Higher Neighborhood Drivability Is Associated With a Higher Diabetes Risk in Younger Adults: A Population-Based Cohort Study in Toronto, Canada
Bibliographic record
Abstract
OBJECTIVE: Car dependency contributes to physical inactivity and, consequently, may increase the likelihood of diabetes. We investigated whether neighborhoods that are highly conducive to driving confer a greater risk of developing diabetes and, if so, whether this differs by age. RESEARCH DESIGN AND METHODS: We used administrative health care data to identify all working-age Canadian adults (20-64 years) who were living in Toronto on 1 April 2011 without diabetes (type 1 or 2). Neighborhood drivability scores were assigned using a novel, validated index that predicts driving patterns based on built environment features divided into quintiles. Cox regression was used to examine the association between neighborhood drivability and 7-year risk of diabetes onset, overall and by age-group, adjusting for baseline characteristics and comorbidities. RESULTS: Overall, there were 1,473,994 adults in the cohort (mean age 40.9 ± 12.2 years), among whom 77,835 developed diabetes during follow-up. Those living in the most drivable neighborhoods (quintile 5) had a 41% higher risk of developing diabetes compared with those in the least drivable neighborhoods (adjusted hazard ratio 1.41, 95% CI 1.37-1.44), with the strongest associations in younger adults aged 20-34 years (1.57, 95% CI 1.47-1.68, P < 0.001 for interaction). The same comparison in older adults (55-64 years) yielded smaller differences (1.31, 95% CI 1.26-1.36). Associations appeared to be strongest in middle-income neighborhoods for younger residents (middle income 1.96, 95% CI 1.64-2.33) and older residents (1.46, 95% CI 1.32-1.62). CONCLUSIONS: High neighborhood drivability is a risk factor for diabetes, particularly in younger adults. This finding has important implications for future urban design policies.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".