1. Using maps and spatial analysis
to inform global health decision making
Peter Speyer
Director of Data Development
@peterspeyer / speyer@uw.edu
UNIVERSITY OF WASHINGTON

2. Institute for Health Metrics and Evaluation
• Independent research center at the University of Washington
• Core funding by Bill & Melinda Gates Foundation and State of
Washington
• 160 faculty, researchers and staff
• Providing independent, rigorous, and scientific measurement and
evaluations
• “Our goal is to improve the health of the world’s
populations by providing the best information
on population health”

3. The Global Burden of Disease Study
• A systematic scientific effort
to quantify the comparative magnitude of
health loss due to diseases, injuries, risk factors
• Created 1993, commissioned by the World Bank
• GBD 2010 covers 291 causes, 67 risk factors in 187 countries
for 1990, 2005 and 2010 by age and sex
• GBD country hierarchy 7 super-regions and 21 regions, based
on geographic proximity and epidemiological profiles with
• Almost 600 country, disease and risk factor experts from 80+
countries
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4. 21 GBD regions
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5. Measuring burden of diseases and injuries
Health
Disability Weight
YLDs
YLDs
Deaths
YLLs (Years of Life Lost)
YLDs (Years Lived with Disability)
YLLs
DALYs (Disability-Adjusted Life Years)
Age
Death
Average
life
expectancy
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6. GBD process & spatial challenges
Find &
manage data
Analyze data
• Standards
• Missing data
• Coverage
• Missing values
• Representativeness
• Geographies over
time
Get data used
• Interactive
visualizations
• Mapping
• Making data
actionable
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7. GBD process & spatial challenges
Find &
manage data
Analyze data
• Standards
• Missing data
• Coverage
• Missing values
• Representativeness
• Geographies over
time
Get data used
• Interactive
visualizations
• Mapping
• Making data
actionable
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8. Data inputs
Population based
• Surveys
• Censuses
• Vital registration
• Verbal autopsy
• Disease
registries
Encounter level
• Hospital /
ambulatory /
primary care
records
• Claims data
Other
• Literature
reviews
• Sensor data
• Mortuaries /
burial sites
• Police records
• Surveillance
systems
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9. Global Health Data Exchange
(http://www.ghdx.org)
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14. GBD process & spatial challenges
Find &
manage data
Analyze data
• Standards
• Missing data
• Coverage
• Missing values
• Representativeness
• Geographies over
time
Get data used
• Interactive
visualizations
• Mapping
• Making data
actionable
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16. GBD covariates and risk factors
• 300+ covariates, e.g. GDP per capita, access to water &
sanitation, education
• Gridded population used for several covariates
(incl. AfriPop, AsiaPop, AmeriPop)
– Population in coastal areas
– Population weighted average elevation, rainfall, temperature
– Population density
– Population at risk for causes like malaria
• Ambient air pollution, ambient ozone pollution (satellite,
surface monitor, TM5 global atmospheric chemistry
transport model)
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18. • Show GBD Compare map for risk factors
– Ambient air pollution
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19. GBD – spatial-temporal regression
• Capture more information than simple covariate models
• Use weighted average of residuals, based on distance in
time, age and space
• Geographic weights based on GBD regional hierarchy
(country/region/super-region)
• Vary weights based on data availability to
increase/decrease smoothing
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20. Add graph from COD Viz
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21. GBD process & spatial challenges
Find &
manage data
Analyze data
• Standards
• Missing data
• Coverage
• Missing values
• Representativeness
• Geographies over
time
Get data used
• Interactive
visualizations
• Mapping
• Making data
actionable
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27. Small area estimation
• Analyze health patterns outcomes and intervention
coverage for 72 districts in Zambia
• Most data only representative at country/province level
• Modeling approaches
– Pooling data over several years
– Borrowing strength by exploiting spatial correlations
– Using covariates
• Add validation environment
– Identify most appropriate measurement strategy
– Establish minimum sample size for future data collection
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34. Remaining tasks and challenges
• Add more spatial covariates
• Conduct burden study at sub-national level
• Identify best practices for managing geographies
(national, subnational) globally over time
• Is there a portal for gridded data?
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