从六气角度探讨北京市痢疾发病与气象变动的关联性

Correlation between incidence of dysentery and climate changes in Beijing from the perspective of six-qi

目的:从运气学说六气的角度探讨北京市痢疾发病与前1年及当年气象因素的相关性。方法:按六气分段收集和整理北京市1970年-2003年的痢疾发病及相应的气象资料,分别将前1年和当年的气象资料与痢疾发病人数进行单因素相关分析,将相关性较高的气象因子纳入多元逐步回归分析,建立回归模型,并评价其对痢疾发病预测的吻合度。结果:北京地区痢疾的发病集中于夏秋之季。痢疾发病与当年和前1年的气温、风速和相对湿度均相关,但仅有风速进入回归方程,回归方程分别为:Y=-171226.89+86883.679Xw.1(Xw.1为当年初之气的平均风速);Y=-291070.976+62177.339Xw.1+62776.246Xw.2(Xw.1和Xw.2分别为前1年初之气和二之气的平均风速)。结论:前1年气象与痢疾发病的回归方程的预测效果较当年为佳。

Objective： To explore the correlation between the incidence of dysentery and climate changes（including the climates of corresponding years and the previous years） from the perspective of six climatic factors doctrine in traditional Chinese medicine（TCM） doctrine of five evolutive phases and six climatic factors.Methods： The cases of dysentery and meteorological data in Beijing from 1970 to 2003 were collected.All the data were categorized according to the six climatic factors stage and calculated by the correlation analysis method.The multiple stepwise regression was used to analyze the correlation between the meteorological factors and dysentery.A regression equation was created and tested.Results： The dysentery incidence was higher during summer and autumn.The incidence of dysentery was closely related with temperature,wind speed and relative humidity,of which wind speed was selected to the regression equation.The regression equation of dysentery and the corresponding years＇ meteorological factors was Y=-171226.89＋86883.679 Xw.1（Xw.1 is the wind speed of 1st stage of climatic factors in the corresponding year）.The regression equation of dysentery and the previous meteorological factors was Y=-291070.976＋62177.339 Xw＇.1＋62776.246 Xw＇.2（Xw＇.1 is the wind speed of 1st stage of qi,and Xw＇.2 is the wind speed of 2nd stage of climatic factors）.Conclusion： Regression equation of the previous year was better than the current years＇ in predicting the prevalence of dysentery.It was feasible to predict the prevalence of dysentery by using the meteorological data in previous year.