Projections of Climate Change Impact on Acute Heat Illnesses in Taiwan: Case-Crossover Study
Bibliographic record
Abstract
Background: With global warming, the number of days with extreme heat is expected to increase and may cause more acute heat illnesses. While decreasing emissions may mitigate the climate impacts, its effectiveness in reducing acute heat illnesses remains uncertain. Taiwan has established a real-time epidemic surveillance and early warning system to monitor acute heat illnesses since January 1, 2011. Predicting the number of acute heat illnesses requires forecasting temperature changes that are influenced by adaptation policies. Objective: The aim of this study was to estimate the changes in the number of acute heat illnesses under different adaptation policies. Methods: We obtained the numbers of acute heat illnesses in Taiwan from January 2011 to July 2023 using emergency department visit data from the real-time epidemic surveillance and early warning system. We used segmented linear regression to identify the join point as a nonoptimal temperature threshold. We projected the temperature distribution and excess acute heat illnesses through the end of the century when Taiwan adopts the "Sustainability (shared socioeconomic pathways 1-2.6 [SSP1-2.6])," "Middle of the road (SSP2-4.5)," "Regional rivalry (SSP3-7.0)," and "Fossil-fueled development (SSP5-8.5)" scenarios. Distributed lag nonlinear models were used to analyze the attributable number (AN) and attributable fraction (AF) of acute heat illnesses caused by nonoptimal temperature. Results: We enrolled a total of 28,661 patients with a mean age of 44.5 (SD 15.3) years up to July 2023, of whom 21,619 (75.4%) were male patients. The nonoptimal temperature was 27 °C. The relative risk of acute heat illnesses with a 1-degree increase in mean temperature was 1.71 (95% CI 1.63-1.79). In the SSP5-8.5 worst-case scenario, the mean temperature was projected to rise by +5.8 °C (SD 0.26), with the AN and AF of acute heat illnesses above nonoptimal temperature being 19,021 (95% CI 2249-35,792) and 89.9% (95% CI 89.3%-90.5%) by 2090-2099. However, if Taiwan adopts the Sustainability SSP1-2.6 scenario, the AN and AF of acute heat illnesses due to nonoptimal temperature will be reduced to 12,468 (95% CI 3233-21,704) and 62.1% (95% CI 61.2-63.1). Conclusions: Adopting sustainable development policies can help mitigate the risk of acute heat illnesses caused by global warming.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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".