Associate Professor of Anthropology Dr. Heather Howard co-publishes an article in the Epidemiologic Reviews

Associate Professor of Anthropology Dr. Heather Howard co-publishes an article in Epidemiologic Reviews with Danielle Gartner (Assistant Professor of Epidemiology and Biostatistics, MSU, primary author), Madeline Nash (Doctoral Student, Sociology, MSU) and Ceco Maples (Undergraduate Major in Anthropology, MSU). The review article is titled, Misracialization of Indigenous People in Population Health and Mortality Studies: A Scoping Review to Establish Promising Practices. This review identifies four primary limitations of approaches used in population health research that misracialize or misclassify indigenous people and offers promising practices to consider. 

Read the full article at:

ABSTRACT: Indigenous people are often misracialized or misclassified as other racial or ethnic identities in population health research. This misclassification leads to underestimation of Indigenous-specific mortality and health metrics, and subsequently, inadequate resource allocation. In recognition of this problem, investigators around the world have devised analytic methods to address racial misclassification of Indigenous people. We carried out a scoping review based on searches in PubMed, Web of Science, and the Native Health Database for empirical studies published after 2000 that include Indigenous-specific estimates of health or mortality and that take analytic steps to rectify racial misclassification of Indigenous people. We then considered the weaknesses and strengths of implemented analytic approaches, with a focus on methods used in the United States (U.S.) context. To do this, we extracted information from 97 articles and compared the analytic approaches used. The most common approach to address Indigenous misclassification is to use data linkage, though other methods include geographic restriction to areas where misclassification is less common, exclusion of some subgroups, imputation, aggregation, and electronic health record abstraction. We identified four primary limitations of these approaches: (1) combining data sources that use inconsistent process and/or sources of race and ethnicity information, (2) conflating race, ethnicity, and nationality, (3) applying insufficient algorithms to bridge, impute, or link race and ethnicity information, and (4) assuming the hyperlocality of Indigenous people. While there is no perfect solution to the issue of Indigenous misclassification in population-based studies, a review of this literature provided promising practices to consider.