The way researchers contextualize race may color scientific understanding and inadvertently impede progress in addressing health inequities, according to an analysis from researchers in the UC Berkeley School of Public Health.
Amani M. Nuru-Jeter, and co-investigators from the Bloomberg School of Public Health at Johns Hopkins University and the Milken Institute School of Public Health at George Washington University, reviewed existing scientific research on health inequalities by race and socioeconomic position.
“Fundamental to our efforts to intervene and reduce racial and socioeconomic health inequalities is understanding that the causes of population health are not the same as the causes of health inequalities and health inequities,” Nuru-Jeter said.
An advanced version of the analysis was published in the January issue of the Annual Review of Public Health. It identifies the common approaches to studying race and socioeconomic position in studies of health inequalities, the methodological challenges associated with interpreting study findings, and the implications of those approaches and challenges for validity in epidemiologic studies, and public health research more broadly.
Chief among those is the manner in which researchers contextualize race. Public health research, the analysis found, is vulnerable to reinforcing and perpetuating health inequities due to widespread adoption of current interpretations of differences in race.
The analysis argues that “there is a need for philosophical rigor … in our efforts to contextualize and hence justify our research questions, methodological approaches, and interpretation of results,” concluding that causes of racial and socioeconomic health inequalities would need to be examined more carefully in order to reduce health inequalities.
“We’ve learned a lot over the years about how to improve population health, and we’ve done just that,” Nuru-Jeter said. “However, health inequalities persist. Addressing inequalities and inequities will require us to ask different kinds of questions and go outside of our disciplinary boundaries and think about the types of data, measurement strategies, and methods that are most appropriate for understanding root causes.”