The Changing Distribution and Determinants of Obesity in the Neighborhoods of New York City, 2003–2007
Bibliographic record
Abstract
Obesity (body mass index >or=30 kg/m(2)) is a growing urban health concern, but few studies have examined whether, how, or why obesity prevalence has changed over time within cities. This study characterized the individual- and neighborhood-level determinants and distribution of obesity in New York City from 2003 to 2007. Individual-level data from the Community Health Survey (n = 48,506 adults, 34 neighborhoods) were combined with neighborhood measures. Multilevel regression assessed changes in obesity over time and associations with neighborhood-level income and food and physical activity amenities, controlling for age, racial/ethnic identity, education, employment, US nativity, and marital status, stratified by gender. Obesity rates increased by 1.6% (P < 0.05) each year, but changes over time differed significantly between neighborhoods and by gender. Obesity prevalence increased for women, even after controlling for individual- and neighborhood-level factors (prevalence ratio = 1.021, P < 0.05), whereas no significant changes were reported for men. Neighborhood factors including increased area income (prevalence ratio = 0.932) and availability of local food and fitness amenities (prevalence ratio = 0.889) were significantly associated with reduced obesity (P < 0.001). Findings suggest that policies to reduce obesity in urban environments must be informed by up-to-date surveillance data and may require a variety of initiatives that respond to both individual and contextual determinants of obesity.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".