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.

When public health considers the health and disease status of Indigenous people, it often does so using a racial lens. In recent decades, public health researchers have begun to acknowledge that commonly employed racial categories represent history, power dynamics, embodiment, and legacies of discrimination and racism, rather than innate biology. Even so, public health has not yet fully embraced an understanding of other components of identity formation for Indigenous people, including political status within Native nations. In this article, we discuss why the continued racial conceptualization of Indigeneity in US public health is inadequate. We begin by providing a brief account of racialization as a tool of colonization, of failure to recognize and acknowledge Indigenous sovereignty, and of common public health practices of Indigenous data collection and interpretation. We then articulate the stakes of racialized health data for Native communities. We end by offering alternative approaches, many drawn from scholarship from Indigenous researchers. (Am J Public Health. 2021;111(11):1969–1975. https://doi.org/10.2105/AJPH.2021.306465 )

Background: We hypothesize that the Affordable Care Act’s (ACA) Medicaid expansion, which extended health insurance coverage to preconception, between-conception, and postconception periods for women meeting income eligibility guidelines, impacted the number of live births in the United States by increasing access to contraception and financial well-being. These impacts may differ by maternal socioeconomic and demographic characteristics. Methods: Using data from birth certificates aggregated to the state–year level and a difference-in-differences design, we estimated the association between Medicaid expansion and count of live births. We also examined whether associations differed by socioeconomic and demographic characteristics. Results: Overall, Medicaid expansion was not meaningfully associated with the count of births (difference-in-differences ß = 0.002; 95% confidence interval [CI] = –0.010, 0.015). However, among certain groups, Medicaid expansion was associated with meaningful changes in the count of live births, though all confidence intervals included the null value. The estimate of the relation between Medicaid expansion and the count of live births was –0.025 (95% CI = –0.052, 0.001) for those ages 18–24 years; –0.078 (95% CI = –0.231, 0.075) for those who were married, and –0.035 (95% CI = –0.104, 0.034) for those who were unmarried. Conclusions: Despite its potential to impact live births, our results indicate that the ACA’s Medicaid expansion was not, in general, associated with live births of US residents of reproductive age. However, for younger, married, and unmarried women, the magnitude of estimates supports the hypothesis of a potentially meaningful effect of Medicaid expansions on live births.

One need not look far for an example of epidemiologic research where Indigenous people have either been excluded from analyses or have been aggregated with other racial and ethnic identities as an “Other.” Exclusion and aggregation of Indigenous peoples prevents us from adequately characterizing their health in ways that are useful for collective action. In this commentary we describe 3 distinct, yet related, issues underlying the relationships between statistical power ($\beta $) and structural and ideational power related to the “small sample size” problem for Indigenous peoples: (1) inadequate data procurement and management processes, (2) normative methodological practices, and (3) insufficient scientific communication. In the spirit of disciplinary reflection and self-critique, we identify and review the manifestation of these issues in 1 author’s previously published research. We then discuss and reemphasize important contributing historical and contemporary systems of injustice, and, finally, summarize existing promising research and analytic practices. Given that the tools that address the health of numerically large groups dominate teaching and research spaces, we must move toward a paradigm shift to fully provide equity, justice, and beneficence to Indigenous peoples and other “numerically small” groups.

Objective: To explore population‐level American Indian & Alaska Native‐White inequalities in cesarean birth incidence after accounting for differences in cesarean indication, age, and other individual‐level risk factors.Data Sources and Study Setting: We used birth certificate data inclusive of all live births within the United States between January 1 and December 31, 2017.Study Design: We calculated propensity score weights that simultaneously incorporate age, cesarean indication, and clinical and obstetric risk factors to estimate the American Indian and Alaska Native‐White inequality.Data Collection/Extraction Methods: Births to individuals identified as American Indian, Alaska Native, or White, and residing in one of the 50 US states or the District of Columbia were included. Births were excluded if missing maternal race/ethnicity or any other covariate.Principal Findings: After weighing the American Indian and Alaska Native obstetric population to be comparable to the distribution of cesarean indication, age, and clinical and obstetric risk factors of the White population, the cesarean incidence among American Indian and Alaska Natives increased to 33.4% (95% CI: 32.0–34.8), 3.2 percentage points (95% CI: 1.8–4.7) higher than the observed White incidence. After adjustment, cesarean birth incidence remained higher and increased in magnitude among American Indian and Alaska Natives in Robson groups 1 (low risk, primary), 6 (nulliparous, breech presentation), and 9 (transverse/oblique lie).Conclusions: The unadjusted lower cesarean birth incidence observed among American Indian and Alaska Native individuals compared to White individuals may be related to their younger mean age at birth. After adjusting for this demographic difference, we demonstrate that American Indian and Alaska Native individuals undergo cesarean birth more frequently than White individuals with similar risk profiles, particularly within the low‐risk Robson group 1 and those with non‐cephalic presentations (Robson groups 6 and 9). Racism and bias in clinical decision making, structural racism, colonialism, or other unidentified factors may contribute to this inequality.