Big Data in Health: The Global Burden of Disease Study
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Transcript of Big Data in Health: The Global Burden of Disease Study
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Big Data in Health:The Global Burden of Disease Study
Peter Speyer@peterspeyerMarch 29, 2014
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
From data to impact
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Big data
Audiences
1. Data access2. Data
preparation3. Data analysis4. Data translation
Getting relevant datain useful formats
to the right audiences
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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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
6Tristan Schmurr / Flickr
CC Chapman / Flickr
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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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
4. Data translation
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• Academic papers
• Policy reports
• Data search engine
• Data visualizations– Input data
– Comprehensive results
– Key insights
Visualization demos
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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
@peterspeyer
http://healthdatainnovation.org
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