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Association between floods and infectious diarrhea and their effect modifiers in Hunan province, China: A two-stage model
- Liu, Zhidong, Zhang, Feifei, Zhang, Ying, Li, Jing, Liu, Xuena, Ding, Guoyong, Zhang, Caixia, Liu, Qiyong, Jiang, Baofa
- The Science of the total environment 2018 v.626 pp. 630-637
- climate change, diarrhea, disease surveillance, floods, meteorological data, meteorological parameters, models, notifiable disease, public health, risk, risk reduction, China
- Understanding the potential links between floods and infectious diarrhea is important under the context of climate change. However, little is known about the risk of infectious diarrhea after floods and what factors could modify these effects in China.This study aims to quantitatively examine the relationship between floods and infectious diarrhea and their effect modifiers.Weekly number of infectious diarrhea cases from 2004 to 2011 during flood season in Hunan province were supplied by the National Notifiable Disease Surveillance System. Flood and meteorological data over the same period were obtained. A two-stage model was used to estimate a provincial average association and their effect modifiers between floods and infectious diarrhea, accounting for other confounders.A total of 134,571 cases of infectious diarrhea were notified from 2004 to 2011. After controlling for seasonality, long-term trends, and meteorological factors, floods were significantly associated with infectious diarrhea in the provincial level with a cumulative RR of 1.22 (95% CI: 1.05, 1.43) with a lagged effect of 0–1 week. Geographic locations and economic levels were identified as effect modifiers, with a higher impact of floods on infectious diarrhea in the western and regions with a low economic level of Hunan.Our study provides strong evidence of a positive association between floods and infectious diarrhea in the study area. Local control strategies for public health should be taken in time to prevent and reduce the risk of infectious diarrhea after floods, especially for the vulnerable regions identified.