Associations of perceived neighbourhood safety from traffic and crime with overweight/obesity among South African adults of low-socioeconomic status
Bibliographic record
Abstract
BACKGROUND: The relationship between perceived neighbourhood safety from traffic and crime with overweight/obesity can provide intervention modalities for obesity, yet no relevant study has been conducted in sub-Saharan African contexts. We investigated the association between perceived neighbourhood safety from traffic and crime with overweight/obesity among urban South African adults. METHODS: This cross-sectional study included 354 adults aged ≥35 years drawn from the Prospective Urban Rural Epidemiology (PURE) cohort study. The Neighborhood Walkability Scale-Africa (NEWS-A) was used to evaluate the perceived neighbourhood safety. Adjusted odds ratios (ORs) and 95% confidence intervals (CIs) were calculated to examine the associations between perceived neighborhood safety and overweight/obesity defined "normal weight" and "overweight/obese" using the 25 Kg/m2 cutoff criterion. RESULTS: In the overall sample, adults who agreed that "the speed of traffic on most nearby roads in their neighborhood was usually slow" were less likely to be overweight/obese (adjusted OR = 0.42; 95%CI 0.23-0.76). Those who agreed that "there was too much crime in their neighborhood to go outside for walks or play during the day" were more likely to be overweight/obese (OR = 2.41; 1.09-5.29). These associations were driven by significant associations in women, and no association in men, with significant statistical interactions. CONCLUSION: Perceived neighborhood safety from traffic and crime was associated with overweight/obesity among South African adults. Our findings provide preliminary evidence on the need to secure safer environments for walkability. Future work should also consider perceptions of the neighbourhood related to food choice.
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 | 0.002 |
| 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 | 0.000 |
| 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".