title: Impacts of floods on dysentery in Xinxiang city, China, during 2004-2010: a time-series Poisson analysis.
abstract: BACKGROUND
  Xinxiang, a city in Henan Province, suffered from frequent floods due to persistent and heavy precipitation from 2004 to 2010. In the same period, dysentery was a common public health problem in Xinxiang, with the proportion of reported cases being the third highest among all the notified infectious diseases.
  
  
  OBJECTIVE
  We focused on dysentery disease consequences of different degrees of floods and examined the association between floods and the morbidity of dysentery on the basis of longitudinal data during the study period.
  
  
  METHODS
  A time-series Poisson regression model was conducted to examine the relationship between 10 times different degrees of floods and the monthly morbidity of dysentery from 2004 to 2010 in Xinxiang. Relative risks (RRs) of moderate and severe floods on the morbidity of dysentery were calculated in this paper. In addition, we estimated the attributable contributions of moderate and severe floods to the morbidity of dysentery.
  
  
  RESULTS
  A total of 7591 cases of dysentery were notified in Xinxiang during the study period. The effect of floods on dysentery was shown with a 0-month lag. Regression analysis showed that the risk of moderate and severe floods on the morbidity of dysentery was 1.55 (95% CI: 1.42-1.670) and 1.74 (95% CI: 1.56-1.94), respectively. The attributable risk proportions (ARPs) of moderate and severe floods to the morbidity of dysentery were 35.53 and 42.48%, respectively.
  
  
  CONCLUSIONS
  This study confirms that floods have significantly increased the risk of dysentery in the study area. In addition, severe floods have a higher proportional contribution to the morbidity of dysentery than moderate floods. Public health action should be taken to avoid and control a potential risk of dysentery epidemics after floods.
authors.affiliation: Department of Epidemiology and Health Statistics, School of Public Health, Shandong University, Jinan, People's Republic of China; Shandong University Climate Change and Health Center, Jinan, People's Republic of China.
authors.forename: Wei
authors.initials: W
authors.lastname: Ni
authors.affiliation: Department of Occupational and Environmental Health, School of Public Health, Taishan Medical College, Taian, People's Republic of China.
authors.forename: Guoyong
authors.initials: G
authors.lastname: Ding
authors.affiliation: Department of Epidemiology and Health Statistics, School of Public Health, Shandong University, Jinan, People's Republic of China; Shandong University Climate Change and Health Center, Jinan, People's Republic of China.
authors.forename: Yifei
authors.initials: Y
authors.lastname: Li
authors.affiliation: Department of Epidemiology and Health Statistics, School of Public Health, Shandong University, Jinan, People's Republic of China; Shandong University Climate Change and Health Center, Jinan, People's Republic of China.
authors.forename: Hongkai
authors.initials: H
authors.lastname: Li
authors.affiliation: Department of Epidemiology and Health Statistics, School of Public Health, Shandong University, Jinan, People's Republic of China; Shandong University Climate Change and Health Center, Jinan, People's Republic of China; bjiang@sdu.edu.cn.
authors.forename: Baofa
authors.initials: B
authors.lastname: Jiang
mesh_terms: D002681:China / Q000453:epidemiology; D016001:Confidence Intervals; D004190:Disasters; D004403:Dysentery / Q000453:epidemiology* / Q000209:etiology / Q000503:physiopathology; D055868:Floods; D006801:Humans; D008137:Longitudinal Studies; D016012:Poisson Distribution; D011159:Population Surveillance / Q000379:methods; D011634:Public Health; D012044:Regression Analysis; D013696:Temperature; D014504:Urban Health
keywords: Poisson regression; dysentery; floods; longitudinal analysis; relative risk
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