s exposure to
nitrogen oxides and sulfur dioxide in
Israel by interpolating measurements
from monitoring stations.
Interpolation is a method for producing
estimates for locations where no data
were collected, based on information
collected at other locations
Reprinted from Environmental Research, vol. 103, Dubnov, J.,
Barchana, M., Rishpon, S., Leventhal, A., Segal, I, Carel R., and
Portnov, B., “Estimating the effect of air pollution from a coalfired power station on the development of children’s pulmonary
function, page 89, © 2007, with permission from Elsevier.
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2) Disease Surveillance
A disease surveillance system uses systematic data collection
to monitor for signals that could indicate new outbreaks of
disease or increasing incidence of a disease.
GIS has enabled spatial data to be utilized in a more dynamic
and systematic way than is possible with paper maps.
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Source: Srivastava et al. (2009)
Courtesy of the International Journal of Health Geographics
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In this study, researchers identified
malaria “hotspots” in Madhya Pradesh,
India, suggesting where government
anti-malaria efforts could be focused.
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3) Cluster Analysis
The term disease cluster refers to a group of disease cases or
high incidence rates beyond what we would normally expect
to observe in a particular time and place (CDC 1990).
The term is generally reserved for unusual groupings of cases
of non-infectious diseases—particularly cancers.
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While it is tempting to
visually examine a map of
disease incidence in order to
identify clusters, great care
must be taken to distinguish
between disease clusters
resulting from a specific
cause and those that have
occurred randomly.
Source: Goovaerts (2006)
Courtesy of the International
Journal of Health Geographics
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Source: Chi et al. (2009)
In spite of some reservations about cluster studies, careful work can
reveal useful findings. This map is from a study of birth defects.
Although high rates of birth defects were found in several villages, only
one legitimate disease cluster was identified (circled).
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4) Analyzing Access and Utilization of Healthcare
GIS can inform discussions of healthcare in a variety
of ways, including mapping healthcare services,
evaluating access to services, and analyzing utilization
patterns (Cromley and McLafferty 2002).
A variety of techniques can be used to assess
healthcare access and utilization, e.g.:
1) Density mapping
2) Network analysis
3) Least cost path analysis
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1) Density mapping uses population and physician data that
are collected for administrative units roughly equivalent to
the distance a person could reasonably be expected to
travel to visit a certain type of healthcare service in order to
produce maps of the number of people per general practice
physician for each administrative unit.
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2) Network analysis analyzes distance between patients and health
facilities, taking into account the transportation network.
Source: Paez et al.
(2010). Courtesy of the
International Journal
of Health Geographics
Paez et al. analyzed accessibility to healthcare by comparing different populations in
Montreal, Canada. The map on the left shows the average length of trips to healthcare
facilities for residents without a car and the map on the right shows the average trip
length for car-owning residents. Darker shading represents greater trip length.
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3) Least cost path analysis uses information about the transportation
network to calculate the shortest distance and travel time required to
go from any point to the nearest healthcare facility.
The shading on this map of
New Zealand represents the
average total time for residents
to travel to the nearest hospital
emergency department in 1991
and 2001.
Reprinted from Health Informatics Journal, vol. 12, Brabyn, L. and P. Beere, “Population access to hospital emergency
departments and the impacts of health reform in New Zealand”, page 234, © 2008 by SAGE. Reprinted by Permission of SAGE.
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Public Participant GIS (PPGIS)
The concept behind Public Participation GIS (PPGIS) is using
GIS to enable communities that have traditionally assumed the
role of subject in studies to become active participants in the
formulation, analysis, and dissemination of data.
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Challenges in GIS and Health
Reprinted from Health Policy, vol. 74, Hassan, M., “Arsenic poisoning in Bangladesh: spatial mitigation planning with GIS and public
participation”, page 225, © 2005, with permission from Elsevier.
In this example of PPGIS, citizens in Bangladesh were given maps of their local area
and asked to discuss suitable locations for tube wells, to explain their reasoning for
their proposed placements, and to plot their proposals on maps.
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Challenges in GIS and Health
While there is much power, utility, and potential of GIS
in the study of the geography of health, it should not be
viewed as a panacea for all spatial-analytical problems.
A number of problems and challenges must be carefully
considered in GIS-based health research.
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Challenges in GIS and Health
Access
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Despite efforts in participatory GIS, the substantial
investment and maintenance costs associated with GIS
technologies continue to put them out of reach of many
individuals and organizations.
An Introduction to the Geography of Health
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Challenges in GIS and Health
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Access
Despite efforts in participatory GIS, the substantial
investment and maintenance costs associated with GIS
technologies continue to put them out of reach of many
individuals and organizations.
Data Quality
The error inherent in original sources of data may be
amplified when the data are translated into digital
format, and further amplified when they are transferred
between different GISs, modeled into different formats,
or converted from one data model to another.
Anthamatten and Hazen
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Challenges in GIS and Health
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Access
Despite efforts in participatory GIS, the substantial
investment and maintenance costs associated with GIS
technologies continue to put them out of reach of many
individuals and organizations.
Data Quality
The error inherent in original sources of data may be
amplified when the data are translated into digital
format, and further amplified when they are transferred
between different GISs, modeled into different formats,
or converted from one data model to another.
Privacy
While guidelines are in place to protect an individual’s
confidentiality in data that are usually stored in a
spreadsheet or database, there are few equivalent rules
to protect an individual’s “spatial confidentiality” (Leitner
and Curtis 2006).
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Conclusion
GIS has had an important impact on the way that health
geographers approach their work, making it possible to store,
display, and analyze huge quantities of spatial data.
Health geographers must always bear in mind that GIS is only
useful to the study of health as a tool, however. Without an
understanding of the biomedical, ecological, demographic,
social, political, and cultural facets of health, GIS may provide
answers but offers little insight.
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Discussion Questions
1. We discuss some of the factors that GIS is able to incorporate into
exposure assessment, such as wind and weather patterns, residence
history, and daytime activity. Assuming that expense is no barrier, what
other factors could be incorporated into a GIS-based exposure model to
improve its accuracy? For example, what should we model in order to
estimate exposure to diesel fumes?
2. Think of a health problem that you have studied. How could a GIS serve to
shed light on the problem? What sorts of data would you combine in your
analysis?
3. In your opinion, what are the most important GIS applications for health
in a low-income context? What are the key applications in a high-income
context? In what ways could a GIS contribute to the health problems that
are particularly relevant to where you live?
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Discussion Questions
4.
5.
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In some places, spatial data are not available to the public. In other
places data are freely available, while in yet other places governments
charge people for spatial data. Do you think that spatial data collected
by the government should be freely available for the public to use? Why
or why not?
One criticism of GIS is that it enforces a specific, dominant worldview
that is not compatible with some cultural and social contexts. Can you
think of examples of health contexts in which GIS is unable to “model
reality” in a useful way for a particular group of people?
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Chapter 10
References
Brabyn, L. and Beere, P. (2006) ‘Population access to hospital emergency departments and the impacts of health reform
in New Zealand’, Health Informatics Journal, 12: 227–37.
Brown, L. M., Pottern, L. M. and Blot, W. J. (1984) ‘Lung cancer in relation to environmental-pollutants emitted from
industrial sources’, Environmental Research, 34: 250–61.
[CDC] Centers for Disease Control and Prevention. (1990) Guidelines for Investigating Clusters of Health Events [Online].
Available: <http://www.cdc.gov/mmwr/preview/mmwrhtml/00001797.htm> (Accessed 20 May 2010).
Chi, W. X., Wang, J. F., Li, X. H., Zheng, X. Y. and Liao, Y. L. (2008) ‘Analysis of geographical clustering of birth defects in
Heshun county, Shanxi province’, International Journal of Environmental Health Research, 18: 243-52.
Colorado Department of Public Health and Environment. (2010) Colorado Health Information Dataset, Death Statistics
[Online]. Available: <http://www.cdphe.state.co.us/cohid/deathmenu.html> (Accessed 03 November 2010).
Cromley, E. K. and McLafferty, S. (2002) GIS and Public Health, New York: Guilford Press.
Dubnov, J., Barchana, M., Rishpon, S., Leventhal, A., Segal, I., Carel, R. and Portnov, B. A. (2007) ‘Estimating the effect of
air pollution from a coal-fired power station on the development of children's pulmonary function’, Environmental
Research, 103: 87–98.
Goovaerts, P. (2006). ‘Geostatistical analysis of disease data: visualization and propagation of spatial uncertainty in cancer
mortality risk using Poisson kriging and p-field simulation’, International Journal of Health Geographics, 5(7). [Online].
Available: <http://www.ij-healthgeographics.com/content/5/1/7> (Accessed 12 January 2011).
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References
Leitner, M. and Curtis, A. (2006) ‘A first step towards a framework for presenting the location of confidential point data
on maps - results of an empirical perceptual study’, International Journal of Geographical Information Science, 20: 813–
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NASA/GSFC, MODIS Rapid Response. (2006). Haze across Northern India. [Online] Available:
<http://rapidfire.sci.gsfc.nasa.gov/gallery/?2006036-0205/India.A2006036.0740.2km.jpg > (Accessed 03 January 2011).
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Nov 2010).
Paez, A., Mercado, R., Farber, S., Morency, C. and M. Roorda. (2010). ‘Accessibility to health care facilities in Montreal
Island: an application of relative accessibility indicators from the perspective of senior and non-senior residents.
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<http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2987784/figure/F4/> (Accessed 12 January 2011).
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[USGS] United States Geological Survey. (2007) Geographic Information Systems [Online]. Available: <http://
egsc.usgs.gov /isb/pubs/gis_poster/index.html> (Accessed 1 March 2010).
[USGSEROS] United States Geological Survey Earth Resources Observation and Science Center. (2010) Global 30 ArcSecond Elevation (GTOPO30) [Online]. Available:
<http://eros.usgs.gov/#/Find_Data/Products_and_Data_Available/GTOPO30> (Accessed 03 November 2010).
[WRI] World Resources Institute. (2010) Download Kenya GIS Data [Online]. Available:
<http://www.wri.org/publication/content/9291> (Accessed 7 November 2010).
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