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Comparing Self-Reported and Aggregated Racial Classification for American Indian/Alaska Native Youths in YRBSS: 2021.

Ashton GatewoodAmy D Hendrix-DickenMicah L Hartwell
Published in: American journal of public health (2024)
Objectives. To identify how race and ethnicity were reclassified with survey variables for children self-reporting as American Indian/Alaska Native (AI/AN) using the 2021 Youth Risk Behavior Surveillance System (YRBSS). Methods. We conducted a cross-sectional analysis of the US Centers for Disease Control and Prevention's 2021 YRBSS. YRBSS collects behaviors and demographics of students in grades 9 through 12, including race and ethnicity via self-report, and then reclassifies data into a "raceeth" variable. To examine the classification of AI/AN in YRBSS, we compared AI/AN composition between self-report and raceeth variables. Results. A total of 816 adolescents self-reported as AI/AN alone (145; 17.70%), AI/AN alone with Hispanic/Latino background (246; 30.15%), or AI/AN in combination with 1 or more race (425; 52.08%). Of those, only 145 were classified as being AI/AN in the calculated raceeth variable. With YRBSS survey weighting, the percentage of AI/AN in the raceeth variable was 13.4%. Conclusions. Misclassification, noncollection, or the use of categories such as "other" and "multirace" without allowing disaggregation can misrepresent disease burden, morbidity, and mortality. Consequently, it is critical to disaggregate data to adequately capture race/ethnicity in self-report surveys and data sources. ( Am J Public Health. 2024;114(4):403-406. https://doi.org/10.2105/AJPH.2023.307561).
Keyphrases
  • artificial intelligence
  • big data
  • machine learning
  • public health
  • deep learning
  • young adults
  • electronic health record
  • cross sectional
  • physical activity
  • mental health
  • african american
  • data analysis
  • drug induced