Abstract
Heat warning systems represent a critical health adaptation strategy under global boiling. This study evaluates the appropriateness of the existing criteria of heat warning categories, which were established based on climatology. The objectives were (1) to evaluate the association of the daily maximum temperature (Tmax) and multiple health outcomes (including emergency visits, hospitalization cases, and mortality of heat-related illness (HRI), cardiovascular, respiratory, diabetes, and renal diseases) considering both the lag and cumulative effects, and (2) to identify vulnerable groups (considering their demographic, occupation, geographic, and health status) and areas of heat-health impacts. Weather and health records in Taiwan from May to October from 2000 to 2019 were analyzed using generalized additive models. The results show that HRI was the most sensitive health outcome, and the relative risk (RR) was 1.81 (confidence interval (CI): 1.51 – 2.18) and 2.99 (CI: 1.99 – 4.49) for emergency visits and hospitalizations, respectively, when Tmax was ≥ 34 °C. The corresponding RRs were 2.00 (CI: 1.67 – 2.39) and 2.39 (CI: 1.44 – 3.95) when Tmax was ≥ 32 and ≥ 31 °C for three consecutive days, respectively. The morbidity risks of cardiovascular, respiratory, diabetes, and renal diseases all increased at different temperature thresholds. Significant associations between Tmax and health outcomes occurred at thresholds lower than the current warning thresholds, indicating the need for revision. Both lag and cumulative effects need to be considered in heat-health warning systems. People with hypertension, hyperglycemia, or hyperlipidemia were found to be more vulnerable, as they had higher RRs for pneumonia, COPD, and stroke than the general population. Among different occupations, farmers were found to be most vulnerable. This study demonstrates a methodology considering both lag and cumulative effects that can be applied in other countries to assist in the establishment of evidence-based heat-health warning systems.
Data availability
The data that support the findings of this study are available from the Ministry of Health and Welfare Data Science Center, but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission from Ministry of Health and Welfare Data Science Center.
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Acknowledgements
We want to express our gratitude to the funding support of Health promotion Administration, Ministry of Health and Welfare from projects numbered N1100101, N1100101-111, and E1111215. We also thank the Health and Welfare Data Science Center of the Ministry of Health and Welfare, Taiwan Central Weather Administration, and Ministry of Environment, Executive Yuan, Taiwan for providing data. The contents of this paper are solely the responsibility of the authors and do not represent the official views of the aforementioned institutes and funding agencies.
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S-CC Lung: conceptualization, funding acquisition, methodology, project administration, resources, supervision, visualization, and writing—original draft and editing. J-CJ Yeh: data curation, formal analysis, software, visualization, and writing—review and editing. J-S Hwang: conceptualization, methodology, and writing—review and editing. L-S Chen: conceptualization, resources, and writing—review and editing.
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Supplementary Material 1
One additional file contains Table S1 (ICD-9 and ICD-10 used), Table S2 (the test results of lag-day choices), Table S3 (number of cases used in the epidemiological analysis), Figure S1 (six divisions of Taiwan), Figure S2 (the numbers of days with Tmax above certain thresholds in different divisions), Figure S3 (the numbers of cases with 3 consecutive days of Tmax above certain thresholds in different divisions), Figure S4 (plots of the relationships between Tmax and the studied health outcomes in the emergency visits), Figure S5 (plots of the relationships between Tmax and the studied health outcomes in the hospitalized cases), and Figure S6 (plots of the relationships between Tmax and the studied health outcomes in mortality).
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Lung, SC.C., Yeh, JC.J., Hwang, JS. et al. Evaluation of heat warning thresholds with multiple lagged and cumulative health impacts based on a 20-year population database. Sci Rep (2026). https://doi.org/10.1038/s41598-025-31832-2
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DOI: https://doi.org/10.1038/s41598-025-31832-2
