Abstract
Purpose. 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/Expected 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.
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/Expected 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.
Original language | American English |
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Pages | 60 |
State | Published - 13 Sep 2024 |
Event | 2024 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 2024 → 13 Sep 2024 |
Conference
Conference | 2024 Symposium on Tribal and Rural Innovations in Disparities and Equity for Health |
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Abbreviated title | STRIDE 2024 |
Country/Territory | United States |
City | Tahlequah |
Period | 13/09/24 → 13/09/24 |