The document discusses how the Institute for Health Metrics and Evaluation at the University of Washington uses maps and spatial analysis to inform global health decision making. It summarizes their work on the Global Burden of Disease study which quantifies health loss from diseases, injuries and risk factors in 187 countries. It describes how they measure and analyze data inputs, manage missing data, use covariates and risk factors, conduct spatial-temporal regression, and perform small area estimation to analyze health patterns at subnational levels. Remaining challenges include adding more spatial covariates and conducting disease burden studies at local levels.

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
14
• 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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