Attributable Mortality Risk of Temperature: A Multi-Country Study.
Bibliographic record
Abstract
INTRODUCTION: while few studies investigated the attributable mortality risk for either heat or cold in selected countries, none so far has provided estimates for the whole temperature range in different climates. METHODS: we collected data for 326 cities in Australia (1988–2009), Canada (1986–2009), China (1996–2008), Italy (1987–2010), Japan (1972–2009), Korea (1992–2010), Spain (1990–2010), Taiwan (1994–2007), Thailand (1999–2008), UK (1993–2006), and USA (1985–2009), totalling over 48 million deaths. A standard time series Poisson model was fit in each city controlling for trend and day of the week. The temperature-mortality relationship was estimated with a distributed lag non-linear model through a bi-dimensional spline, then reduced to the overall risk cumulated over lag 0–21. City-specific best linear unbiased predictions were computed from a multivariate meta-analytical model. Attributable risk were calculated for heat and cold, defined as temperatures above and below the point of minimum mortality. RESULTS: temperature is attributed in total 6.31% (95% CI 6.05–6.50%) of mortality, with substantial inter-country variation, from 3.3% in Thailand to 11.3% in China. The temperature percentile of minimum mortality varies from around 60 th in (sub)tropical countries (Thailand and Taiwan) to around 80 th -90 th in the other countries. Most of the attributable deaths are due to cold, with a fraction of 5.90% (5.65–6.09%), if compared to 0.41% (0.37–0.44%) due to heat, a ratio relatively stable across countries. Sensitivity analyses show that the estimates are robust to modelling choices and confounding control.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.005 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".