The association between temperature variability and mortality: an international collaborative study
Bibliographic record
Abstract
Introduction: The evidence is limited for the impacts of temperature variability (TV) within or between days on mortality. In this study, we applied a novel method to calculate TV, and investigated the TV-mortality associations using a large multi-country data set. Methods: We collected daily time series data of temperature and mortality from 372 locations in 12 countries/regions with a wide range of climates (Australia, Brazil, Canada, China, Japan, Moldova, South Korea, Spain, Taiwan, Thailand, UK, and USA). We developed a novel method to calculate TV using the standard deviation of current day’s and preceding days’ minimum and maximum temperatures. Two-stage analyses were used to assess the relation between TV and mortality. Firstly, a Poisson regression model allowing over-dispersion was used to estimate the community-specific TV-mortality relation, after controlling for a long-term trend, seasonality, day of the week, and main effects of daily mean temperature up to 21 days. In the second stage, a meta-analysis was used to pool the effects within each country. Results: There was a significant association between TV and mortality in all countries, even after controlling for the effects of daily mean temperature. In stratified analyses, TV was still significantly associated with mortality in cold, hot, and moderate seasons. Mortality risks related to TV were higher in hot areas than cold areas when using short TV exposure (0–1 days), while TV-related mortality risks were higher in moderate areas than cold and hot areas when using longer TV exposure days (0–7 days). Conclusion: This large multi-country analysis suggests that TV has significant impacts on mortality. Results indicate that more attention should be paid to unstable weather conditions in order to protect health. These findings may have implications for developing public health policies for managing health risks of climate change.
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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 | 0.011 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".