Using Natural Experiments to Evaluate the Potential Public Health Benefits of the Toronto Cold Weather Program
Bibliographic record
Abstract
Extreme cold weather alert programs have been implemented in some areas to address the significant health impacts of exposure to cold. One such program is the Toronto Cold Weather Program (TCWP) that was implemented in the City of Toronto since 1996 to protect the public from extreme weather conditions. In this paper, we aim to evaluate the effectiveness of the TCWP in reducing mortality and morbidity outcomes related to cold temperatures. We applied a quasi-experimental study design using the Difference-in-Differences method coupled with propensity-score-matching to determine the effect of the TCMP on daily hospitalizations and deaths due to cardiovascular disease (CVD), coronary heart disease (CHD) or cerebrovascular disease, using two complementary analytical approaches. Overall, the analysis did not detect an impact on reduced mortality/morbidity in the City of Toronto from the TCMP. For example, we obtained a Risk Difference (RD) of -0.88 (per 1,000,000 people) (95% CI: -3.27 to 1.51) and a Risk Ratio (RR) of 0.98 (95% CI: 0.91 to 1.05) people for CVD hospitalizations. The TCWP was not found to be effective in reducing cold related mortality and morbidity which demonstrates the importance of improving existing policies related to cold in Canada and other countries.
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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 | 0.020 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".