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Vital Records: Vital input for population health measurement

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  • 1. Vital Records: Vital input for population health measurement Peter Speyer Chief Data & Technology Officer speyer@uw.edu / @peterspeyer
  • 2. 2www.healthdata.org Overview • IHME • Global Burden of Disease (GBD) • Vital records in GBD • Data visualizations • GBD results • Outlook
  • 3. 3www.healthdata.org Institute for Health Metrics and Evaluation (IHME) • Independent research center at the University of Washington • Core funding by Bill & Melinda Gates Foundation and state of Washington • 190 faculty, researchers, and staff • Providing independent, rigorous, and scientific measurement and evaluations – What are the world’s major health problems? – How well is society addressing these problems? – How do we best dedicate resources to get the maximum impact in improving population health in the future? • “Our goal is to improve the health of the world’s populations by providing the best information on population health”
  • 4. 4www.healthdata.org Demo: US Health Map (LE in US, females, 2010)
  • 5. 5www.healthdata.org The Global Burden of Disease Study • A systematic, scientific effort to quantify the comparative magnitude of health loss due to diseases, injuries & risk factors • GBD 2010 published in The Lancet in 2012 • GBD 2013 published in 2014 – 323 diseases and injuries, 1,501 sequelae, 69 risk factors – 188 countries, 1990 to 2013 – Findings published in major medical journals, policy reports, data visualizations
  • 6. 6www.healthdata.org GBD collaborative model 1,050 experts, 106 countries
  • 7. 7www.healthdata.org Measuring burden of diseases and injuries DALYs (Disability-Adjusted Life Years) Health AgeDeath Deaths Best life expectancy YLLs YLLs (Years of Life Lost) YLDs YLDs YLDs (Years Lived with Disability) Disability Weight
  • 8. 8www.healthdata.org GBD data inputs •Vital registration •Censuses •Surveys •Verbal autopsy •Disease registries •Surveillance systems Population-based Encounter-level Other •Hospital records •Ambulatory records •Primary care records •Claims data •Literature reviews •Sensor data •Mortuaries/burial sites •Police records
  • 9. 9www.healthdata.org The Global Health Data Exchange (GHDx.org)
  • 10. 10www.healthdata.org GHDx: search term NCHS
  • 11. 11www.healthdata.org A GHDx record
  • 12. 12www.healthdata.org Data & Model Flow Mortality 2 Causes of death 3 Nonfatal health outcomes 4 Risk factors 5 Co- variates 1 YLLs/ YLDs/ DALYs 6
  • 13. 13www.healthdata.org Vital records in GBD • Mortality • Preparing data for Causes of Death analysis • Causes of Death Ensemble Modeling (CODEm) • CodCorrect • Results
  • 14. 14www.healthdata.org Demo: Mortality Visualization
  • 15. 15www.healthdata.org Causes of death data: 600M deaths back to 1980 Type Site years Coun- tries Vital registration 2,798 130 Verbal autopsy 486 66 Cancer registries 2,715 93 Police reports 1,129 122 Surveys/ census 1,564 82 Maternal mortality surveillance 83 8 Deaths in health facilities 21 9 Burial and mortuary 32 11
  • 16. 16www.healthdata.org Garbage codes in VR data, most recent year, 1980-2013
  • 17. 17www.healthdata.org US garbage codes, 1982
  • 18. 18www.healthdata.org US garbage codes, 2010
  • 19. 19www.healthdata.org US garbage codes, change, 1982 to 2010
  • 20. 20www.healthdata.org Change in garbage codes, 1982-2010
  • 21. 21www.healthdata.org Garbage codes (percent of deaths) 0.0% 5.0% 10.0% 15.0% 20.0% 25.0% 30.0% 35.0% ENN LNN PNN 1 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 symptoms, signs and abnormal findings unspecified cause or sequelae in each chapters (except Injuries) intermediate causes hypertension and atherosclerosis ill-defined and impossible causes of death immediate causes garbage codes in neoplasm chapters garbage code in Injury chapters
  • 22. 22www.healthdata.org Garbage code redistribution • Understanding disease classification • Pathology/ epidemiology • Lit review • Multiple causes of death data • Hospital data
  • 23. 23www.healthdata.org Garbage code redistribution • Understanding disease classification • Pathology/ epidemiology • Lit review • Multiple causes of death data • Hospital data
  • 24. 24www.healthdata.org Garbage codes: summary • US is doing very well in international comparison • Active role in discouraging use of garbage codes • Consistency: maternal mortality increase in US (pregnancy check-box on some states’ death certificates) • Methods available to correct for garbage codes; working on software to provide to others
  • 25. 25www.healthdata.org Cause of Death Ensemble Modeling (CODEm) 1. Identify and prep all available data 2. Develop a diverse set of plausible models for each cause – Different types: negative binomial, fixed proportion, natural history, etc. – Different (sets of) covariates 3. Assess predictive validity of each individual model and each ensemble of models via out-of-sample test 4. Use best performing model/ensemble for analysis
  • 26. 26www.healthdata.org CodCorrect • Ensure that cause-specific deaths fit all-cause mortality envelopes • Key advantage of looking at all causes at once in GBD • Implemented taking into account uncertainty in every cause of death model • Applied at all hierarchical levels
  • 27. 27www.healthdata.org
  • 28. 28www.healthdata.org Visualizing results • Vetting input data • Reviewing results • Collaborating with experts • Communicating results Simple visualizations Google Motion Charts Viz platforms Custom coding Static graphs
  • 29. 29www.healthdata.org Communicating Data for Impact • Audiences and characteristics – Casual user – Data actor – Data analyst – Researcher • Granularity of data • Type of tool or visual http://bit.ly/1mogRom
  • 30. 30www.healthdata.org Leading causes of YLLs, 2010, both sexes
  • 31. 31www.healthdata.org Demo: GBD Cause Patterns & GBD Compare
  • 32. 32www.healthdata.org Strengths of the GBD approach • Synthesis of all available data • Innovative, peer reviewed methods • Consistent methods make results comparable • Uncertainty bounds for all metrics • Coverage of all causes prevents double-counting, e.g., mortality, anemia • Fully imputed dataset
  • 33. 33www.healthdata.org Looking ahead: US burden by county • Successful collaborations with UK, China, Mexico • Extend US burden to subnational level – All counties – Sub-county for large counties – Objective: entities smaller than 100K people • Starting with Causes of Death by county • Funding discussions for proof of concept with RWJF (10-20 counties)
  • 34. 34www.healthdata.org US burden by county: access to data • Issues with some data at the county/sub-county level – Access only at state or county level – Masking at county level – Access via RDC • IHME data security – Servers owned and operated, not shared – Access control by individual for Limited Use folders – Secure room – Data use agreements
  • 35. 35www.healthdata.org US burden by county: collaboration • Expert collaboration like GBD Global – Discussion of input data – Review of preliminary results – Joint outreach – Collaboration at state and county level • Visualizations • Trainings
  • 36. 36www.healthdata.org Summary • Fantastic data work in the US at the county, state, and national levels • Great progress over the past 30 years in quality of VR • There can never be enough data • Looking forward to collaborations on US burden and more Contact me: Peter Speyer speyer@uw.edu @peterspeyer
  • 37. Vital Records: Vital input for population health measurement Peter Speyer speyer@uw.edu @peterspeyer

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