Quantifying structural racism in cohort studies to advance prospective evidence

Shawna Follis; Khadijah Breathett; Lorena Garcia; Monik Jimenez; Crystal W Cené; Eric Whitsel; Haley Hedlin; Electra D Paskett; Shiqi Zhang; Cynthia A Thomson; Marcia L Stefanick

doi:10.1016/j.ssmph.2023.101417

Quantifying structural racism in cohort studies to advance prospective evidence

SSM Popul Health. 2023 Apr 28:22:101417. doi: 10.1016/j.ssmph.2023.101417. eCollection 2023 Jun.

Authors

Affiliations

¹ Stanford Prevention Research Center, Department of Medicine, Stanford University, 3180 Porter Drive, Mail Code 5702, Palo Alto, CA, 94304, USA.
² Division of Cardiovascular Medicine, Indiana University, Indianapolis, IN, USA.
³ UC Davis School of Medicine, Department of Public Health Sciences, Davis, CA, USA.
⁴ Division of Women's Health and Division of Preventive Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
⁵ University of California, San Diego Department of Medicine, San Diego, CA, USA.
⁶ Department of Epidemiology, Gillings School of Global Public Health and Department of Medicine, School of Medicine, University of North Carolina, Chapel Hill, NC, USA.
⁷ Quantitative Sciences Unit, Department of Medicine, Stanford University School of Medicine, CA, USA.
⁸ College of Medicine, The Ohio State University, Columbus, OH, USA.
⁹ Health Promotion Sciences, Mel & Enid Zuckerman College of Public Health, University of Arizona, Tucson, AZ, USA.
¹⁰ Department of Obstetrics & Gynecology and Department of Epidemiology and Population Health, Stanford University, Stanford, CA, USA.

Abstract

Calls-to-action in health research have described a need to improve research on race, ethnicity, and structural racism. Well-established cohort studies typically lack access to novel structural and social determinants of health (SSDOH) or precise race and ethnicity categorization, contributing to a loss of rigor to conduct informative analyses and a gap in prospective evidence on the role of structural racism in health outcomes. We propose and implement methods that prospective cohort studies can use to begin to rectify this, using the Women's Health Initiative (WHI) cohort as a case study. To do so, we evaluated the quality, precision, and representativeness of race, ethnicity, and SSDOH data compared with the target US population and operationalized methods to quantify structural determinants in cohort studies. Harmonizing racial and ethnic categorization to the current standards set by the Office of Management and Budget improved measurement precision, aligned with published recommendations, disaggregated groups, decreased missing data, and decreased participants reporting "some other race". Disaggregation revealed sub-group disparities in SSDOH, including a greater proportion of Black-Latina (35.2%) and AIAN-Latina (33.3%) WHI participants with income below the US median compared with White-Latina (42.5%) participants. We found similarities in the racial and ethnic patterning of SSDOH disparities between WHI and US women but less disparity overall in WHI. Despite higher individual-level advantage in WHI, racial disparities in neighborhood resources were similar to the US, reflecting structural racism. Median neighborhood income was comparable between Black WHI ($39,000) and US ($34,700) women. WHI SSDOH-associated outcomes may be generalizable on the basis of comparing across race and ethnicity but may quantitatively (but not qualitatively) underestimate US effect sizes. This paper takes steps towards data justice by implementing methods to make visible hidden health disparity groups and operationalizing structural-level determinants in prospective cohort studies, a first step to establishing causality in health disparities research.

Keywords: Cohort study; Disparities; Ethnicity; Health; Office of management and budget; Race; Social determinants of health; Social epidemiology; Structural racism.