Zhengyu Yang, Wenzhong Huang, Joanne E McKenzie, Rongbin Xu, Pei Yu, Yao Wu, Yanming Liu, Bo Wen, Yiwen Zhang, Wenhua Yu, Tingting Ye, Yuxi Zhang, Ke Ju, Simon Hales, Micheline de Sousa Zanotti Stagliorio Coelho, Patricia Matus, Kraichat Tantrakarnapa, Yue Leon Guo, Wissanupong Kliengchuay, Eric Lavigne, Dung Phung, Paulo Hilario Nascimento Saldiva, Yuming Guo, Shanshan Li
{"title":"Hospitalization risks associated with floods in a multi-country study.","authors":"Zhengyu Yang, Wenzhong Huang, Joanne E McKenzie, Rongbin Xu, Pei Yu, Yao Wu, Yanming Liu, Bo Wen, Yiwen Zhang, Wenhua Yu, Tingting Ye, Yuxi Zhang, Ke Ju, Simon Hales, Micheline de Sousa Zanotti Stagliorio Coelho, Patricia Matus, Kraichat Tantrakarnapa, Yue Leon Guo, Wissanupong Kliengchuay, Eric Lavigne, Dung Phung, Paulo Hilario Nascimento Saldiva, Yuming Guo, Shanshan Li","doi":"10.1038/s44221-025-00425-8","DOIUrl":null,"url":null,"abstract":"<p><p>Floods of unprecedented intensity and frequency have been observed. However, evidence regarding the impacts of floods on hospitalization remains limited. Here we collected daily hospitalization counts during 2000-2019 from 747 communities in Australia, Brazil, Canada, Chile, New Zealand, Taiwan, Thailand and Vietnam. For each community, flooded days were defined as days from the start dates to the end dates of flood events. Lag-response associations between flooded day and daily hospitalization risks were estimated for each community using a quasi-Poisson regression model with a distributed lag nonlinear function. The community-specific estimates were then pooled using a random-effects meta-analysis. Based on the pooled estimates, attributable fractions of hospitalizations due to floods were calculated. We found that hospitalization risks increased and persisted for up to 210 days after flood exposure, with the overall relative risks being 1.26 (95% confidence interval 1.15-1.38) for all causes, 1.35 (1.21-1.50) for cardiovascular diseases, 1.30 (1.13-1.49) for respiratory diseases, 1.26 (1.10-1.44) for infectious diseases, 1.30 (1.17-1.45) for digestive diseases, 1.11 (0.98-1.25) for mental disorders, 1.61 (1.39-1.86) for diabetes, 1.35 (1.21-1.50) for injury, 1.34 (1.21-1.48) for cancer, 1.34 (1.20-1.50) for nervous system disorders and 1.40 (1.22-1.60) for renal diseases. The associations were modified by climate types, flood severity, age, population density and socioeconomic status. Flood exposure contributed to hospitalizations by up to 0.27% from all causes. This study revealed that flood exposure was associated with increased all-cause and ten cause-specific hospitalization risks within up to 210 days after exposure.</p>","PeriodicalId":74252,"journal":{"name":"Nature water","volume":"3 5","pages":"561-570"},"PeriodicalIF":0.0000,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12098117/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Nature water","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1038/s44221-025-00425-8","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/4/8 0:00:00","PubModel":"Epub","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Floods of unprecedented intensity and frequency have been observed. However, evidence regarding the impacts of floods on hospitalization remains limited. Here we collected daily hospitalization counts during 2000-2019 from 747 communities in Australia, Brazil, Canada, Chile, New Zealand, Taiwan, Thailand and Vietnam. For each community, flooded days were defined as days from the start dates to the end dates of flood events. Lag-response associations between flooded day and daily hospitalization risks were estimated for each community using a quasi-Poisson regression model with a distributed lag nonlinear function. The community-specific estimates were then pooled using a random-effects meta-analysis. Based on the pooled estimates, attributable fractions of hospitalizations due to floods were calculated. We found that hospitalization risks increased and persisted for up to 210 days after flood exposure, with the overall relative risks being 1.26 (95% confidence interval 1.15-1.38) for all causes, 1.35 (1.21-1.50) for cardiovascular diseases, 1.30 (1.13-1.49) for respiratory diseases, 1.26 (1.10-1.44) for infectious diseases, 1.30 (1.17-1.45) for digestive diseases, 1.11 (0.98-1.25) for mental disorders, 1.61 (1.39-1.86) for diabetes, 1.35 (1.21-1.50) for injury, 1.34 (1.21-1.48) for cancer, 1.34 (1.20-1.50) for nervous system disorders and 1.40 (1.22-1.60) for renal diseases. The associations were modified by climate types, flood severity, age, population density and socioeconomic status. Flood exposure contributed to hospitalizations by up to 0.27% from all causes. This study revealed that flood exposure was associated with increased all-cause and ten cause-specific hospitalization risks within up to 210 days after exposure.