Date post: | 20-Aug-2015 |
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UNIVERSITY OF WASHINGTON
Using maps and spatial analysis
to inform global health decision making
Peter Speyer
Director 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’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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