How do we assess a racial disparity in health? Distribution, interaction, and interpretation in epidemiological studies

Ann Epidemiol. 2019 Jan;29:1-7. doi: 10.1016/j.annepidem.2018.09.007. Epub 2018 Sep 29.


Identifying the exposures or interventions that exacerbate or ameliorate racial health disparities is one of the fundamental goals of social epidemiology. Introducing an interaction term between race and an exposure into a statistical model is commonly used in the epidemiologic literature to assess racial health disparities and the potential viability of a targeted health intervention. However, researchers may attribute too much authority to the interaction term and inadvertently ignore other salient information regarding the health disparity. In this article, we highlight empirical examples from the literature demonstrating limitations of overreliance on interaction terms in health disparities research; we further suggest approaches for moving beyond interaction terms when assessing these disparities. We promote a comprehensive framework of three guiding questions for disparity investigation, suggesting examination of the group-specific differences in (1) outcome prevalence, (2) exposure prevalence, and (3) effect size. Our framework allows for better assessment of meaningful differences in population health and the resulting implications for interventions, demonstrating that interaction terms alone do not provide sufficient means for determining how disparities arise. The widespread adoption of this more comprehensive approach has the potential to dramatically enhance understanding of the patterning of health and disease and the drivers of health disparities.

Keywords: Health disparities; Interaction; Interpretation; Modification; Race; Regression.

Publication types

  • Research Support, N.I.H., Extramural

MeSH terms

  • Disease Susceptibility / epidemiology
  • Environmental Exposure / statistics & numerical data*
  • Epidemiologic Studies
  • Health Services Accessibility / statistics & numerical data*
  • Health Status Disparities*
  • Healthcare Disparities*
  • Humans
  • Population Health
  • Prevalence
  • Race Factors*
  • Socioeconomic Factors*
  • United States