Spatiotemporal distribution and meteorological factors of hemorrhagic fever with renal syndrome in Hubei province.

IF 3.4 2区 医学 Q1 PARASITOLOGY PLoS Neglected Tropical Diseases Pub Date : 2024-11-04 DOI:10.1371/journal.pntd.0012498
Hang Li, Rui Yang, Xuhua Guan, Xiaobo Huang, Honglin Jiang, Liangfei Tan, Jinfeng Xiong, Mingjun Peng, Tianbao Zhang, Xuan Yao
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Abstract

Background: Hemorrhagic fever with renal syndrome (HFRS) is a vital rodent-borne disease, and poses a serious public health threat in Hubei province. We aimed to explore the spatiotemporal distribution of HFRS in Hubei province during 2005-2022, and the effects of meteorological factors.

Methods: Data on HFRS cases at the county level in Hubei province during 2005-2022 were obtained from the Chinese Center for Disease Prevention and Control Information System. The monthly meteorological data at the city level was extracted from the China Meteorological Data Sharing Service System from 2016 to 2020. Descriptive analyses, joinpoint regression model, spatial correlation analyses, Geodetector model and autoregressive integrated moving average (ARIMA) model were conducted to investigate the epidemic characteristics, temporal trend, spatial distribution, influencing factors of HFRS and predict its trend.

Results: A total of 6,295 cases were reported in Hubei province during 2005-2022, with an average incidence of 6/1,000,000. Most cases were males (74.52%) and aged 40-69 years (71.87%). The monthly HFRS cases showed two seasonal peaks, which were summer (May to June) and winter (November to December). The HFRS incidence remained fluctuating at a low level during 2005-2015, followed an increasing trend during 2015-2018, and then decreased during 2018-2022. Hotspots were concentrated in the center of Hubei province in all 3 periods, including Qianjiang, Tianmen and some counties from Xiangyang, Jingmen and Jingzhou cities. The distribution of HFRS had a positive association with wind speed, while a "V"-shaped correlation with mean temperature, with an explanatory power of 3.21% and 1.03% respectively (both P <0.05). The ARIMA model predicted about 1,223 cases occurred in the next 3 years.

Conclusions: HFRS cases showed seasonal fluctuation and spatial clustering in Hubei province. Central plain areas showed high risk of HFRS. Wind speed and mean temperature had significant effects on the transmission of HFRS in Hubei province. The results alert health authorities to conduct disease-climate surveillance and comprehensive prevention strategies, especially in high-risk counties.

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湖北省出血热合并肾综合征的时空分布和气象因素。
背景:出血热合并肾综合征(HFRS)是一种重要的鼠源性疾病,对湖北省的公共卫生构成严重威胁。我们旨在探讨 2005-2022 年间湖北省肾综合征出血热的时空分布以及气象因素的影响:2005-2022年湖北省县级HFRS病例数据来自中国疾病预防控制中心信息系统。市级月气象数据来自中国气象数据共享服务系统2016-2020年的数据。通过描述性分析、连接点回归模型、空间相关性分析、Geodetector模型和自回归整合移动平均(ARIMA)模型,研究HFRS的流行特征、时间趋势、空间分布、影响因素并预测其流行趋势:2005-2022年间,湖北省共报告了6 295例病例,平均发病率为6/1 000 000。大多数病例为男性(74.52%),年龄在 40-69 岁之间(71.87%)。每月的 HFRS 病例呈现两个季节性高峰,即夏季(5 月至 6 月)和冬季(11 月至 12 月)。2005-2015年间,HFRS发病率在低位波动,2015-2018年间呈上升趋势,2018-2022年间有所下降。3个时期的热点地区均集中在湖北省中部,包括潜江市、天门市以及襄阳市、荆门市和荆州市的部分县市。HFRS的分布与风速呈正相关,与平均气温呈 "V "形相关,解释力分别为3.21%和1.03%(均为P结论):HFRS病例在湖北省呈现季节性波动和空间聚集性。中部平原地区是 HFRS 的高发区。风速和平均气温对湖北省 HFRS 的传播有显著影响。这些结果提醒卫生部门开展疾病气候监测和综合预防策略,尤其是在高风险县。
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PLoS Neglected Tropical Diseases
PLoS Neglected Tropical Diseases PARASITOLOGY-TROPICAL MEDICINE
自引率
10.50%
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723
期刊介绍: PLOS Neglected Tropical Diseases publishes research devoted to the pathology, epidemiology, prevention, treatment and control of the neglected tropical diseases (NTDs), as well as relevant public policy. The NTDs are defined as a group of poverty-promoting chronic infectious diseases, which primarily occur in rural areas and poor urban areas of low-income and middle-income countries. Their impact on child health and development, pregnancy, and worker productivity, as well as their stigmatizing features limit economic stability. All aspects of these diseases are considered, including: Pathogenesis Clinical features Pharmacology and treatment Diagnosis Epidemiology Vector biology Vaccinology and prevention Demographic, ecological and social determinants Public health and policy aspects (including cost-effectiveness analyses).
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