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利用北京市2009-2011年疾病数据和同期气象资料,从24节气的角度分析了北京市近年来上呼吸道感染、支气管炎和脑梗死的发病与流行时间变化特征,发现呼吸系统疾病的发病受干冷空气影响较大,体现了以冷效应为主的特征,春季此类疾病的发病人数明显减少;循环系统疾病发病峰值期的出现,主要是秋末冬初冷暖空气频繁交替所致,与气温的变化幅度与频次密切相关.两种疾病发病的气象成因有一定差异,建立了北京市相关天气敏感性疾病发病的逐月预报方程,分别进行了回代检验和试预报检验,结果表明,回代检验中3种疾病的逐月预报方程均较好的反映了当天的患病人数;试预报结果不如回代检验的结果,且呈现夏季暖湿天气条件下呼吸系统疾病发病人数最少,干冷的冬季及粉尘较多的春季为呼吸系统疾病流行高发期的季节变化特征.预报方程在描述呼吸系统疾病发病高峰期时在数值上存在一定偏差,说明发病高峰期并不仅仅与气象因子有关,可能还受环境、空气污染及社会因素等影响.构建的逐月预报方程充分考虑了疾病发病的滞后效应和周末效应,利用了扩展后的368个气象因子进行优化筛选,充分体现了主控因子的主导作用,能够对相关天气敏感性疾病发病情况做出较好预报.
Using the 2009-2011 disease data and meteorological data from 2009 to 2011, the characteristics of upper respiratory tract infection, bronchitis and cerebral infarction in Beijing in recent years were analyzed from the perspective of 24 solar terms, and the incidence of respiratory diseases was found to be dry and cold The impact of air is greater, which reflects the cold effect-based features, the incidence of such diseases in spring decreased significantly; peak incidence of circulatory system disease occurs, mainly due to frequent alternating cold and warm air autumn late winter, and the temperature changes Amplitude and frequency are closely related to the occurrence of the two diseases have some differences in the meteorological cause of the weather-related diseases in Beijing to establish a monthly prediction equation, respectively, were back to the test and forecast test, the results show that the back-generation test The monthly prediction equations of three kinds of diseases better reflect the prevalence of the same day; the test results are not as good as the results of the back-test and show the least incidence of respiratory diseases in summer in warm and humid weather, dry and cold winter The more dusty spring is the seasonal variation of the prevalence of respiratory diseases. The prediction equation describes the respiratory diseases The peak value of the disease there is a certain deviation, indicating that peak incidence is not only meteorological factors, may also be affected by the environment, air pollution and social factors, etc. The construction of the monthly forecast equation fully take into account the lag effect of disease onset And weekends effect, the optimized 368 meteorological factors were selected for screening, which fully reflected the leading role of the master factor and made a good forecast on the incidence of related weather-sensitive diseases.