Social determinants of obesity in American Indian and Alaska Native peoples aged ≥ 50 years
Bibliographic record
Abstract
ObjectiveAmerican Indian and Alaska Native peoples (AI/ANs) have a disproportionately high rate of obesity, but little is known about the social determinants of obesity among older AI/ANs. Thus, our study assessed social determinants of obesity in AI/ANs aged ≥ 50 years.DesignWe conducted a cross-sectional analysis using multivariate generalized linear mixed models to identify social determinants associated with the risk of being classified as obese (BMI ≥ 30.0 kg/m2). Analyses were conducted for the total study population and stratified by median county poverty level.SettingIndian Health Service (IHS) data for AI/ANs who used IHS services in FY2013.Participants27,696 AI/ANs aged ≥ 50 years without diabetes.ResultsMean BMI was 29.8 ± 6.6 with 43% classified as obese. Women were more likely to be obese than men, and younger ages were associated with higher obesity risk. While having Medicaid coverage was associated with lower odds of obesity, private health insurance was associated with higher odds. Living in areas with lower rates of educational attainment and longer drive times to primary care services were associated with higher odds of obesity. Those who lived in a county where a larger percentage of people had low access to a grocery store were significantly less likely to be obese.ConclusionsOur findings contribute to the understanding of social determinants of obesity among older AI/ANs and highlight the need to investigate AI/AN obesity, including longitudinal studies with a life course perspective to further examine social determinants of obesity in older AI/ANs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".