Overview of the Global Burden of Disease study along with 8 key insights from turning 50K data sources into comparable measurements of health loss by country, age and sex. Insights range from finding, managing, and wrangling/prepping to analyzing and visualizing the results.

1. Population health measurement
Key insights from the Global Burden of Disease Study
Peter Speyer
Chief Data & Technology Officer
speyer@uw.edu @peterspeyer

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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
• 230 faculty, researchers and staff
• Providing rigorous, scientific measurement
– What are the world’s major health problems?
– How well is society addressing these problems?
– How should we best dedicate resources to improving
health?
Goal:
improve health
by providing the best
information
on population health

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Measuring population health: Global Burden of Disease
• A systematic scientific effort
to quantify the comparative magnitude of
health loss due to diseases, injuries & risk factors
• Applied to different populations
– Administrative: global, regional, country, sub-national
(e.g. LA county, Shilin county, Paris)
– Any other population (e.g. General Motors employees)
• GBD 2013 currently being published in The Lancet
– Scope: 300 diseases, 75 risk factors, 188 countries, 1990-2013

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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

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Insight #1: increasing opportunities to access, collate and
synthesize data from many sources
• Vital registration
• Censuses
• Surveys
• Disease
registries
• Surveillance
systems
Population based Encounter level Other
• Medical records
(primary care,
ambulatory,
hospital)
• Pharmacy
records
• Claims data
• Activity trackers
• Digital health
• Satellite data
• Other sensor
data
Web / catalog
searches
Lit reviews
Collaboration
DUA/
purchases

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Access at www.ghdx.org

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Insight #2: maps and cross-walks are crucial to make use of data
from different sources

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Insight #3: fixing data quality issues requires a combination of
subject matter expertise, statistical models, and lots of data
% Garbage codes from VR, latest year 1980-2013

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Insight #4: track data provenance by linking research results to
input data points

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Insight #5: powerful computation makes innovative modeling
approaches possible
• Cause of Death Ensemble Modeling (CODEm)
– Create wide range of plausible models and relevant combination of models
– Test predictive validity by using 75% of data to predict remaining 25% (1000 iterations)
– Apply best model / combination of models to full dataset
• Disease Modeling with Bayesian meta-regression (DisMod)
– Use prevalence, incidence, case fatality and other data as input to
– Model from global to sub-national level
• Microsimulations
– Estimate burden of co-morbidities

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Insight #6: discuss data, methods and results directly with data
providers and stakeholders
Collaborators in GBD 2013: 1050 experts, 108 countries

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Insight #7: more detailed data and powerful computation create
huge opportunities for better geo-spatial resolution
DALY burden of all cancers; source: GBD 2010

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Insight #7: more detailed data and better computation create
huge opportunities for better geo-spatial resolution

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Insight #7: more detailed data and better computation create
huge opportunities for better geo-spatial resolution

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Insight #8: to create impact with data, get the right data
to the right audiences in the right format
THE
RIGHT
DATA
THE
RIGHT
AUDIENCES
THE
RIGHT
FORMAT
THE
RIGHT
TIME
THE QUESTION

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Insight #8: to create impact, get the right data
to the right audiences in the right format
Audience Attention
span
Data
skills
Domain
expertise
Impact
Casual user Low Low Low Low
Data actor Low Varied Varied High
Data analyst Medium High High Medium
Researcher High High High Medium

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30. Peter Speyer
Chief Data & Technology Officer
speyer@uw.edu @peterspeyer
www.healthdata.org | www.peterspeyer.org