The Impacts of Climatic Factors and Vegetation on Hemorrhagic Fever with Renal Syndrome Transmission in China: A Study of 109 Counties

被引:12
|
作者
He, Junyu [1 ]
Wang, Yong [2 ]
Mu, Di [3 ]
Xu, Zhiwei [4 ]
Qian, Quan [2 ]
Chen, Gongbo [5 ]
Wen, Liang [2 ]
Yin, Wenwu [3 ]
Li, Shanshan [5 ]
Zhang, Wenyi [2 ]
Guo, Yuming [5 ]
机构
[1] Zhejiang Univ, Ocean Coll, Zhoushan 316021, Peoples R China
[2] Chinese PLA Ctr Dis Control & Prevent, Beijing 100071, Peoples R China
[3] Chinese Ctr Dis Control & Prevent, Div Infect Dis, Key Lab Surveillance & Early Warning Infect Dis, Beijing 102206, Peoples R China
[4] Queensland Univ Technol, Sch Publ Hlth & Social Work, Inst Hlth & Biomed Innovat, Brisbane, Qld 4059, Australia
[5] Monash Univ, Sch Publ Hlth & Prevent Med, Dept Epidemiol & Prevent Med, Melbourne, Vic 3004, Australia
基金
中国国家自然科学基金;
关键词
orthohantavirus; hantavirus disease; risk map; distributed lag non-linear model; meta-analysis; HANTAAN VIRUS; HANTAVIRUS INFECTION; AMBIENT-TEMPERATURE; RODENT OUTBREAKS; VARIABILITY; PROVINCE; PRECIPITATION; ASSOCIATION; WEATHER; DISEASE;
D O I
10.3390/ijerph16183434
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
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 degrees 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.
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页数:13
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