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目的研究安徽省亳州市气象因素与疟疾发病的关系。方法收集亳州市2005—2011年疟疾发病数据及同期气象数据,拟合准泊松quasipoisson分布滞后非线性模型(DLNM),研究周平均温度、周平均湿度、周平均降雨量对疟疾发病的即时效应、滞后效应和累积效应。结果对周平均温度的即时效应分析显示,随着温度的升高(-5~30℃),疟疾的发病风险逐渐升高;温度越高,滞后效应的强度越大,最佳滞后时间约为1~3周;当温度为26℃且滞后时间为10周时疟疾发病的累积危险度最高,RR值为228.9(95%CI:8.0~6 547.9)。以降雨量0 mm为参照,降雨量的即时效应无统计学意义(P>0.05);但随着滞后天数的增加,累积效应先增加后减小,降雨量越大,最长滞后天数越短;当降雨量为30 mm且累积时间为6周时,疟疾发病的累积危险度最大,RR值为3.79(95%CI:1.38~8.49)。以最低相对湿度31%为参照,周平均相对湿度的即时效应无统计学意义(P>0.05);随着滞后时间的增加,疟疾发病的相对危险度呈先增加后减少的趋势,最长滞后期为10周,当滞后时间为4周时疟疾发病的相对危险度最大;随着相对湿度的增加,疟疾发病的累积相对危险度先增加后减少,当相对湿度为62%且滞后10周时的累积效应最大,累积相对危险度为513.58(95%CI:14.70~17 943.94)。结论气象因素如温度、湿度和降雨量对疟疾的发生均有影响,且有一定的滞后作用。
Objective To study the relationship between meteorological factors and the incidence of malaria in Bozhou City, Anhui Province. Methods The data of malaria incidence and meteorological data from 2005 to 2011 in Bozhou City were collected and fitted with quasipoisson distribution lag nonlinear model (DLNM) of quasi-Poisson pine. The effects of average weekly temperature, average weekly humidity and weekly average rainfall on the incidence of malaria were studied. , Lag effect and cumulative effect. Results The analysis of the instantaneous effect on the average weekly temperature showed that the incidence of malaria increased with increasing temperature (-5 ~ 30 ℃). The higher the temperature, the stronger the hysteresis effect and the best lag time was 1 to 3 weeks. When the temperature was 26 ℃ and the lag time was 10 weeks, the cumulative risk of malaria was the highest, with an RR of 228.9 (95% CI: 8.0-6 547.9). However, with the increase of lag days, the cumulative effect firstly increased and then decreased, and the larger the rainfall, the shorter the lag days; When the rainfall was 30 mm and the cumulative time was 6 weeks, the cumulative risk of malaria was the highest, with an RR of 3.79 (95% CI: 1.38 to 8.49). With the minimum relative humidity of 31% as a reference, there was no significant difference in the average weekly relative humidity (P> 0.05). With the increase of lag time, the relative risk of malaria increased first and then decreased, the longest lag The relative risk of malaria was the highest when the lag time was 4 weeks. With the increase of relative humidity, the cumulative relative risk of malaria increased at first and then decreased. When the relative humidity was 62% and lagged 10 weeks Had the highest cumulative effect with a cumulative relative risk of 513.58 (95% CI: 14.70 to 17 943.94). Conclusions Meteorological factors such as temperature, humidity and rainfall have an impact on the occurrence of malaria and have a certain lag effect.