Short-term association between hot nights and mortality: a multicountry analysis in 178 locations considering hourly ambient temperature
Bibliographic record
Abstract
BACKGROUND: The rise in hot nights over recent decades and projections of further increases due to climate change underscores the critical need to understand their impact. This knowledge is essential for shaping public health strategies and guiding adaptation efforts. Despite their significance, research on the implications of hot nights remains limited. OBJECTIVE: This study estimated the association between hot-night excess (the sum of excess heat during the nighttime above a threshold) and duration (the percent of nighttime with a positive excess) based on hourly ambient temperatures and daily mortality in the warm season over multiple locations worldwide. METHODS: We fitted time series regression models to mortality in 178 locations across 44 countries using a distributed lag non-linear model over lags of 0-3 days, controlling for daily maximum temperature and daily mean absolute humidity. Next, we used a multivariate meta-regression model to pool results and estimated attributable burdens. RESULTS: We found a positive, increasing mortality risk with hot-night excess and duration. Assuming 0 as a reference, the pooled relative risks of death associated with extreme excess and duration, defined as the 90th percentile in each index, were both similar at 1.026 (95 % CI, 1.017; 1.036) and 1.026 (95 % CI, 1.013; 1.040). The overall estimated attributable fractions were also observed to be closely similar at 0.60 % (95 % CI, 0.09; 1.10 %) and 0.62 % (95 % CI, 0.00; 1.23 %), respectively. DISCUSSION: This study provides new evidence that hot nights have a specific contribution to heat-related mortality risk. Modeling thermal characteristics' sub-hourly impact on mortality during the night could improve decision-making for long-term adaptions and preventive public health strategies.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.008 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".