UNIVERSITY OF WASHINGTON

Using maps and spatial analysis

to inform global health decision making

Peter Speyer

Director of Data Development

@peterspeyer / speyer@uw.edu

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’spopulations by providing the best information on population health”

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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21 GBD regions

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Measuring burden of diseases and injuries

DALYs (Disability-Adjusted Life Years)

Health

Age

Death

Deaths

Averagelife

expectancy

YLLsYLLs (Years of Life Lost)

YLDs YLDs

YLDs (Years Lived with Disability)

Disability Weight

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

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

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

Global Health Data Exchange(http://www.ghdx.org)

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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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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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• Show GBD Compare map for risk factors– Ambient air pollution

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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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Add graph from COD Viz

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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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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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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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