Neighborhood Walkability and Diabetes-Related Complications
Bibliographic record
Abstract
Neighborhoods that are more conducive to walking are associated with increased levels of physical activity and lower levels of obesity and diabetes, but the effect of neighborhood walkability on the clinical course of patients with diabetes is not known. We conducted a retrospective cohort study in 439,392 adults using population-based administrative health databases to evaluate the effect of neighborhood walkability on the incidence of diabetes related complications. Adults (≥30 years) with diabetes living in Southern Ontario cities were followed from April 1 2007 to March 31 2017. Neighborhood walkability was derived from a validated index and classified into quintiles from lowest (Q1) to highest (Q5). Cox proportional hazards models were used to estimate the hazard of neighborhood walkability on retinopathy (laser photocoagulation/vitrectomy/VEGF therapy), nephropathy (dialysis or kidney transplantation), foot complications (infection/amputation), and avoidable hospitalizations (hyper/hypoglycemia), adjusting for age, sex, ethnicity, income, and comorbid conditions, as well as significant interactions between these and walkability. Overall, baseline characteristics were similar across neighborhoods, however, low walkability areas tended to be wealthier than high walkability areas. The cohort sustained 30457 retinopathy events, 4883 nephropathy events, 40989 foot complications, and 24272 avoidable hospitalizations. There was an increased risk of retinopathy (Q1:Q5 adjusted HR 1.13, 95% CI 1.07-1.19, p<0.001), nephropathy (Q1:Q5 aHR 1.33, 95% CI 1.16-1.52, p<0.001), foot complications (Q1:Q5 aHR 1.27, 95% CI 1.18-1.37, p<0.001), and avoidable hospitalizations (Q1:Q5 aHR 1.49, 95% CI 1.36-1.64, p<0.001) in lower walkability compared to higher walkability neighborhoods. Neighborhood design may have beneficial effects on diabetes complications. This has important implications for policies to promote healthy environments to better manage the burden of chronic metabolic diseases. Disclosure R. Shah: None. J. Luo: None. H.C. Gerstein: Research Support; Self; Eli Lilly and Company. Advisory Panel; Self; Eli Lilly and Company. Research Support; Self; Sanofi. Advisory Panel; Self; Sanofi. Other Relationship; Self; Sanofi. Advisory Panel; Self; Merck & Co., Inc.. Research Support; Self; Merck & Co., Inc.. Other Relationship; Self; Merck & Co., Inc.. Advisory Panel; Self; AstraZeneca. Research Support; Self; AstraZeneca. Other Relationship; Self; AstraZeneca. Advisory Panel; Self; Novo Nordisk Inc.. Other Relationship; Self; Novo Nordisk Inc.. Advisory Panel; Self; Abbott, Boehringer Ingelheim Pharmaceuticals, Inc.. Other Relationship; Self; Boehringer Ingelheim Pharmaceuticals, Inc.. Advisory Panel; Self; Janssen Pharmaceuticals, Inc.. G. Booth: None.
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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 | 0.000 |
| Bibliometrics | 0.000 | 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".