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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

IHME input data cataloged in the GHDx

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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

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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Demo: Tobacco Viz

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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

speyer@uw.edu

@peterspeyer

http://healthdatainnovation.org

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