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Big Data in Health:
The Global Burden of Disease Study
Peter Speyer
@peterspeyer
March 29, 2014

2. Big data in health
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•Surveys
•Censuses
•Disease registries
•Vital registration
•Verbal autopsy
•Mortuaries/
burial sites
•Police records
Variety Volume Velocity
•Hospital/
ambulatory/
primary care
records
•Claims data
•Surveillance
systems
•Administrative
data
•Literature
reviews
•Sensor data
•Social media
•Quantified self

3. IHME input data cataloged in the GHDx
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4. From data to impact
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Big data
Audiences
1. Data access
2. Data
preparation
3. Data analysis
4. Data translation
Getting relevant data
in useful formats
to the right audiences

5. Example: Global Burden of Disease Study
• A systematic, scientific effort to quantify the
comparative magnitude of health loss due to
diseases, injuries, and risk factors
• GBD 2010 results published in
The Lancet in 2012
– 291 causes, 67 risk factors
– 187 countries
– 1990-2010
– By age and sex
• GBD 2013 update in process
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6. 1. Accessing the data
• Systematic identification of all
relevant data sources
– Data indexers
– Lit reviews
• Challenges
– Data on paper, PDF, proprietary &
obsolete formats
– Patient/participant consent
– Confidentiality/de-identification
– Cost
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Tristan Schmurr / Flickr
CC Chapman / Flickr

7. 2. Preparing data for analysis
• Data extraction
(databases, tables,
papers)
• Analysis of
microdata
• Correction for bias
• Data quality issues,
e.g., garbage
codes
• Cross-walks, e.g.,
between ICDs
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8. Demo: Tobacco Viz
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9. 3. Analyzing data
• Use all available data
– Use covariates: indicators related to quantity of interest
• Test the modeling approach, e.g., predictive validity
testing (CODEm)
• Apply appropriate corrections, e.g., causes of death
to match all-cause mortality
• Quantify uncertainty
• Review: 1000+ experts, peer-reviewed publication

10. 4. Data translation
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• Academic papers
• Policy reports
• Data search
engine
• Data visualizations
– Input data
– Comprehensive
results
– Key insights

11. Visualization demos
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12. Parting thoughts
• Grab relevant data and get started
– Prep data with Data Wrangler, MS Power BI
– Visualize with Tableau Public, Google Fusion
– Learn to code: R, Python (analysis), JavaScript (viz)
• Check out IHME data sources and GHDx
Find resources & contact me at
speyer@uw.edu
@peterspeyer
http://healthdatainnovation.org
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