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Gai Y, Su H, Fan Y, Cheng W, Zou X, Fan Y, Li Y, Ding Z, Liu J, Su Y, Jin Z, Zhang L, Ouyang Y, Zhai Y, Ding Y, Zhao C, Cheng J, Zheng H. Establishing and mapping heat-sensitive disease spectrum in eastern China: A comprehensive analysis of 1.4 million deaths involving 14 major disease categories. ENVIRONMENT INTERNATIONAL 2025; 200:109529. [PMID: 40381410 DOI: 10.1016/j.envint.2025.109529] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/13/2025] [Revised: 04/08/2025] [Accepted: 05/11/2025] [Indexed: 05/20/2025]
Abstract
BACKGROUND Although high temperatures can affect multiple systems and organs, the comprehensive assessment of heat-sensitive diseases remains unclear. We aimed to establish the heat-related sensitive disease spectrum and assess the relative importance of affected diseases from the health risk and burden perspectives. METHODS A space-time-stratified case-crossover analysis was used to examine the short-term association between high temperatures and cause-specific deaths in Jiangsu Province, China during the warm season of 2016 to 2019. A total of 14 major disease categories and 29 specific diseases were tested to identify heat-sensitive diseases. A multi-level comparison of heat-affected diseases was conducted based on the health risk and burden indicators including mortality risk, years of life lost (YLL) to measure disease burden, and value of YLL (VYLL) to measure economic burden. RESULTS High temperatures were associated with an increased risk of mortality from 23 specific diseases involving 12 major disease categories, including well-studied cardiovascular, respiratory, endocrine, and nervous diseases, and less-studied skin, urinary system diseases, mental and behavioral disorders, external causes, injury and poisoning, symptoms, signs and abnormal clinical, and neoplasms. The top three greatest heat-related risks of mortality from major disease categories were skin system (OR: 1.72, 95 % CI: 1.37-2.36), external causes of mortality (OR: 1.71, 95 % CI: 1.57-1.87), and nervous system (OR: 1.46, 95 % CI: 1.26-1.68), and cause-specific diseases were asthma (OR: 2.26, 95 % CI: 1.46-3.50), accidental drowning (OR: 1.85, 95 % CI: 1.42-2.40), and acute respiratory infections (OR: 1.80, 95 % CI: 1.02-3.16). In terms of both disease and economic burdens attributable to heat, cardiovascular diseases contributed to the greatest proportion, followed by neoplasms, external causes, and respiratory diseases. Within specific diseases, cerebrovascular diseases contributed the greatest disease and economic burdens, followed by ischemic heart disease, lung (neoplasm), and COPD. Furthermore, the largest heat-related reduction in life expectancy reached 5.27 years for external causes and 12.96 years for accidental drowning. CONCLUSION This study provides a heat-sensitive disease spectrum and resulting death risk and burden vary by different systems and specific diseases. Our findings may have implications for implementing heat-health action plans to mitigate the adverse effects of heat-sensitive diseases.
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Affiliation(s)
- Yiming Gai
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, China; Anhui Province Key Laboratory of Major Autoimmune Disease, Hefei, China
| | - Hong Su
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, China; Anhui Province Key Laboratory of Major Autoimmune Disease, Hefei, China
| | - Yinguang Fan
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, China; Anhui Province Key Laboratory of Major Autoimmune Disease, Hefei, China
| | - Wenjun Cheng
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, China; Anhui Province Key Laboratory of Major Autoimmune Disease, Hefei, China
| | - Xiaojie Zou
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, China; Anhui Province Key Laboratory of Major Autoimmune Disease, Hefei, China
| | - Yarui Fan
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, China; Anhui Province Key Laboratory of Major Autoimmune Disease, Hefei, China
| | - Yuefang Li
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, China; Anhui Province Key Laboratory of Major Autoimmune Disease, Hefei, China
| | - Zhen Ding
- Department of Environmental Health, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, China
| | - Jintao Liu
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, China; Anhui Province Key Laboratory of Major Autoimmune Disease, Hefei, China
| | - Yongmei Su
- The First Clinical College of Anhui Medical University, Hefei, China
| | - Zien Jin
- School of Public Health, Anhui Medical University, Hefei, China
| | - Liwei Zhang
- School of Public Health, Anhui Medical University, Hefei, China
| | - Yanan Ouyang
- School of Public Health, Anhui Medical University, Hefei, China
| | - Yujia Zhai
- School of Public Health, Anhui Medical University, Hefei, China
| | - Yiyun Ding
- School of Public Health, Anhui Medical University, Hefei, China
| | - Chun Zhao
- School of Public Health, Anhui Medical University, Hefei, China
| | - Jian Cheng
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, China; Anhui Province Key Laboratory of Major Autoimmune Disease, Hefei, China; The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China; Anhui Public Health Clinical Center, Hefei, Anhui, China.
| | - Hao Zheng
- Department of Environmental Health, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, China.
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Revich B, Shaposhnikov D. The influence of heat and cold waves on mortality in Russian subarctic cities with varying climates. INTERNATIONAL JOURNAL OF BIOMETEOROLOGY 2022; 66:2501-2515. [PMID: 36198888 DOI: 10.1007/s00484-022-02375-2] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/30/2021] [Revised: 08/27/2022] [Accepted: 09/23/2022] [Indexed: 06/16/2023]
Abstract
Publications on ambient temperature-related mortality among Arctic or subarctic populations are extremely rare. While circumpolar areas cover large portions of several European countries, Canada, and the USA, the population of these territories is relatively small, and the data needed for statistical analysis of the health impacts of extreme temperature events are frequently insufficient. This study utilizes standard time series regression techniques to estimate relative increases in cause- and age-specific daily mortality rates during heat waves and cold spells in four Russian cities with a subarctic climate. The statistical significance of the obtained effect estimates tends to be greater in the continental climate than in the marine climate. A small meta-analysis was built around the obtained site-specific health effects. The effects were homogeneous and calculated for the selected weather-dependent health outcomes. The relative risks of mortality due to ischemic heart disease, all diseases of the circulatory system, and all non-accidental causes during cold spells in the age group ≥ 65 years were 1.20 (95% CI: 1.11-1.29), 1.14 (1.08-1.20), and 1.12 (1.07-1.17), respectively. Cold spells were more harmful to the health of the residents of Murmansk, Archangelsk, and Magadan than heat waves, and only in Yakutsk, heat waves were more dangerous. The results of this study can help the public health authorities develop specific measures for the prevention of excess deaths during cold spells and heat waves in the exposed subarctic populations.
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Affiliation(s)
- Boris Revich
- Laboratory of Forecasting of Environmental Quality and Public Health, Institute of Economic Forecasting of Russian Academy of Sciences, Nakhimovsky Prospect 47, Moscow, 117418, Russia
| | - Dmitry Shaposhnikov
- Laboratory of Forecasting of Environmental Quality and Public Health, Institute of Economic Forecasting of Russian Academy of Sciences, Nakhimovsky Prospect 47, Moscow, 117418, Russia.
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