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Research Article

Precision medicine

Respecting Autonomy And Enabling Diversity: The Effect Of Eligibility And Enrollment On Research Data Demographics

Affiliations
  1. Kayte Spector-Bagdady is an assistant professor of obstetrics and gynecology and an associate director of the Center for Bioethics and Social Sciences in Medicine at the University of Michigan Medical School, in Ann Arbor, Michigan. Spector-Bagdady, Shengpu Tang, and Sarah Jabbour are co–first authors.
  2. Shengpu Tang is a PhD candidate in computer science and engineering at the University of Michigan, in Ann Arbor, Michigan.
  3. Sarah Jabbour is a PhD candidate in computer science and engineering at the University of Michigan.
  4. W. Nicholson Price II is a professor of law at the University of Michigan Law School, in Ann Arbor, Michigan.
  5. Ana Bracic is an assistant professor of political science and a member of the Minority Politics Initiative at Michigan State University, in East Lansing, Michigan.
  6. Melissa S. Creary is an assistant professor of health management and policy at the University of Michigan School of Public Health, in Ann Arbor, Michigan, and the senior director for the Office of Public Health Initiatives at the American Thrombosis and Hemostasis Network (ATHN), in Rochester, New York.
  7. Sachin Kheterpal is a professor of anesthesiology and the associate dean for research information technology at the University of Michigan Medical School.
  8. Chad M. Brummett is a professor of anesthesiology and senior associate chair for research at the University of Michigan Medical School.
  9. Jenna Wiens ([email protected]) is an associate professor of computer science and engineering, associate director of the Artificial Intelligence Lab, and codirector for Precision Health at the University of Michigan.
PUBLISHED:No Accesshttps://doi.org/10.1377/hlthaff.2021.01197

Many promising advances in precision health and other Big Data research rely on large data sets to analyze correlations among genetic variants, behavior, environment, and outcomes to improve population health. But these data sets are generally populated with demographically homogeneous cohorts. We conducted a retrospective cohort study of patients at a major academic medical center during 2012–19 to explore how recruitment and enrollment approaches affected the demographic diversity of participants in its research biospecimen and data bank. We found that compared with the overall clinical population, patients who consented to enroll in the research data bank were significantly less diverse in terms of age, sex, race, ethnicity, and socioeconomic status. Compared with patients who were recruited for the data bank, patients who enrolled were younger and less likely to be Black or African American, Asian, or Hispanic. The overall demographic diversity of the data bank was affected as much (and in some cases more) by which patients were considered eligible for recruitment as by which patients consented to enroll. Our work underscores the need for systemic commitment to diversify data banks so that different communities can benefit from research.

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