Transcript Health_GIS_AfricaGIS_Oct_09_short.ppt

eHealth + GIS/RS
Activities by
CGIS-NUR +
Misc. Partners
OpenMRS Team
eHealth + GIS/RS = Projects + Partners
1.
PIH (Partners in Health) & MVP + Doris Duke = communitybased Health IT, patient tracking, OPENMRS
2.
Saddleback Church (Kibuye)- GPS waypoints of + 700 schools,
health centers, water-sites, churches = RDG…
3.
AHI (Adventist Health International) + LLU SPH—
Geoinformatics + Univ. of Redlands, various projects
4.
MOH, TRAC-Net, PNLIP – Disease Surveillance, e.g. TB, HIV,
Malaria, MCH risk, “stock-out” management, etc
5.
DFGFI = Ecosystem Health & Conservation
6.
SEI (Stockholm Environment Institute + EU-FP7 + TCD (Trinity
College Dublin) – Malaria + Climate Change + disease
7. IDRC, ITC, CGIS, ESRI, OSGeo…tech support
The OASIS-I Project
Open Architectures, Standards & Information
Systems for Healthcare in Africa
Chris Seebregts, PhD
Biomedical Informatics Research, Medical Research
Council
Department of Computer Science, University of
KwaZulu-Natal, South Africa
Jembi
+
Acacia Program
Acacia’s mission is to support research on ICTs that
improve livelihood opportunities, enhance social
service delivery, and empower citizens while building
the capacity of African researchers and research
networks.
OASIS-II Project
Now considering how to
Integrate GIS into ICT4D
Next Steps with IDRC
WIKI – RESOURCES, TOOLS, ETC
INTEGRATING GIS INTO eHEALTH INITIATIVES IN AFRICA
Chairs: Robert Ford, CGIS-NUR/Rwanda and Chaitali Sinha, IDRC
Cape Town, South Africa, September 17th, 2009
Workshop Agenda and Programme
Go to =
(http://idrc-gisworkshop.pbworks.com/)
The KEY QUESTION
Does geography play a role in ehealth and
health services delivery, quality control,
planning, health outcomes,
epidemiological research, etc…?
Where and how does GIS/RS fit into the
OASIS-II agenda and
What do we need to do to enable it to
succeed?
Questions & Answers Sought
from Participants
• What are the USE-CASES (applications) for
which geospatial data, analytic tools, APIs,
visualizations or other products/webservices
are/were
• most useful in adding value to clinical,
outreach, public health, research, and other
related community-development
applications?
Key Issues - Constraints
• Technical, e.g. software, hardware,
instrumentation, connectivity, integration of
systems, architecture and design,
geodatabases, analytic tools, APIs, etc.
• Logistics, e.g. travel, communications,
scheduling, coordination, etc.
• Human capacity, e.g. training, availability of
personnel, etc.
Key Issues - Constraints
• Data & Webservices, e.g. access, availability,
quality-control, appropriateness, validity,
reliability, timeliness, etc…
• Cost/funding …finances, donor support, etc.
• Sustainability issues
• Institutional, e.g. legal, administrative, policy
constraints, privacy, etc.
Next Steps and Needs
• How to better integrate GIS/RS tools, data,
visualizations, web-services into:
–OpenMRS
–CommCARE
–OpenROSA & JavaROSA
OASIS 1 Objective 1
Support the Implementers Network
www.openmrs.org
OpenMRS sites
• 9 countries so far
– Rwanda
– Kenya
– Lesotho
– Malawi
– South Africa
– Tanzania
– Uganda
– Haiti
– Zimbabwe
• In process/review:
– Peru, Mozambique, others?
OASIS 1 Objective 4
Standards-based Data Integration
OASIS 1 Objective 3
Interoperability
OpenMRS Data Model
Encounter Forms (HIV / ART and TB)
OASIS Objective 5
Build Sustainable Business Models
Jembi
• The fifth objective of OASIS 1 is to develop sustainable business
models for the eHealth and health information system solutions
developed by OASIS nodes in South Africa, Mozambique and
Zimbabwe to provide continuity and sustainability.
• We have established a non-governmental organization, Jembi,
registered as a voluntary association in South Africa.
• Jembi is presently managing several core OASIS
objectives.
OpenROSA & JavaROSA
CommCare Application
Neal Lesh
D-tree International,
Dimagi Inc
OpenROSA: Mobile Data Collection
CommCare: JavaROSA
app. for CHWs
HEALTH PREGNANCY
Vitamin A, Iron
Started tetanus
Finished tetanus
Started IPT (Malaria)
Finished IPT (Malaria)
Deworming tablet
Has birth plan
Tested for HIV
22
What we did at the workshop /
planning session?
Started by introducing GI Science to
non-GIS types who are very steeped
in the Open Source world…
What is GIS ?
Spatial Database
SOIL
LAND TENURE
SPATIAL
DATA
DESCRIPTIVE
DATA
HYDROLOGY
ROAD NETWORK
POPULATION
VEGETATION
STUDY
ZONE
ZONE ETUDE

GIS : a powerful Decision Support System for
Management, Planning and Projection
(Basics) = Epidemiology =
• Person
PPP+T
•Place - Community
• Pest (Agent)
• Time
History
Oxford Street of spatial analysis
Dean
Street
John Snow
(18131858)
Soho
(London)
cholera
epidemic of
1854.
Regent
Street
Brewer St.
Piccadilly
26
John Snow
infected pump
(1813-1858)
Soho (London)
cholera
epidemic of
1854.
27
Memorial to John Snow
where well stood
Modern Spatial Epidemiology
Digital base map
Map with victim locations
Geocoding
process
address
File of
disease
victims
Geocoded victim file
28
GIS
analyst
tool
Levels of GIS Use
Level #1 – Visualization & Localization
Level #2 – Operations Management,
QC, Tracking, Monitoring & Evaluation
http://flutracker.rhizalabs.com/
181 out of 426 Health Facilities covered
22 District Hospital
143 Health Centre
5 Dispensaire
7 Prison Hospitals
4 Other
Level #3:
Advanced Geospatial Analysis
• Patterns of Satisfaction with Health Services
in Malawi: An Analysis using GIS Technology
• By Gideon Mazinga, Loma Linda University
School of Science & Technology
A pattern of districts with
higher satisfaction
highlighted in scatter plot
Moran’s I =0.4045
Districts with higher % in
Southern region
Anselin’s LISA
CLUSTER MAP
Satisfaction of health services
based on spatial lag and
standardized value of
satisfaction.
Tom McConnell= ArcGIS & eHealth
With remote help from ESRI/Redlands
Overview of the ArcGIS Toolkit
for Spatial Analysis in eHealth
• GUI access to the most frequently
used tools
• ArcToolbox – an expandable
collection of ready-to-use tools
• ModelBuilder – a visual programming
environment
• Python – A FOSS scripting language
integrated with
ArcGIS
ArcGIS 9.3 – ArcMap - ArcEditor GUI & Toolbox
Regression analysis
• Regression analysis allows you to:
– Model, examine, and explore spatial relationships
– Better understand the factors behind observed spatial patterns
– Predict outcomes based on that understanding
Ordinary Least Square
(OLS)
Geographically Weighted Regression
(GWR)
100
80
60
40
20
Observed Values
0
Predicted Values
0
20
40
60
80
100
40
GIS INTEGRATION IN eHEALTH
CASE OF RWANDA
MOH
Representatives
Uwayezu Gilbert
and
Murenzi Daniel
• Our logic frame: Applying GIS technology to process HIV disease strategic
information to support decision-making and guide work processes (public
health actions) in order to accomplish the program mission.
Challenges
• Not enough skilled personnel to implement
GIS projects in health sector.
• No tools to run GIS applications (Software and
Hardware).
• Getting spatial data is still an issue…(too
expensive).
saving lives through
improvements in global health
Experience sharing from Pakistan
OpenMRS MDR-TB Module
implementation and scale-up
Aamir Khan MD PhD
Executive Director, Interactive Research & Development
Executive Director, Indus Hospital Research Center
Associate Faculty, Johns Hopkins University
8/4/2016
8/4/2016
8/4/2016
Primary Health Care
8/4/2016
8/4/2016
8/4/2016
8/4/2016
8/4/2016
Low-cost, real-time surveillance for
dog bite and rabies in Pakistan
Study
sponsor:
Real time data visualization
Total Dog Bite Cases Per Week (n=1707)
Feb 24 to Sep 2 2009
120
100
Number of Dog bite cases
24
80
9
16
14
26 17
13
21
11
17
8 9
60
40
64
60 50
20
62
4 11
13
34
44 48
LUHMS - Feb 24
11
57
22
3
VH-Aug 18
JPMC - Feb 25
14
37
Indus - Feb 24
44 43
40
12
CHT - Mar 13
39
20
42
23
17
10
14
19
38
13
10
10
24
7
5
13 16
10
1 9 12 10
6
4
6
16
15
7
15
20
8
6
2 1
4
4 4 4 7
10
5 5
14
2
4
3 9 8 5 2
8
9
9
8
8
8
8
7 4 3
6 7 4 7
5 2 1 7
5
5
5
4
4 1 2 2 3
2
1
1
19
0
23 16
33
43
10
Number of weeks of Surveillance
SCHQ - Apr 2
Status of some eHealth +
GIS/RS Projects we’re
working with…
GIS & Remote Sensing Research and
Training Centre
National University of Rwanda
(CGIS-NUR)
GIS for
Improving
Health Service
Delivery
Cheryl AMOROSO, John DERIGGI, Nicole GREY, Danielle RICHEY, Peter
BAREBWANUWE, Donn Gaede
GIS – EMR Pilot Project
Partners in Health – Rwanda
Pilot in Rwinkwavu Sector
January -June 09
Aim:
Determine if collecting and
using GIS data is feasible
in our environment
Estimate time of collecting
data
Estimate costs of “doing this
properly”
Mapping and Training Outcomes of
GIS Pilot • Mapped all of the 48
villages, 14
schools,100+ water
sources,homes of 157
CHWs, and homes of
600+ HIV, TB and
chronic care patients
• Developed
Kinyarwanda GIS
training guide
• Informal and formal
(by CGIS) training for
community health
workers supervisors to
collect GPS data
Associating Coordinates to Patients
1 ) Patient address goes
into the system
2 ) If the address is valid
then get the coordinates
3 ) Associate the
coordinates to the
patient's address
umudugudu_id
latitude
longitude
0104020507
-1.9534521
30.651238
…
…
…
address_id
precision
lat
lon
34
1
-1.9534521
30.651238
…
…
…
Using The Google Earth and Maps API
Web Browser
Retrieve
patient data
in JSON
Retrieve
imagery from
Google
Public
internet
connection
Patient data
Open Source Spatial Data
Infrastructure
OpenLayers WMS
Module
WMS/WFS
WMS/WFS
uDig (Desktop App)
GeoServer
Shape
Files
Data Sources
PostGIS
Imagery
• This work is
possible – but we
need to resource
it
• There is an
opportunity for
GIS to integrate
with OpenMRS
• The community is
interested in GIS
technology, and
capable of
understanding and
using it with some
initial support
What we learned
GIS and PIH: Future plans
• Continue to map villages in
District Hospital catchment
area
• Link GIS data with primary
care data in EMR
• Make the technical
architecture a reality
• Produce sharable products
related to GIS and
OpenMRS
A Healthy Environment is
Evident from the Inhabitants
DFGFI ECOSYSTEM HEALTH
PROJECTS:
De-Worming and Hygiene/Conservation
Education,
Clean Water Access, Essential Protein Access,
And Clinic Rehabilitation
Gorillas and Livestock and People Together
How Do
The Ecosystem
Health Projects
Work?
First,
We go to the
People
And the
Gorillas
New Project Rwanda
Capping of Existing
Community Water Source
Collection Containers
Will Be Installed for the
Dry Season
Climate change impacts
in tropical Africa (i.e. HEALTH)
David Taylor
Professor of Geography
Trinity College University of Dublin
[email protected]
80
Hope to investigate through an opportunity
provided by the EU 7th Framework:
FP7-Africa-2010 (the 'Coordinating
call for Africa‘)
HEALTHY FUTURES PROJECT
ENV.2010.1.2.1-1 The effect of environmental change on the occurrence
and distribution of water related vector-borne diseases in Africa
Working with partners in eastern Africa, the proposed research aims to
develop (1) regional climate change modelling, and the ability to anticipate
future changes in climate in the region of relevance to water-related infectious
diseases, and (2) the capacity of health services in impacted countries to
respond to early warnings of future heightened risks of outbreaks.
81
83
A linked system of
oscillation also
occurs in the
Indian Ocean –
known as the
Indian Ocean
Dipole (IOD)
84
IPCC 4th AR WGII (2007)
85
UK Met Office/Hadley Centre PRECIS simulation
for southern Africa:
(PRECIS= Predicting
Regional Climates for
Impact Studies)
For southern Africa, regional models
indicate climate conditions will
become warmer, and droughts will
become increasingly common
(increased seasonality)
86
Paleo temp change
Ice core image
Warming-related changes present problems generally because we
inhabit a predominantly cool-adapted planet
Exact nature of warming-related impacts is difficult to predict
87
Possible global
warming impacts in
Africa:
Reduced agricultural productivity
(and food security)
Increased political instability and
insecurity
Increased frequency of catastrophic
events (e.g., storms, biomass fires
etc)
Loss of important habitats (e.g, rain
forests, fynbos etc) and destruction of
natural water towers
Increased incidence of some
infectious diseases (e.g., malaria,
cholera, rift valley fever etc)
88
Map of eastern Africa
showing differences in
altitude, and six areas
where incidence of
malaria is on the
increase (& see below)
Figure above shows
percentage change in
incidence of Plasmodium
falciparum malaria for same
areas (1980–2000).
From Hay et al. (2002)
89
But how much of
increased incidence of
malaria is due to
recent climate
changes? And how
much is due to other
factors, such as
resistance & changes
in population?
(a) = % change in population
per hospital bed, 19801990;
(b) = % change in NDVI
between 1981-1985 and
1996-2000 (proxy of
changes in soil moisture)
(c) = percentage change in the
human population – total
(open bars) and urban
(solid bars) – 1980-2000
90
Modeling Malaria-Risk Evolution
with Climate Change –Current Efforts
GIS Tools for
Disease and
Landscape
Analysis
In East Africa
Malaria,
Rift Valley Fever
Schistosomiasis
Dengue
Malaria & Climate
SEI Case Study
SEI – Stockholm Environment
Institute (Oxford)
Assessment of the economic impact of
climate change on malaria –Rwanda/ Burundi
Difficulties: 1) how to accurately assess the true clinical burden
2) how to assess the social, economical and psychological implications
At this stage of the assessment we are addressing the first issue.
1) Malaria is under-reported, but (when reported) over-diagnosed (the clinical diagnosis
lacks specificity). This makes incidence data difficult to use.
We have chosen to use survey prevalence data to obtain accurate information on
the burden of malaria. These data are not recent, and may not reflect the current
intensity of the disease and the (recent) successes in its control, but are usefull to
determine the potential of malaria, and to be able to assess the cost to avert this burden.
We have used prevalence data on a range of altitudes (as altitude is a local proxy for
temperature), and used the expected changes in temperature to shift the malaria altitude
graph according to the local relation between temperature and altitude, taking into
account the size of the population on different altitudes.
The decline in malaria with altitude in Kigezi,
highlands comparable to those
in Rwanda & Burundi
5 year moving average of survey points
80
70
y = -0.1082x + 175.67
(R2 = 0.5247, 900-1600 m, untansformed data)
60
50
40
30
20
10
0
900
Altitude1300
(m)
1100
1500
1700
1900
2100
Data: Garnham, 1946 & Zulueta, 1964,
Excluding lake-side prevalence point
reported by Cox et al, 1999)
Population between 1000 and 2500 m. Shift of the prevalence
curve based on the Rwanda lapse rate ( 150 m. for 1oC)
80
population x 1000
1200
prevalence
70
60
+ 2 0C
1000
800
50
40
+ 1 0C
600
30
Geographic
extension
400
20
+ 1 0C
200
prevalence (% )
Prevalence x Population = “Cases”
1400 Increasing
10
+2
0C
0
0
2500
2400
2300
2200
2100
2000
1900
1800
1700
1600
1500
1400
1300
1200
1100
1000
altitude (m)
+ 1oC increase in ambient temperaure. Shift curve:150 m.
+ 2oC increase (output models for 2046-2065). Shift curve: 300 m.
Baseline prevalence
Calculation of changes in annual malaria
episodes under 1o C and 2o C temperature
increase
• Sum of the malaria burden (population x prevalence) for all 50 meters
intervals between 1000 and 2500 m. of altitude.
• We conservatively estimate that the episodes of malaria per year per 100
of population equals the prevalence rate.
• Baseline morbidity: 1.001.000 cases per year
•
+ 1oC: additional 1.086.000 cases (108 % over baseline)
+ 2oC: additional 2.520.000 cases (252 % over baseline)
• The baseline (prevalence) is considered the malaria potential, and may
not reflect the current level of endemicity (which may be lower due to
interventions, e.g. mosquito-nets)
• Assumption: saturation malaria prevalence at 70%
• Rural Population: 1999 ? (urban population excluded from analysis).
The Main Challenges
Outlined
Where do we go next?
Are we reaching the REAL users?
Poverty Mapping:
Small-Area Estimation
http://www.povertymap.net/
http://econ.worldbank.org/WBSITE/EXT
ERNAL/EXTDEC/EXTRESEARCH/EXT
PROGRAMS/EXTPOVRES/0,,
http://gisweb.ciat.cgiar.o
rg/povertymapping/
PPGIS = Public Participation GIS
• PPgis.net, the electronic forum on
participatory use of geo-spatial information
systems and technologies.
• About PPGIS =
http://www.ppgis.net/ppgis.htm
• GeoWeb GIS tools =
http://www.ppgis.net/gis_tools.htm
• Participatory spatial information
management - bibliographic collections
• http://www.ppgis.net/bibliography.htm
Gender Daily Calendar Data
Collection
What is Needed?
Resources
Institutional Support
Training
Better Tools for Specific Applications—
The challenge: How do we get the health
including Health
and sustainable development community
more involved in the SDI-building process
so that both the poor and vulnerable truly
benefit as well as decision-makers?
Page last updated at 10:05 GMT,
Monday, 21 September 2009
11:05 UK
RWANDA
SDI…is needed
for eHealth
to work…
Spatial
Data
Infrastructure
Courtesy: Kate Lance, NASA-SERVIR, ITC
CGIS-NUR NEXT STEPS
• Will Remake IDRC ICT4D Atlas to be a
“geoportal” to support ENRM and eHealth
community
• Will continue several capacity-building
workshops in 2010 focused on “spatial
epidemiology” and OSGeo—FOSS-GIS.
• Will continue eHealth projects in Rwanda and
elsewhere in Africa with IDRC
• MATRIX comparing OSS and COTS
software…on GoogleDocs
Shared Catalog Listings Can Unite the Rwanda’s Community
and Empower Individual Groups to Work with Government
and Academic Spatial Technology Centers
Hope to investigate through an opportunity
provided by the EU 7th Framework:
FP7-Africa-2010 (the 'Coordinating
call for Africa‘)
HEALTHY FUTURES PROJECT
ENV.2010.1.2.1-1 The effect of environmental change on the occurrence
and distribution of water related vector-borne diseases in Africa
Working with partners in eastern Africa, the proposed research aims to
develop (1) regional climate change modelling, and the ability to anticipate
future changes in climate in the region of relevance to water-related infectious
diseases, and (2) the capacity of health services in impacted countries to
respond to early warnings of future heightened risks of outbreaks.
109
First Product Outcome – Soon…!!
Product
Website
Type
Description
Overall Rating?
QuantumGIS/Grass GRASS
gvSIG
www.qgis.org
grass.osgeo.org/www.gvsig.gva.es
Desktop
Desktop
Desktop
****
Data Formats & Acquisition
Yes
No
Yes
Yes
Yes
No
Yes
Yes
Yes
Yes
No
No
Geocoding by direct address match
No
No
No
Change projection
Yes
Yes
Yes
Yes
Shapefile
File Geodatabase
Yes
No
Convert coordinates to points
Vector-Raster 2-way conversion
3D Volumes (voxels)
No
Feature editing
Geometric topologies
Yes
Yes
No
Geocoding by street network
No
Editing & Conversion
Attribute editing
Yes
Projection on the fly
Query
Simple identification (row display,Yes
pop-ups)
NEXT
STEP…
2010
Next
Steps
with IDRC
CONTACT
Robert E. Ford, MPH, PhD
Research Professor
CGIS-NUR, Huye, Rwanda
http://www.cgisnur.org/
Email: [email protected]
Personal homepage:
WEB: http://geobobford.com/