Distribution and Determinants of Obesity in New York City, 2003–2007
Bibliographic record
Abstract
OBJECTIVES This study characterized the individual and neighborhood‐level determinants and distribution of obesity (BMI≥30 kg/m 2 ) in New York City (NYC) from 2003 to 2007. METHODS 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, and stratified by gender. RESULTS Obesity risk increased by 1.6% ( P <0.05) each year, but changes over time differed significantly between neighborhoods and by gender. Obesity risk increased for women, even after controlling for individual and neighborhood‐level factors (Prevalence Ratio (PR)=1.021, P <0.05), whereas no significant changes were reported for men. Neighborhood factors including increased area income (PR=0.932) and availability of local food and fitness amenities (PR=0.890) were significantly associated with reduced obesity ( P <0.001). CONCLUSIONS Findings suggest that policies intended to decrease obesity in urban environments should be informed by up to date surveillance data, and may require a variety of initiatives that respond to both the individual and contextual determinants of obesity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".