Neighbourhood walkability and the incidence of diabetes: an inverse probability of treatment weighting analysis
Bibliographic record
Abstract
BACKGROUND: People living in highly walkable neighbourhoods tend to be more physically active and less likely to be obese. Whether walkable urban design reduces the future risk of diabetes is less clear. METHODS: We used inverse probability of treatment weighting to compare 10-year diabetes incidence between residents living in high-walkability and low-walkability neighbourhoods within five urban regions in Ontario, Canada. Adults (aged 30-85 years) who were diabetes-free on 1 April 2002 were identified from administrative health databases and followed until 31 March 2012 (n=958 567). Within each region, weights reflecting the propensity to live in each neighbourhood type were created based on sociodemographic characteristics, comorbidities and healthcare utilisation and incorporated into region-specific Cox proportional hazards models. RESULTS: Low-walkability areas were more affluent and had more South Asian residents (6.4%vs3.6%, p<0.001) but fewer residents from other minority groups (16.6%vs21.7%, p<0.001). Baseline characteristics were well balanced between low-walkability and high-walkability neighbourhoods after applying individual weights (standardised differences all <0.1). In each region, high walkability was associated with lower diabetes incidence among adults aged <65 years (overall weighted incidence: 8.2vs9.2 per 1000; HR 0.85, 95% CI 0.78 to 0.93), but not among adults aged ≥65 years (weighted incidence: 20.7vs19.5 per 1000; HR 1.01, 95% CI 0.91 to 1.12). Findings were consistent regardless of income and immigration status. CONCLUSIONS: Younger adults living in high-walkability neighbourhoods had a lower 10-year incidence of diabetes than similarly aged adults living in low-walkability neighbourhoods. Urban designs that support walking may have important benefits for diabetes prevention.
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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 | 0.021 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".