Heat warnings, mortality, and hospital admissions among older adults in the United States
Bibliographic record
Abstract
Heat warnings are issued in advance of forecast extreme heat events, yet little evidence is available regarding their effectiveness in reducing heat-related illness and death. We estimated the association of heat warnings and advisories (collectively, “alerts”) issued by the United States National Weather Service with all-cause mortality and cause-specific hospitalizations among Medicare beneficiaries aged 65 years and older in 2,817 counties, 2006–2016. In each county, we compared days with heat alerts to days without heat alerts, matched on daily maximum heat index and month. We used conditional Poisson regression models stratified on county, adjusting for year, day of week, federal holidays, and lagged daily maximum heat index. We identified a matched non-heat alert day for 92,029 heat alert days in 2,817 counties, or 54.6% of all heat alert days during the study period. Contrary to expectations, heat alerts were not associated with lower risk of mortality (RR: 1.005 [95% CI: 0.997, 1.013]). However, heat alerts were associated with higher risk of hospitalization for fluid and electrolyte disorders (RR: 1.040 [95% CI: 1.015, 1.065]) and heat stroke (RR: 1.094 [95% CI: 1.038, 1.152]). Results were similar in sensitivity analyses additionally adjusting for same-day heat index, ozone, and PM2.5. Our results suggest that heat alerts are not associated with lower risk of mortality but may be associated with higher rates of hospitalization for fluid and electrolyte disorders and heat stroke, potentially suggesting that heat alerts lead more individuals to seek or access care.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 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 teacher head, 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".