Using maps and spatial analysis to inform global health decision making

UNIVERSITY OF WASHINGTON

Using maps and spatial analysisto inform global health decision-making

Peter SpeyerDirector of Data Development

@peterspeyer / [email protected]

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”

http://www.healthmetricsandevaluation.org/

The Global Burden of Disease Study

• A systematic, scientific effortto 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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21 GBD regions

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Measuring burden of diseases and injuries

DALYs (Disability-Adjusted Life Years)

Health

AgeDeath

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

dataAnalyze data Get data

used




over time

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actionable

Find & manage


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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over time

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actionable

Find & manage


used

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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• 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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over time

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actionable

Find & manage data Analyze data Get data used

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 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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Using maps and spatial analysis to inform global health decision making

Data & Analytics

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