Using maps and spatial analysis to inform global health decision making

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Using maps and spatial analysis to inform global health decision making

  1. 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. 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. 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 3
  4. 4. 21 GBD regions 4
  5. 5. Measuring burden of diseases and injuries 5 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. 6. GBD process & spatial challenges • Standards • Coverage • Representa- tiveness • Geographies over time 6 • Missing data • Missing values • Interactive visualizations • Mapping • Making data actionable Find & manage data Analyze data Get data used
  7. 7. GBD process & spatial challenges • Standards • Coverage • Representa- tiveness • Geographies over time 7 • Missing data • Missing values • Interactive visualizations • Mapping • Making data actionable Find & manage data Analyze data Get data used
  8. 8. Data inputs 8 •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. 9. Global Health Data Exchange (http://www.ghdx.org) 9
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  14. 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. 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) 16
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  18. 18. • Show GBD Compare map for risk factors – Ambient air pollution 18
  19. 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. 20. Add graph from COD Viz 20
  21. 21. GBD process & spatial challenges • Standards • Coverage • Representa- tiveness • Geographies over time 21 • Missing data • Missing values • Interactive visualizations • Mapping • Making data actionable Find & manage data Analyze data Get data used
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  27. 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. 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? 34

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