Obesity prevalence in large US cities: association with socioeconomic indicators, race/ethnicity and physical activity
Bibliographic record
Abstract
BACKGROUND: Obesity has a complex association with socioeconomic factors. Further clarification of this association could guide population interventions. METHODS: To determine the relationship between obesity prevalence, socioeconomic indicators, race/ethnicity, and physical activity, we performed a cross-sectional, multivariable linear regression, with data from large US cities participating in the Big Cities Health Inventory. RESULTS: Increased household income was significantly associated with decreased obesity prevalence, for White (-1.97% per 10 000USD), and Black (-3.02% per 10 000USD) populations, but not Hispanic. These associations remained significant when controlling for the proportion of the population meeting physical activity guidelines. Educational attainment had a co-linear relationship with income, and only a bachelor's degree or higher was associated with a lower prevalence of obesity in White (-0.30% per percentage) and Black (-0.69% per percentage) populations. No association was found between obesity prevalence and the proportion of the population meeting physical activity guidelines for any race/ethnicity grouping. CONCLUSION: At the population level of large US cities, obesity prevalence is inversely associated with median household income in White and Black populations. Strategies to increase socioeconomic status may also decrease obesity. Targeting attainment of physical activity guidelines as an obesity intervention needs further appraisal.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".