Yang Zhengyu, Huang Wenzhong, McKenzie Joanne E, Xu Rongbin, Yu Pei, Wu Yao, Liu Yanming, Wen Bo, Zhang Yiwen, Yu Wenhua, Ye Tingting, Zhang Yuxi, Ju Ke, Hales Simon, Coelho Micheline de Sousa Zanotti Stagliorio, Matus Patricia, Tantrakarnapa Kraichat, Guo Yue Leon, Kliengchuay Wissanupong, Lavigne Eric, Phung Dung, Saldiva Paulo Hilario Nascimento, Guo Yuming, Li Shanshan
Climate Air Quality Research Unit, School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria Australia.
Methods in Evidence Synthesis Unit, School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria Australia.
Nat Water. 2025;3(5):561-570. doi: 10.1038/s44221-025-00425-8. Epub 2025 Apr 8.
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.
已观测到强度和频率前所未有的洪水。然而,关于洪水对住院影响的证据仍然有限。在此,我们收集了2000年至2019年期间澳大利亚、巴西、加拿大、智利、新西兰、中国台湾地区、泰国和越南747个社区的每日住院人数。对于每个社区,洪水日被定义为从洪水事件开始日期到结束日期的天数。使用具有分布滞后非线性函数的准泊松回归模型估计每个社区洪水日与每日住院风险之间的滞后响应关联。然后使用随机效应荟萃分析汇总特定社区的估计值。基于汇总估计值,计算了洪水导致的住院归因比例。我们发现,暴露于洪水后,住院风险增加并持续长达210天,所有原因导致的总体相对风险为1.26(95%置信区间1.15 - 1.38),心血管疾病为1.35(1.21 - 1.50),呼吸系统疾病为1.30(1.13 - 1.49),传染病为1.26(1.10 - 1.44),消化系统疾病为1.30(1.17 - 1.45),精神障碍为1.11(0.98 - 1.25),糖尿病为1.61(1.39 - 1.86),伤害为1.35(1.21 - 1.50),癌症为1.34(1.21 - 1.48),神经系统疾病为1.34(1.20 - 1.50),肾脏疾病为1.40(1.22 - 1.60)。这些关联因气候类型、洪水严重程度、年龄、人口密度和社会经济地位而有所不同。洪水暴露导致所有原因导致的住院率最高增加0.27%。这项研究表明,暴露于洪水后,在长达210天内,全因及十种特定病因的住院风险均增加。