Structural racism has long been a topic of interest in the field of public health, with researchers delving into the laws, policies, practices, and norms that contribute to unequal access to resources and opportunities, particularly in healthcare. Over the years, the study of structural racism has evolved from mere commentary to data-driven research, shedding light on its real-world impact.
A recent comprehensive review of empirical studies on structural racism has revealed two critical limitations in the way most scholars approach the use of area-based data. The study, led by Kristi L. Allgood, Ph.D., a social epidemiologist at Texas A&M University School of Public Health, along with researchers from Tufts University, the University of Michigan, and the University of California-Irvine, was published in the American Journal of Epidemiology.
The researchers focused on federal policy domains outlined in the 1968 Kerner Report and its 50th-anniversary update, identifying relevant federal policies and existing indicators of structural racism in the literature. They found that while many empirical studies rely on a limited set of area-based measures, such as comparing educational attainment between Black and white residents in a specific county, there is room for improvement.
Allgood suggests that researchers can enhance area-based measures by explicitly linking these indicators to racist policies and using a wider range of indicators. By establishing a clear connection between area-level racial inequities and racist policies, researchers can pinpoint areas for intervention more effectively. Additionally, expanding the range of indicators considered beyond traditional sources like the Decennial Census and American Community Survey can provide a more comprehensive understanding of racial health inequities.
To assist researchers in addressing these limitations, the team provided practical tools for research, including a table of discriminatory federal policies, a list of common and novel indicators of structural racism across multiple domains, and an example illustrating how to connect policies and indicators of structural racism. By incorporating a variety of domains, researchers can better capture the multi-dimensional nature of structural racism and its impact on health disparities.
Allgood views the paper as a starting point for examining the various domains of structural racism over time and understanding the impact of co-occurring policies on racial health inequities. The hope is that this work will inform future research aimed at countering and rectifying the historical discriminatory effects of past policies.
In conclusion, the study highlights the importance of expanding the use of area-based data and indicators in research on structural racism to gain a more nuanced understanding of its impact on health outcomes. By addressing these limitations and utilizing a broader range of indicators, researchers can better inform interventions to combat racial health inequities and promote health equity for all.
