Temperature-mortality associations by age and cause: a multi-country multi-city study
Bibliographic record
Abstract
Background: Heterogeneity in temperature-mortality relationships across locations may partly result from differences in the demographic structure of populations and their cause-specific vulnerabilities. Here we conduct the largest epidemiological study to date on the association between ambient temperature and mortality by age and cause using data from 532 cities in 33 countries. Methods: We collected daily temperature and mortality data from each country. Mortality data was provided as daily death counts within age groups from all, cardiovascular, respiratory, or noncardiorespiratory causes. We first fit quasi-Poisson regression models to estimate location-specific associations for each age-by-cause group. For each cause, we then pooled location-specific results in a dose-response multivariate meta-regression model that enabled us to estimate overall temperature-mortality curves at any age. The age analysis was limited to adults. Results: We observed high temperature effects on mortality from both cardiovascular and respiratory causes compared to noncardiorespiratory causes, with the highest cold-related risks from cardiovascular causes and the highest heat-related risks from respiratory causes. Risks generally increased with age, a pattern most consistent for cold and for nonrespiratory causes. For every cause group, risks at both temperature extremes were strongest at the oldest age (age 85 years). Excess mortality fractions were highest for cold at the oldest ages. Conclusions: There is a differential pattern of risk associated with heat and cold by cause and age; cardiorespiratory causes show stronger effects than noncardiorespiratory causes, and older adults have higher risks than younger adults.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.008 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".