Health equity proponents are buzzing over today’s publication of research studying health disparities by social and other categories stratified to a deeper degree than race and ethnicity. In exploring country of birth and preferred language groupings, many diverse categories of individuals were identified whose susceptibility to health inequities and poor health outcomes otherwise would be reviewed only through racial and ethnic lenses.
A significant takeaway from this study is the notion that potential shortcomings exist in analyzing health equity through traditional broad categories, such as ethnicity and race, which might mask health needs of certain more vulnerable populations and, at the same time, might prescribe heightened risk to individuals possessing less risk. The study’s results suggest that reviewing more demographically relevant data can lead to enhanced knowledge of health outcomes of populations that, in turn, can better inform heath policy and interventions most appropriate for distinct groups.
This study specifically analyzed data from patients of a large Midwestern health system between January 1, 2019, and July 31, 2021, in connection with COVID-19 related treatment. Importantly, the research incorporated not only the patients’ race and ethnicity, but also their countries of birth, preferred language, and multiple other segmentations.
Conclusions from the study included the result that stratifications by individuals’ preferred language and country of birth exhibited certain health impacts of COVID-19 that otherwise would not have been detected in the same manner in which outcomes would be understood by looking only at the more traditional categorizations of ethnicity and race.
While additional research would be informative in better understanding how different, less traditional, segmentation of patient data might impact health equity research, this study demonstrates that the collection of two divergent variables may better identify patients’ risk to COVID-19 than an analysis looking only at broader data like race and ethnicity. Moreover, a similar approach could be applied to other health and medical conditions to similarly better prescribe risk to patients across different segmentation continuums.
Today’s publication can be found here.
