Division of Biostatistics, JC School of Public Health and Primary Care, The Chinese University of Hong Kong, Prince of Wales Hospital, Shatin, New Territories, Hong Kong.
Zhejiang Province Centre for Disease Control and Prevention, 3399 Binsheng Road, Binjiang District, Hangzhou, Zhejiang 310051, China.
Sci Total Environ. 2018 Dec 10;644:696-709. doi: 10.1016/j.scitotenv.2018.06.390. Epub 2018 Jul 11.
Since the first reported human infection with an avian-origin influenza A (H7N9) virus in China in early 2013, there have been recurrent outbreaks of the virus in the country. Previous studies have shown that meteorological factors are associated with the risk of human infection with the virus; however, their possible nonlinear and lagged effects were not commonly taken into account.
To quantify the effect of meteorological factors on the risk of human H7N9 infection, daily laboratory-confirmed cases of human H7N9 infection and meteorological factors including total rainfall, average wind speed, average temperature, average relative humidity, and sunshine duration of the 11 sub-provincial/prefecture cities in Zhejiang during the first four outbreaks (13 March 2013-30 June 2016) were analyzed. Separate models were built for the 6 sub-provincial/prefecture cities with the greatest number of reported cases using a combination of logistic generalized additive model and distributed lag nonlinear models, which were then pooled by a multivariate meta-regression model to determine their overall effects.
According to the meta-regression model, for rainfall, the log adjusted overall cumulative odds ratio was statistically significant when log of rainfall was >4.0, peaked at 5.3 with a value of 12.42 (95% confidence intervals (CI): [3.23, 21.62]). On the other hand, when wind speed was 2.1-3.0 m/s or 6.3-7.1 m/s, the log adjusted overall cumulative odds ratio was statistically significant, peaked at 7.1 m/s with a value of 6.75 (95% CI: [0.03, 13.47]). There were signs of nonlinearity and lag effects in their associations with the risk of infection.
As rainfall and wind speed were found to be associated with the risk of human H7N9 infection, weather conditions should be taken into account when it comes to disease surveillance, allowing prompt actions when an outbreak takes place.
自 2013 年初中国首次报告有人感染甲型禽流感(H7N9)病毒以来,该病毒在国内反复爆发。既往研究表明,气象因素与人类感染该病毒的风险有关;然而,其可能存在的非线性和滞后效应并未得到普遍考虑。
为了量化气象因素对人类 H7N9 感染风险的影响,分析了 2013 年 3 月 13 日至 2016 年 6 月 30 日期间浙江省 11 个地级市首次发生的 4 次疫情中,实验室确诊的人类 H7N9 感染病例及总降雨量、平均风速、平均气温、平均相对湿度和日照时间等气象因素。对报告病例数最多的 6 个地级市分别建立了模型,采用逻辑广义加性模型和分布式滞后非线性模型相结合的方法,并通过多元荟萃回归模型进行汇总,以确定其总体影响。
根据荟萃回归模型,对于降雨量,当降雨量的对数大于 4.0 时,对数调整后的总累积优势比具有统计学意义,峰值为 5.3,值为 12.42(95%置信区间[3.23,21.62])。另一方面,当风速为 2.1-3.0 m/s 或 6.3-7.1 m/s 时,对数调整后的总累积优势比具有统计学意义,峰值出现在 7.1 m/s,值为 6.75(95%置信区间[0.03,13.47])。它们与感染风险之间存在非线性和滞后效应的迹象。
由于降雨量和风速与人类 H7N9 感染风险有关,在进行疾病监测时应考虑天气条件,以便在疫情发生时及时采取行动。