Optimal heat stress metric for predicting warm-season mortality varies from country to country
Bibliographic record
Abstract
BACKGROUND AND AIM: While heat combined with high humidity is frequently described as the main driver of heat stress, this remains unclear in epidemiological literature. A range of heat stress metrics, each being a different combination of temperature and humidity and sometimes other variables, are available in the literature. We compared eight heat stress metrics with warm-season mortality, with the aim of finding the optimal metric(s) for predicting mortality. METHODS: We performed a two-stage time-series approach using quasi-Poisson regression with distributed lag nonlinear models to derive warm-season exposure-response associations between each heat stress metric and mortality, over 604 locations in 39 countries within the Multi-Country Multi-City (MCC) Collaborative Research Network. The metrics studied were dry-bulb temperature (Tmean), wet-bulb temperature (Tw), apparent temperature (AT), discomfort index, and swamp cooler temperatures at 20, 40, 60 and 80% efficiencies (Swmp20 to Swmp80). The goodness-of-fit of each exposure-response model was assessed using the Quasi-Akaike Information Criterion (qAIC). For each metric and country, we summed the qAIC values across all locations and identified the metric with the lowest country-level qAIC as the optimal metric. We also compared the heat-mortality fraction for each metric. RESULTS: According to qAIC, AT, a metric combining temperature, humidity and wind speed, is the dominant driver of warm-season mortality, especially in Northern and Eastern Europe. Metrics with no or little humidity modification (Tmean and Swmp20) dominate in Southern and Western Asia, Eastern Asia, and Australia. Tw, a metric with large humidity modification, dominate in Caribbean, Central and South American countries but with large uncertainties. However, using Tmean as the only exposure metric does not result in significantly different attributable fractions compared to using the optimal metric. CONCLUSIONS: There is no one-size-fits-all metric for predicting heat-related mortality, but Tmean is suitable enough for estimating impacts in present-day climate. KEYWORDS: heat stress, mortality
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".