Hidden statistics: Examining the obesity epidemic across American Indians and Alaska Natives using self-reported identity compared to imputed racial categories

Introduction/Objectives: To identify obesity rates among high school students who identify solely as non-Hispanic, American Indian/Alaska Native (AI/AN) in comparison to a disaggregated approach that includes all youth identifying as AI/AN—alone or in combination with other ethnoracial groups using the 2021 Youth Risk Behavior Surveillance System (YRBSS).

Design Methods: We conducted a cross-sectional analysis of the Youth Risk Behavior Surveillance System (YRBSS) to assess obesity rates among high school students in the United States, self-reporting as AI/AN alone or in combination, compared to the imputed raceeth variable in YRBSS.

Results: According to the imputed raceeth variable, 119 high school students were classified as AI/AN only, with 30 being classified as obese (30; 29.43%). In contrast, 664 participants identified as AI/AN alone or in combination with other racial groups, with 149 students classified as obese (149; 22.11%). The self-report data yielded a total of 128 AI/AN-only high school students, with 31 students being classified as obese (31; 25.7%). Obesity rates varied among the other AI/AN subgroups: AI/AN and White/Caucasian (15.23%), AI/AN and Black (21.72%), AI/AN alone with Hispanic/Latino ethnicity (23.52%), or AI/AN in combination with 1 or more race (24.25%).

Discussion/Conclusion: Disaggregation of ethnic groups into smaller subgroups by allowing individuals to self-report ethnoracial status limits bias and provides a more accurate dataset. Accurate data representation is crucial for adequately reporting obesity and other metabolic disorders in conjunction with race/ethnicity in medicine. Classifying AI/AN populations as non-Hispanic/Latino single-race limits the population size and hinders the amount of public resources that are allocated towards AI/AN health and well-being.