Then, the local health authority must report these cases to the next level of the organization within 24 h.23 Therefore, it is believed that the degree of compliance in disease notification over the study period was consistent. The Yearbooks of Meteorological Disasters in check details China recorded the occurrence, deaths, damage area and economic loss of floods in detail from 2004 to 2009.24 According to the Yearbooks of

Meteorological Disasters in China, there were seven times of floods recorded in Kaifeng and Xinxiang from 2004 to 2009, which was less than that of Zhengzhou with nine times of floods. Flooding per se would be a variable depending on the quantitation over a shorter period time than a month. But in our study, we analyzed monthly data to assess the effects of floods on the find more dysentery disease on the basis of a time series data from 2004 to 2009, which included flooded months, non-flooded months, pre-flooded and post-flooded months, and the same period over other years, so monthly data would estimate the effects of floods well. Demographic data were obtained from the Center

for Public Health Science Data in China (http://www.phsciencedata.cn/). Monthly meteorological data were obtained from the China Meteorological Data Sharing Service System (http://cdc.cma.gov.cn/). The meteorological variables included monthly cumulative precipitation (MCP), monthly average temperature (MAT), monthly average relative humidity (MARH) and monthly cumulative sunshine duration (MCSD). Firstly, a descriptive analysis was performed to describe the distribution

of dysentery SPTLC1 cases and meteorological factors between the flooded and nonflooded months through the Kruskal–Wallis H test. Spearman correlation was adopted to examine the association between floods, climatic variables and the morbidity of dysentery with various lagged values in each city. The lagged value with the maximum correlation coefficient for each climate variable was selected for inclusion in the subsequent regression models. According to the reproducing of pathogen and the incubation period of dysentery disease, a time lag of 0–2 months was considered in this study.25 The widely used generalized additive models (GAM) method is a flexible and effective technique for conducting nonlinear regression analysis in time-series studies with a Poisson regression.26 GAM allows this Poisson regression to be fit as a sum of nonparametric smooth functions of predictor variables. The purpose of GAM is to maximize the predictive quality of a dependent variable, “Y” from various distributions by estimating archetypical function of the predictor variables that connected to the dependent variable. In time-series studies of air pollution and mortality, GAM has been the most widely applied method, because it allows for nonparametric adjustment for nonlinear confounding effects of seasonality, trends, and weather variables.