The graded heat-health risk forecast and early warning with full-season coverage across China: a predicting model development and evaluation studyResearch in context
Summary: Background: Due to global climate change, high temperature and heatwaves have become critical issues that pose a threat to human health. An effective early warning system is essential to mitigate the health risks associated with high temperature and heatwaves. However, most of the current...
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Elsevier
2025-01-01
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| Series: | The Lancet Regional Health. Western Pacific |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666606524002608 |
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| author | Qing Wang Chen Chen Huaiyue Xu Yuanyuan Liu Yu Zhong Jing Liu Menghan Wang Mengxue Zhang Yiting Liu Jing Li Tiantian Li |
| author_facet | Qing Wang Chen Chen Huaiyue Xu Yuanyuan Liu Yu Zhong Jing Liu Menghan Wang Mengxue Zhang Yiting Liu Jing Li Tiantian Li |
| author_sort | Qing Wang |
| collection | DOAJ |
| description | Summary: Background: Due to global climate change, high temperature and heatwaves have become critical issues that pose a threat to human health. An effective early warning system is essential to mitigate the health risks associated with high temperature and heatwaves. However, most of the current heatwave early warning systems are not adequately developed based on the heat-health risk model, and the health impact of hot weather has not been well managed in most countries. Methods: This study proposed a “full-season coverage and population health-oriented graded early-warning” concept and developed a heat-health surveillance, forecast and early warning (HHSEW) model. The exposure-response (E-R) relationship between temperature and mortality was analyzed through a two-stage approach using time-series analysis data from 323 counties across China for the period 2013–2018. The premature mortality curve at each temperature percentile was plotted and four temperature-percentile points on the curve were determined as the thresholds of the pre-warning and warning levels 1–3 based on the variations in the rates of the segmental slopes on the curve. The HHSEW model was evaluated by comparing the frequency, the mortality risk of all-cause and cause-specific diseases, the predicted numbers of premature deaths, and the heat-related health economic burden at each warning level with those of the current high temperature early warning systems. Findings: The HHSEW model determined five levels, including seasonal surveillance, pre-warning, and warning levels 1–3. There was a gradual increase in the mortality risks of all-cause and cause-specific diseases along with the increase of warning levels. The risk of all-cause mortality increased by 9.79% (95% CI: 8.59%–11.01%), 22.62% (95% CI: 19.49%–25.83%), 28.36% (95% CI: 24.72%–32.10%), and 33.87% (95% CI: 28.89%–39.06%) at the pre-warning level, warning level 1, warning level 2, and warning level 3, respectively. Through our HHSEW model, 94,008 heat-related all-cause deaths were predicted annually in the 337 major cities of China, which was much larger than the number (14,858) of the China Meteorological Administration (CMA) heatwave early-warning system currently used in China. It was estimated that the proper implementation of the HHSEW-based early warning system would save 220 billion CNY in heat-related health burden compared to the current heatwave early-warning system. Interpretation: The HHSEW model has been proven to surpass the current heatwave early warning system. With its full-season coverage and graded warning levels for heat-related health risks, the HHSEW model and system can provide timely early warnings to the public, leading to significant health benefits. This methodology, labeled “full-season coverage and population health-oriented graded early-warning”, should be implemented globally to mitigate the escalating health risks associated with high temperature. Funding: National Natural Science Foundation of China (82425051, 42071433, 42305196, 82241051) and the Special Foundation of Basic Science and Technology Resources Survey of Ministry of Science and Technology of China (2017FY101204). |
| format | Article |
| id | doaj-art-55c89c503a65427dacd505638806a074 |
| institution | Kabale University |
| issn | 2666-6065 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | The Lancet Regional Health. Western Pacific |
| spelling | doaj-art-55c89c503a65427dacd505638806a0742025-01-13T04:19:15ZengElsevierThe Lancet Regional Health. Western Pacific2666-60652025-01-0154101266The graded heat-health risk forecast and early warning with full-season coverage across China: a predicting model development and evaluation studyResearch in contextQing Wang0Chen Chen1Huaiyue Xu2Yuanyuan Liu3Yu Zhong4Jing Liu5Menghan Wang6Mengxue Zhang7Yiting Liu8Jing Li9Tiantian Li10National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Beijing, China; China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China; China Meteorological Administration Key Laboratory of Meteorological Medicine and Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, ChinaNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Beijing, China; China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China; China Meteorological Administration Key Laboratory of Meteorological Medicine and Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, ChinaNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Beijing, China; China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China; Dong Fureng Institute of Economic and Social Development, Wuhan University, Wuhan, ChinaNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Beijing, China; China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China; China Meteorological Administration Key Laboratory of Meteorological Medicine and Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, ChinaNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Beijing, China; China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, ChinaNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Beijing, China; China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, ChinaNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Beijing, China; China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, ChinaNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Beijing, China; China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, ChinaNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Beijing, China; China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, ChinaNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Beijing, China; China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, ChinaNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Beijing, China; China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China; China Meteorological Administration Key Laboratory of Meteorological Medicine and Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China; Corresponding author. No. 7 Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China.Summary: Background: Due to global climate change, high temperature and heatwaves have become critical issues that pose a threat to human health. An effective early warning system is essential to mitigate the health risks associated with high temperature and heatwaves. However, most of the current heatwave early warning systems are not adequately developed based on the heat-health risk model, and the health impact of hot weather has not been well managed in most countries. Methods: This study proposed a “full-season coverage and population health-oriented graded early-warning” concept and developed a heat-health surveillance, forecast and early warning (HHSEW) model. The exposure-response (E-R) relationship between temperature and mortality was analyzed through a two-stage approach using time-series analysis data from 323 counties across China for the period 2013–2018. The premature mortality curve at each temperature percentile was plotted and four temperature-percentile points on the curve were determined as the thresholds of the pre-warning and warning levels 1–3 based on the variations in the rates of the segmental slopes on the curve. The HHSEW model was evaluated by comparing the frequency, the mortality risk of all-cause and cause-specific diseases, the predicted numbers of premature deaths, and the heat-related health economic burden at each warning level with those of the current high temperature early warning systems. Findings: The HHSEW model determined five levels, including seasonal surveillance, pre-warning, and warning levels 1–3. There was a gradual increase in the mortality risks of all-cause and cause-specific diseases along with the increase of warning levels. The risk of all-cause mortality increased by 9.79% (95% CI: 8.59%–11.01%), 22.62% (95% CI: 19.49%–25.83%), 28.36% (95% CI: 24.72%–32.10%), and 33.87% (95% CI: 28.89%–39.06%) at the pre-warning level, warning level 1, warning level 2, and warning level 3, respectively. Through our HHSEW model, 94,008 heat-related all-cause deaths were predicted annually in the 337 major cities of China, which was much larger than the number (14,858) of the China Meteorological Administration (CMA) heatwave early-warning system currently used in China. It was estimated that the proper implementation of the HHSEW-based early warning system would save 220 billion CNY in heat-related health burden compared to the current heatwave early-warning system. Interpretation: The HHSEW model has been proven to surpass the current heatwave early warning system. With its full-season coverage and graded warning levels for heat-related health risks, the HHSEW model and system can provide timely early warnings to the public, leading to significant health benefits. This methodology, labeled “full-season coverage and population health-oriented graded early-warning”, should be implemented globally to mitigate the escalating health risks associated with high temperature. Funding: National Natural Science Foundation of China (82425051, 42071433, 42305196, 82241051) and the Special Foundation of Basic Science and Technology Resources Survey of Ministry of Science and Technology of China (2017FY101204).http://www.sciencedirect.com/science/article/pii/S2666606524002608High temperatureHeatwaveHealth riskSurveillanceEarly warningChina |
| spellingShingle | Qing Wang Chen Chen Huaiyue Xu Yuanyuan Liu Yu Zhong Jing Liu Menghan Wang Mengxue Zhang Yiting Liu Jing Li Tiantian Li The graded heat-health risk forecast and early warning with full-season coverage across China: a predicting model development and evaluation studyResearch in context The Lancet Regional Health. Western Pacific High temperature Heatwave Health risk Surveillance Early warning China |
| title | The graded heat-health risk forecast and early warning with full-season coverage across China: a predicting model development and evaluation studyResearch in context |
| title_full | The graded heat-health risk forecast and early warning with full-season coverage across China: a predicting model development and evaluation studyResearch in context |
| title_fullStr | The graded heat-health risk forecast and early warning with full-season coverage across China: a predicting model development and evaluation studyResearch in context |
| title_full_unstemmed | The graded heat-health risk forecast and early warning with full-season coverage across China: a predicting model development and evaluation studyResearch in context |
| title_short | The graded heat-health risk forecast and early warning with full-season coverage across China: a predicting model development and evaluation studyResearch in context |
| title_sort | graded heat health risk forecast and early warning with full season coverage across china a predicting model development and evaluation studyresearch in context |
| topic | High temperature Heatwave Health risk Surveillance Early warning China |
| url | http://www.sciencedirect.com/science/article/pii/S2666606524002608 |
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