title: Climate variations and bacillary dysentery in northern and southern cities of China.
abstract: OBJECTIVE
  This paper was aimed at examining the relationship between meteorological variables and bacillary dysentery in different climatic and geographic areas in China.
  
  
  METHODS
  Jinan in northern China, with a temperate climate, and Baoan in southern China, with a subtropical climate were chosen as study areas. Spearman correlations and seasonal Autoregressive Integrated Moving Average (SARIMA) models were used to quantify the association between meteorological variables and dysentery. The Hockey Stick model was used to explore the threshold of the effect of temperatures.
  
  
  RESULTS
  Maximum temperature, minimum temperature, rainfall, relative humidity and air pressure were significantly correlated with the incidence of dysentery in the both cities, with lag effects varying from zero to two months. In the SARIMA models, maximum and minimum temperatures were significantly associated with dysentery transmission. The thresholds for the effects of maximum and minimum temperatures were 17 degrees C and 8 degrees C, respectively, in the northern city. No thresholds were detected in the southern city.
  
  
  CONCLUSIONS
  Climate variations have different impacts on the transmission of bacillary dysentery in temperate and subtropical cities in China. Public health action should be taken at this stage to reduce future risks of climate change with consideration of local climatic conditions.
authors.affiliation: Department of Public Health, University of Adelaide, Level 9, 10 Pulteney Street, Adelaide, SA5005, Australia. ying.zhang@adelaide.edu.au
authors.forename: Ying
authors.initials: Y
authors.lastname: Zhang
authors.forename: Peng
authors.initials: P
authors.lastname: Bi
authors.forename: Janet E
authors.initials: JE
authors.lastname: Hiller
authors.forename: Yuwei
authors.initials: Y
authors.lastname: Sun
authors.forename: Philip
authors.initials: P
authors.lastname: Ryan
mesh_terms: D002681:China / Q000453:epidemiology; D002980:Climate; D004405:Dysentery, Bacillary / Q000453:epidemiology*; D006801:Humans; D011159:Population Surveillance / Q000379:methods*; D012044:Regression Analysis