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目的探讨北京地区猩红热发病与六气及气象因子的相关性。方法收集北京地区1970—2004年共35年的猩红热发病人数及相对应气象资料,包括日平均气温、日平均降雨量、日平均相对湿度、日平均水汽压、日平均风速,按六气分段并进行描述性分析和相关分析,筛选相关性高的气象因子,建立多元逐步回归方程,并对方程进行检验。结果猩红热的发病集中于冬春之季(初之气、二之气和终之气),与气温(初之气、二之气、三之气、四之气、五之气和终之气)、风速(初之气、二之气、五之气、终之气)、相对湿度(初之气、三之气、四之气)共13个气象因子的相关性具有统计学意义,初之气的气温和五之气的风速进入回归方程。猩红热的发病与前1年初之气的气温呈负相关,与前1年五之气的风速呈正相关。回归方程:Y=-914.932-351.455X1+1 351.195X2(X1为前1年初之气的平均气温,X2为前1年五之气的平均风速)对猩红热流行的预测具有较好的效果。结论猩红热的发病与六气和气象密切相关,利用前1年不同时段的气象变化规律可以预测后1年猩红热的发病情况。
Objective To investigate the correlation between the incidence of scarlet fever and six gas and meteorological factors in Beijing. Methods The incidence of scarlet fever in Beijing from 1970 to 2004 was collected and the corresponding meteorological data were collected including daily average temperature, daily average rainfall, daily average relative humidity, daily average vapor pressure, daily mean wind speed, Descriptive analysis and correlation analysis were conducted to screen meteorological factors with high relativity, establish multiple stepwise regression equations and test the equations. Results The incidence of scarlet fever concentrated in the winter season (early gas, second gas and final gas), and the temperature (first gas, two gas, three gas, four gas, five gas and the final gas ), Wind speed (early gas, second gas, five gas, the final gas), relative humidity (gas early, gas three, gas four) a total of 13 meteorological factors of relevance, The temperature of the gas and the velocity of the five gases enter the regression equation. The incidence of scarlet fever was negatively correlated with the temperature of the gas at the beginning of the previous year, and positively correlated with the wind speed of the fifth year of the previous year. Regression equation: Y = -914.932-351.455X1 + 1 351.195X2 (X1 is the mean temperature of the early 1 year and X2 is the mean wind speed of the previous 1 year), which has a good effect on the prediction of the scarlet fever. Conclusions The incidence of scarlet fever is closely related to the six-gas and meteorological phenomena. The use of the meteorological changes in different periods of the previous year can predict the incidence of scarlet fever in the next year.