Geospatial Methods
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This document discusses how spatial analysis and mapping can inform global health decision making. It describes the Global Burden of Disease study which quantifies health loss from diseases, injuries, and risk factors in 187 countries. Spatial challenges include managing data from different geographies over time and addressing missing data. The Global Health Data Exchange provides access to health data. Maps of risk factors like air pollution are shown. Spatial regression models capture information over time, age, and space. Small area estimation is used to analyze health patterns at subnational levels with limited data. Remaining tasks involve adding more spatial covariates and conducting subnational burden studies.
Geospatial Methods
- 1. Using maps and spatial analysis to inform global health decision making Peter Speyer Director of Data Development @peterspeyer / speyer@uw.edu UNIVERSITY OF WASHINGTON
- 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 with • Almost 600 country, disease and risk factor experts from 80+ countries 3
- 5. Measuring burden of diseases and injuries Health Disability Weight YLDs YLDs Deaths YLLs (Years of Life Lost) YLDs (Years Lived with Disability) YLLs DALYs (Disability-Adjusted Life Years) Age Death Average life expectancy 5
- 6. GBD process & spatial challenges Find & manage data Analyze data • Standards • Missing data • Coverage • Missing values • Representativeness • Geographies over time Get data used • Interactive visualizations • Mapping • Making data actionable 6
- 7. GBD process & spatial challenges Find & manage data Analyze data • Standards • Missing data • Coverage • Missing values • Representativeness • Geographies over time Get data used • Interactive visualizations • Mapping • Making data actionable 7
- 8. Data inputs Population based • Surveys • Censuses • Vital registration • Verbal autopsy • Disease registries Encounter level • Hospital / ambulatory / primary care records • Claims data Other • Literature reviews • Sensor data • Mortuaries / burial sites • Police records • Surveillance systems 8
- 9. Global Health Data Exchange (http://www.ghdx.org) 9
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- 14. GBD process & spatial challenges Find & manage data Analyze data • Standards • Missing data • Coverage • Missing values • Representativeness • Geographies over time Get data used • Interactive visualizations • Mapping • Making data actionable 14
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- 16. 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) 16
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- 18. • Show GBD Compare map for risk factors – Ambient air pollution 18
- 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 19
- 20. Add graph from COD Viz 20
- 21. GBD process & spatial challenges Find & manage data Analyze data • Standards • Missing data • Coverage • Missing values • Representativeness • Geographies over time Get data used • Interactive visualizations • Mapping • Making data actionable 21
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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 27
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- 34. 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? 34