Abstract 10333: Recreational Multi Use Trails and Cardiovascular Disease: A Difference-in-Differences 18 Year Natural Experiment
Bibliographic record
Abstract
Introduction: There is little experimental evidence of the impact of multi-use recreational trails that support physical activity on cardiovascular disease (CVD). Hypothesis: Neighbourhoods that added multi-use trails would experience a greater decline in CVD events and risk factors compared to neighbourhoods that did not. Methods: We used a difference in differences design to study the addition of four multi-use trails 4-7km in length on CVD-related outcomes using administrative health, census and built environment data available for all citizens 30 years of age and older from Winnipeg, Canada. A 400m buffer stratified intervention and comparison neighbourhoods. Bicycle counts were recorded via electromagnetic counters for 5 years on all trails. The primary and secondary outcomes were composite measures of incident CVD events (mortality, ischemic heart disease, cerebrovascular disease and congestive heart failure) and CVD risk factors (hypertension, diabetes and dyslipidemia) and assessed quarterly for 10 years prior to and 6 years following the intervention. Intervention and comparison areas were propensity score matched, using scores regressed from baseline measures of age, sex, socioeconomic indicators, and built environment attributes that support physical activity. Results: Between 2012 and 2018, 1,429,588 cyclists were recorded on the trails and cycling use varied ~2.0-fold across the trails. Between 2000 and 2018, there were 82,632 CVD events and 201,058 CVD risk events. During the 18-year natural experiment CVD event rates and risk factors declined ~33% in both comparison and intervention neighbourhoods. In propensity score matched regression models, the incident rate ratio was 1.06 (95% CI: 0.90 to 1.24) for CVD events and 0.92 (95% CI: 0.84 to 1.02) for CVD risk factors. Sensitivity analyses revealed greater effect sizes with increasing trail use (incident rate ratios for highest vs lowest cycling counts = 0.85; 95% CI: 0.75 to 0.96 vs 1.08; 95% CI: 0.92 to 1.27). Conclusions: The addition of recreational multi-use trails was not associated with changes in overall CVD events or risk factors in adjacent neighbourhoods, compared to distant neighbourhoods, however, the effects on CVD risk factors may be influenced by trail use.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".