Reconciling Racial Data for Inclusive Representation of American Indian and Alaska Native Mothers in PRAMS and Reassessment of Comorbidities

Kayleigh Noblin, Jasha Lyons Echo-Hawk, Amy Hendrix-Dicken, Ashton Gatewood, Micah Hartwell

Research output: Contribution to conferencePosterpeer-review

Abstract

Purpose: Previous research shows aggregated ethnoracial groupings often result in the misclassification or exclusion of American Indian and Alaska Native (AI/AN) individuals in health research. Given the reported high rates of comorbidities among AI/AN women, we sought to explore the prevalence of maternal comorbidities between aggregated AI/AN variables to a constructed, disaggregated variable that includes all AI/AN individuals.

Methods: We used PRAMS Phase 8 (2016-2021) data to explore inclusivity among included race variables — an aggregated, grouped variable (MAT_RACE_PU) that assigned a singular race to individuals which is most commonly used in published studies, an aggregated AI/AN variable (MRACE_AMI), as well as a disaggregated variable utilizing various race/ethnicity variables within the data. We compared reported prevalence rates of comorbidities between the imputed and constructed variables—with statistical significance determined through non-overlapping 95% confidence intervals and employing PRAMS’ survey design and sampling weights.

Results: Our study showed differences in AI/AN inclusion between all 3 variables. MAT_RACE_PU included 7,494 American Indians but no Alaska Natives. MRACE_AMI included 13,341—including both AI and AN individuals. The constructed variable had a sample size of 13,383—having 19 subgroups, including 42 enrolled tribal members who did not report AI/AN race. Significant differences in the prevalence of existing maternal comorbidities were found between the aggregated variables and the constructed variable subgroups for diabetes, hypertension, asthma, anemia, epilepsy, thyroid disease, polycystic ovarian syndrome, depression, and anxiety. For instance, the prevalence rate for diabetes showed 4.04% to 4.93% for aggregated AI variables, but the disaggregated subgroup AI (alone) was 5.46% and AN (alone) at 1.37%.

Conclusion: Our results highlight significant disparities in maternal comorbidities among AI/AN women when different racial classification strategies are employed. Disaggregating these data revealed differences that are crucial for understanding the unique health challenges faced by various subgroups.
Original languageAmerican English
Pages45
StatePublished - 13 Sep 2024
Event2024 Symposium on Tribal and Rural Innovations in Disparities and Equity for Health - Oklahoma State University College of Osteopathic Medicine at the Cherokee Nation, Tahlequah, United States
Duration: 13 Sep 202413 Sep 2024

Conference

Conference2024 Symposium on Tribal and Rural Innovations in Disparities and Equity for Health
Abbreviated titleSTRIDE 2024
Country/TerritoryUnited States
CityTahlequah
Period13/09/2413/09/24

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