Did a vehicle emission inspection program lead to lower cardiovascular mortality in the Greater Vancouver region?
Bibliographic record
Abstract
Introduction: The AirCare vehicle inspection program began in 1992 to control tailpipe emissions within the Greater Vancouver area, Canada, and ended in 2014. Since 1992, there have been regional declines in concentrations of traffic-related air pollution (TRAP) and cardiovascular mortality. Some of the declines in TRAP can be attributed to the AirCare program. While higher level of TRAP has been associated with higher rate of cardiovascular mortality, it is possible that some of the declines in cardiovascular mortality in the region is the effect of the AirCare program. This study is aimed to quantify such an effect, which is challenging to separate from the secular trend of mortality resulting from other complex factors. Methods: A time-slice analysis was applied to estimate the effect of each AirCare phase compared with the previous phase, both unadjusted and with adjustment for cardiovascular mortality secular trends observed in Victoria and Calgary, two most comparable cities in Canada without similar programs. A time-series analysis was applied to estimate the effect of every 100 failed cars on cardiovascular mortality in a distributed lag framework. Results: Time-slice analyses showed that the AirCare effects were sensitive to choice of the control population. Time-series analyses showed a 2.9% (95%CI 1.0%, 4.9%) decrease in daily cardiovascular mortality for each 100 cars that failed, when the lagged effect was distributed over one year. Results suggest that the AirCare program reduced cardiovascular mortality in the region by 2,000-11,000 deaths over its lifetime. Conclusions: Evaluation of an air quality intervention implemented over 22 years is statistically challenging. Our results suggest that the AirCare program contributes to the reduction of cardiovascular mortality in the Greater Vancouver area, and provide evidence for other regions considering implementation or extension of similar programs.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".