Objectives. To determine the generalizability of crowdsourced, electronic health data from self-selected individuals using a national survey as a reference.
Methods. Using the world’s largest crowdsourcing platform in 2015, we collected data on characteristics known to influence cardiovascular disease risk and identified comparable data from the 2013 Behavioral Risk Factor Surveillance System. We used age-stratified logistic regression models to identify differences among groups.
Results. Crowdsourced respondents were younger, more likely to be non-Hispanic and White, and had higher educational attainment. Those aged 40 to 59 years were similar to US adults in the rates of smoking, diabetes, hypertension, and hyperlipidemia. Those aged 18 to 39 years were less similar, whereas those aged 60 to 75 years were underrepresented among crowdsourced respondents.
Conclusions. Crowdsourced health data might be most generalizable to adults aged 40 to 59 years, but studies of younger or older populations, racial and ethnic minorities, or those with lower educational attainment should approach crowdsourced data with caution.
Public Health Implications. Policymakers, the national Precision Medicine Initiative, and others planning to use crowdsourced data should take explicit steps to define and address anticipated underrepresentation by important population subgroups.
- Veronica Yank and Sanjhavi Agarwal are with the Division of General Internal Medicine, University of California, San Francisco. Pooja Loftus is with the Division of General Medical Disciplines, Stanford University, Stanford, CA. Steven Asch is with the VA Palo Alto Health Care System, Palo Alto, CA, and the Division of General Medical Disciplines, Stanford University. David Rehkopf is with the Division of General Medical Disciplines, Stanford University.
