Neighborhood walkability and pre-diabetes incidence in a multiethnic population
Bibliographic record
Abstract
INTRODUCTION: We examined whether adults living in highly walkable areas are less likely to develop pre-diabetes and if so, whether this association is consistent according to immigration status and ethnicity. RESEARCH DESIGN AND METHODS: Population-level health, immigration, and administrative databases were used to identify adults aged 20-64 (n=1 128 181) who had normoglycemia between January 2011 and December 2011 and lived in one of 15 cities in Southern Ontario, Canada. Individuals were assigned to one of ten deciles (D) of neighborhood walkability (from lowest (D1) to highest (D10)) and followed until December 2013 for the development of pre-diabetes. RESULTS: Overall, 220 225 individuals in our sample developed pre-diabetes during a median follow-up of 8.4 years. Pre-diabetes incidence was 20% higher among immigrants living in the least (D1) (adjusted HR 1.20, 95% CI 1.15 to 1.25, p<0.0001) versus most (D10, referent) walkable neighborhoods after accounting for age, sex, and area income. Findings were similar among long-term residents and across sexes. However, susceptibility to walkability varied by ethnicity where D1 versus D10 adjusted HRs ranged from 1.17 (95% CI 1.02 to 1.35, p=0.03) among West Asian and Arab immigrants to 1.32 (95% CI 1.19 to 1.47, p<0.0001) in Southeast Asians. Ethnic variation in pre-diabetes incidence was more marked in low walkability settings. Relative to Western Europeans, the adjusted HR for pre-diabetes incidence was 2.11 (95% CI 1.81 to 2.46, p<0.0001) and 1.50 (95% CI 1.27 to 1.77, p<0.0001) among Sub-Saharan African and the Carribean and Latin American immigrants, respectively, living in the least walkable (D1) neighborhoods, but only 1.24 (95% CI 1.08 to 1.42, p=0.002) and 1.00 (95% CI 0.87 to 1.15, p=0.99) for these same groups living in the most walkable (D10) neighborhoods. CONCLUSIONS: Pre-diabetes incidence was reduced in highly walkable areas for most groups living in Southern Ontario cities. These findings suggest a potential role for walkable urban design in diabetes prevention.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".