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Big Data in Global Health: Steps to get data to audiences
Big Data in Global Health: Steps to get data to audiences
Big Data in Global Health: Steps to get data to audiences
Big Data in Global Health: Steps to get data to audiences
Big Data in Global Health: Steps to get data to audiences
Big Data in Global Health: Steps to get data to audiences
Big Data in Global Health: Steps to get data to audiences
Big Data in Global Health: Steps to get data to audiences
Big Data in Global Health: Steps to get data to audiences
Big Data in Global Health: Steps to get data to audiences
Big Data in Global Health: Steps to get data to audiences
Big Data in Global Health: Steps to get data to audiences
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Big Data in Global Health: Steps to get data to audiences

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  • 1. 1 Big Data in Health: The Global Burden of Disease Study Peter Speyer @peterspeyer March 29, 2014
  • 2. Big data in health 2 • 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 3
  • 4. From data to impact 4 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 5
  • 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 6 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 7
  • 8. Demo: Tobacco Viz 8
  • 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 10 • Academic papers • Policy reports • Data search engine • Data visualizations – Input data – Comprehensive results – Key insights
  • 11. Visualization demos 11
  • 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 12

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