首页> 美国卫生研究院文献>International Journal of Environmental Research and Public Health >The Impacts of Climatic Factors and Vegetation on Hemorrhagic Fever with Renal Syndrome Transmission in China: A Study of 109 Counties
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The Impacts of Climatic Factors and Vegetation on Hemorrhagic Fever with Renal Syndrome Transmission in China: A Study of 109 Counties

机译:气候因素和植被对中国肾综合征出血热的影响:109个县的研究

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摘要

Hemorrhagic fever with renal syndrome (HFRS) is a rodent-borne infectious disease caused by hantaviruses. About 90% of global cases were reported in China. We collected monthly data on counts of HFRS cases, climatic factors (mean temperature, rainfall, and relative humidity), and vegetation (normalized difference vegetation index (NDVI)) in 109 Chinese counties from January 2002 to December 2013. First, we used a quasi-Poisson regression with a distributed lag non-linear model to assess the impacts of these four factors on HFRS in 109 counties, separately. Then we conducted a multivariate meta-analysis to pool the results at the national level. The results of our study showed that there were non-linear associations between the four factors and HFRS. Specifically, the highest risks of HFRS occurred at the 45th, 30th, 20th, and 80th percentiles (with mean and standard deviations of 10.58 ± 4.52 °C, 18.81 ± 17.82 mm, 58.61 ± 6.33%, 198.20 ± 22.23 at the 109 counties, respectively) of mean temperature, rainfall, relative humidity, and NDVI, respectively. HFRS case estimates were most sensitive to mean temperature amongst the four factors, and the lag patterns of the impacts of these factors on HFRS were heterogeneous. Our findings provide rigorous scientific support to current HFRS monitoring and the development of early warning systems.
机译:肾综合征出血热(HFRS)是由汉坦病毒引起的啮齿动物传播的传染病。全球约有90%的病例是在中国报告的。我们收集了2002年1月至2013年12月中国109个县的HFRS病例数,气候因素(平均温度,降雨量和相对湿度)和植被(归一化植被指数(NDVI))的月度数据。拟泊松回归和分布滞后非线性模型分别评估了这四个因素对109个县的HFRS的影响。然后,我们进行了多元荟萃分析,以汇总国家/地区一级的结果。我们的研究结果表明,这四个因素与HFRS之间存在非线性关联。具体而言,HFRS的最高风险发生在第45、30、20和80个百分位(在109个县中,平均偏差和标准偏差为10.58±4.52°C,18.81±17.82 mm,58.61±6.33%,198.20±22.23),平均温度,降雨量,相对湿度和NDVI)。在这四个因素中,HFRS病例估计值对平均温度最为敏感,并且这些因素对HFRS影响的滞后模式是异质的。我们的发现为当前的HFRS监测和预警系统的开发提供了严格的科学支持。

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