vital records: vital input for population health measurement
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Vital Records:
Vital input for population health measurement
Peter Speyer
Chief Data & Technology Officer
[email protected] / @peterspeyer
2www.healthdata.org
Overview
• IHME
• Global Burden of Disease (GBD)
• Vital records in GBD
• Data visualizations
• GBD results
• Outlook
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’spopulations by providing the best information on population health”
4www.healthdata.org
Demo: US Health Map (LE in US, females, 2010)
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
6www.healthdata.org
GBD collaborative model
1,050 experts, 106 countries
7www.healthdata.org
Measuring burden of diseases and injuries
DALYs (Disability-Adjusted Life Years)
Health
AgeDeath
Deaths
Bestlife
expectancy
YLLsYLLs (Years of Life Lost)
YLDs YLDs
YLDs (Years Lived with Disability)
Disability Weight
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
10www.healthdata.org
GHDx: search term NCHS
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A GHDx record
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Data & Model Flow
Mortality
2Causes of death
3
Nonfatal health
outcomes
4Risk
factors
5Co-
variates
1
YLLs/ YLDs/ DALYs
6
13www.healthdata.org
Vital records in GBD
• Mortality
• Preparing data for Causes of Death analysis
• Causes of Death Ensemble Modeling (CODEm)
• CodCorrect
• Results
14www.healthdata.org
Demo: Mortality Visualization
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Causes of death data: 600M deaths back to 1980Type 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
16www.healthdata.org
Garbage codes in VR data, most recent year, 1980-2013
17www.healthdata.org
US garbage codes, 1982
18www.healthdata.org
US garbage codes, 2010
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US garbage codes, change, 1982 to 2010
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Change in garbage codes, 1982-2010
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Garbage codes (percent of deaths)
ENN LNN PNN 1 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 800.0%
5.0%
10.0%
15.0%
20.0%
25.0%
30.0%
35.0%
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
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Garbage code redistribution
• Understanding disease classification
• Pathology/ epidemiology
• Lit review
• Multiple causes of death data
• Hospital data
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Garbage code redistribution
• Understanding disease classification
• Pathology/ epidemiology
• Lit review
• Multiple causes of death data
• Hospital data
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
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
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
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Visualizing results
• Vetting input data
• Reviewing results
• Collaborating with
experts
• Communicating results
Simple visualizations
Google Motion Charts
Viz platforms
Custom coding
Static graphs
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
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Leading causes of YLLs, 2010, both sexes
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Demo: GBD Cause Patterns & GBD Compare
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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 preventsdouble-counting, e.g., mortality, anemia
• Fully imputed dataset
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)
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
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
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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
@peterspeyer
