Estimating the impacts of nonoptimal temperatures on mortality: A study in British Columbia, Canada, 2001–2021
Bibliographic record
Abstract
Background: Studies show that more than 5.1 million deaths annually are attributed to nonoptimal temperatures, including extreme cold and extreme heat. However, those studies mostly report average estimates across large geographical areas. The health risks attributed to nonoptimal temperatures in British Columbia (BC) are reported incompletely or limit the study area to urban centers. In this study, we aim to estimate the attributable deaths linked to nonoptimal temperatures in all five regional health authorities (RHAs) of BC from 2001 to 2021. Methods: We applied the widely used distributed lag nonlinear modeling approach to estimate temperature-mortality association in the RHAs of BC, using daily all-cause deaths and 1 × 1 km gridded daily mean temperature. We evaluated the model by comparing the model-estimated attributable number of deaths during the 2021 heat dome to the number of heat-related deaths confirmed by the British Columbia Coroners Service. Results: Overall, between 2001 and 2021, we estimate that 7.17% (95% empirical confidence interval = 3.15, 10.32) of deaths in BC were attributed to nonoptimal temperatures, the majority of which are attributed to cold. On average, the mortality rates attributable to moderate cold, moderate heat, extreme cold, and extreme heat were 47.04 (95% confidence interval [CI] = 45.83, 48.26), 0.94 (95% CI = 0.81, 1.08), 2.88 (95% CI = 2.05, 3.71), and 3.10 (95% CI = 1.79, 4.4) per 100,000 population per year, respectively. Conclusions: Our results show significant spatial variability in deaths attributable to nonoptimal temperatures across BC. We find that the effect of extreme temperatures is significantly less compared to milder nonoptimal temperatures between 2001 and 2021. However, the increased contribution of extreme heat cannot be ruled out in the near future.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.005 | 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 teacher head, 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".