Effect of race and ethnicity classification on survey estimates: Anomaly of the weighted totals of American Indians and Alaska Natives

Misracialization of Indigenous people in population health and mortality studies: a scoping review to establish promising practices

Indigenous people are often misracialized 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 US 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; 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 4 primary limitations of these approaches: (1) combining data sources that use inconsistent processes 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. Although there is no perfect solution to the issue of Indigenous misclassification in population-based studies, a review of this literature provided information on promising practices to consider.

MEASURING EVERYDAY RACIAL/ETHNIC DISCRIMINATION IN HEALTH SURVEYS: How Best to Ask the Questions, in One or Two Stages, Across Multiple Racial/Ethnic Groups?

While it is clear that self-reported racial/ethnic discrimination is related to illness, there are challenges in measuring self-reported discrimination or unfair treatment. In the present study, we evaluate the psychometric properties of a self-reported instrument across racial/ ethnic groups in a population-based sample, and we test and interpret findings from applying two different widely-used approaches to asking about discrimination and unfair treatment. Even though we found that the subset of items we tested tap into a single underlying concept, we also found that different groups are more likely to report on different aspects of discrimination. Whether race is mentioned in the survey question affects both frequency and mean scores of reports of racial/ethnic discrimination. Our findings suggest caution to researchers when comparing studies that have used different approaches to measure racial/ethnic discrimination and allow us to suggest practical empirical guidelines for measuring and analyzing racial/ethnic discrimination. No less important, we have developed a self-reported measure of recent racial/ethnic discrimination that functions well in a range of different racial/ethnic groups and makes it possible to compare how racial/ethnic discrimination is associated with health disparities among multiple racial/ethnic groups.