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

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
• 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
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DALYs (Disability-Adjusted Life Years)
Health
Age
Death
Deaths
Average
life
expectancy
YLLs
YLLs (Years of Life Lost)
YLDs YLDs
YLDs (Years Lived with Disability)
Disability weight

6. GBD process & spatial challenges
• Standards
• Coverage
• Representa-
tiveness
• Geographies
over time
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• Missing data
• Missing values
• Interactive
visualizations
• Mapping
• Making data
actionable
Find &
manage
data
Analyze data
Get data
used

7. GBD process & spatial challenges
• Standards
• Coverage
• Representa-
tiveness
• Geographies
over time
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• Missing data
• Missing values
• Interactive
visualizations
• Mapping
• Making data
actionable
Find &
manage
data
Analyze data
Get data
used

8. Data inputs
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•Surveys
•Censuses
•Vital registration
•Verbal autopsy
•Disease registries
•Surveillance
systems
Population-based Encounter-level Other
•Hospital/
ambulatory/
primary care
records
•Claims data
•Literature
reviews
•Sensor data
•Mortuaries/
burial sites
•Police records

9. Global Health Data Exchange
(http://www.ghdx.org)
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14. GBD process & spatial challenges
• Standards
• Coverage
• Representa-
tiveness
• Geographies
over time
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• Missing data
• Missing values
• Interactive
visualizations
• Mapping
• Making data
actionable
Find &
manage
data
Analyze data
Get data
used

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16. GBD covariates and risk factors
• 300+ covariates, e.g., GDP per capita, access to
water and sanitation, education
• Gridded population used for several covariates
(including 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
• Standards
• Coverage
• Representa-
tiveness
• Geographies
over time
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• Missing data
• Missing values
• Interactive
visualizations
• Mapping
• Making data
actionable
Find &
manage data
Analyze data
Get data
used

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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 of disease study at subnational level
• Identify best practices for managing geographies
(national, subnational) globally over time
• Is there a portal for gridded data?
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