Change in Residential Proximity to Traffic and Risk of Death from Coronary Heart Disease
Bibliographic record
Abstract
ISEE-0606 Background and Objectives: Several epidemiologic studies have demonstrated that residential proximity to traffic is associated with accelerated coronary atherosclerosis and increased risk of coronary events. This study was aimed to investigate whether change in residential proximity to traffic was able to alter the risk of death from coronary heart disease (CHD). Methods: This population-based cohort study was conducted in the greater Vancouver metropolitan region, Canada. All residents aged 45–85 years who resided in the study region for at least 5 years (exposure period) and without previous CHD at baseline were included. CHD deaths during a 4-year follow-up period were identified using hospitalization and death records. Residential (postal code) proximity to traffic was calculated using a geographic information system. The data were modeled using multivariate logistic regression. Results: A total of 450,283 participants with complete demographic and residential proximity information were enrolled. Compared to the participants consistently living far from traffic (>150m from a highway or >50m from a major road) during the exposure period and the follow-up period, those consistently living close to traffic (≤150m from a highway or ≤50m from a major road) were 29% (95% CI 1.18–1.41) more likely to die from CHD during the follow-up period after adjustment for baseline age, sex, pre-existing diseases (diabetes, COPD, or hypertensive heart disease), and neighborhood socioeconomic status. For those who moved away from traffic during the exposure period, there was a non-significant 14% increase in the risk of CHD death (95% CI 0.95–1.37) during the follow-up period; whereas for those moving closer to traffic, the risk increased 20% (95% CI 1.00–1.43). Conclusions: This study confirmed previous findings that living close to traffic was associated with increased risk of coronary death. Importantly, this study revealed that change in residential proximity to traffic was able to alter the risk of coronary death.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".