Heat alerts associated with higher rates of cause-specific hospital admissions but not lower mortality among older adults in the US
Bibliographic record
Abstract
BACKGROUND AND AIM: Heat alerts are issued in advance of forecast periods of extreme heat in order to protect the public’s health, yet little evidence is available regarding their effectiveness in reducing heat-related illness and death. We estimated the association of heat alerts with all-cause mortality and cause-specific hospital admissions among Medicare beneficiaries aged 65 years and older living in counties in the contiguous United States, 2006-2016. METHODS: In each county, we compared days with heat alerts to days without heat alerts, matched on daily maximum heat index (plus or minus 2 degrees Fahrenheit) and month. We used conditional Poisson regression models stratified on county to estimate the association between heat alerts and each health outcome, adjusting for year, day of week, federal holidays, and lagged daily maximum heat index. In sensitivity analyses, we additionally adjusted for ozone, PM2.5, and same-day daily maximum heat index. RESULTS:We were able to identify a matched non-heat alert day for 92,029 heat alert days in 2,817 counties. We did not observe evidence of a protective association between heat alerts and mortality (RR: 1.005 [95% CI: 0.997, 1.013). However, heat alerts were associated with a 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 adjusting for air pollution and same-day heat index. CONCLUSIONS:We found that heat alerts were not associated with lower mortality on the days and counties included in this study. However, this study does provide initial evidence that heat alerts may be associated with higher rates of healthcare utilization for fluid and electrolyte disorders and heat stroke, potentially suggesting that heat alerts lead more individuals to seek or access needed care. KEYWORDS: Temperature, Climate, Environmental epidemiology, Short-term exposure
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".