Effects of extreme temperature on morbidity, mortality, and case severity in German emergency care
Bibliographic record
Abstract
Climate change affects the frequency and intensity of extreme heat and cold events, which can have severe health repercussions. Therefore, we investigated the effects of extreme ambient temperature on emergency care-associated morbidity, mortality, and case severity in Germany. We analyzed all somatic emergency admissions (EA) to German hospitals from 2010 to 2019. Using weather station data from the German Meteorological Service, we estimated immediate and 28-day lagged effects of extreme heat (99th percentile of mean temperature) and extreme cold (1st percentile of mean temperature) in a two-stage time-series analysis using a distributed lag non-linear model. 78,486,368 EAs were included in the study. The cumulated immediate and lagged effect of temperature indicated that extreme cold decreased the EA risk but increased the fatal EA risk and case mortality. In turn, extreme heat increased the EA risk, the fatal EA risk, and the case mortality. The 1% (5%) coldest days prevented 3,400 (11,950) EAs but led to 450 (2150) additional in-hospital deaths following an EA. The 1% (5%) hottest days resulted in 4,900 (20,550) additional EAs and 300 (1,050) additional deaths. Generally, the effect of extreme cold unfolded over four weeks, while the effects of heat manifested more promptly and subsided virtually within the first week. Our findings highlight that extreme heat is associated with an increase in emergency care-associated morbidity, while both extreme heat and cold are associated with a higher emergency care-associated mortality and case severity in Germany, urging greater efforts to curb the health effects of extreme temperatures. • Analysis of all somatic emergency admissions (EA) to German hospitals within 10 years. • Effects of extreme heat unfold promptly; effects of extreme cold across several weeks. • Extreme heat raises the EA risk, resulting in 4900 additional cases p.a. • Accounting for lagged effects, extreme cold leads to a net-reduction of the EA risk. • Overall, extreme heat and cold raise the fatal EA risk, with 750 excess deaths p.a.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".