The Changing Distribution and Determinants of Obesity in the Neighborhoods of New York City, 2003–2007
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it