山东省发热伴血小板减少综合征时空流行特征分析

目的 分析山东省发热伴血小板减少综合征(SFTS)的时空分布特征,为查找重点区域及聚集时间,采取针对性的干预措施,优化卫生资源配置提供科学依据。方法 基于2010-2016年山东省各县(市、区)网络报告的SFTS疫情数据,结合人口数据、地理数据,建立地理信息数据库,采用Open GeoDa 1.2.0软件进行空间自相关分析,采用SaTScan 9.4软件进行时空扫描聚类分析。结果 2010-2016年山东省累计报告SFTS病例2 319例,平均发病率为0.34/10万,累计报告死亡病例230例,平均病死率9.92%,发病数和发病率逐年增多。全局自相关分析显示,2012-2016年SFTS空间分布均具有自相关性,呈聚集性分布, Moran’s I值均为正值(P<0.05);局部自相关分析结果表明,高-高流行区主要位于淄博、泰安、莱芜、威海、烟台等市的相关县区。时空扫描分析发现3个时空聚集区域:1)2012年1月至2015年1月,以烟台市芝罘区为中心点,共覆盖16个县(市、区)(LLR=677.15,RR=11.58,P<0.001)。2)2013年1月至2016年12月,以泰安市新泰市为中心点,共覆盖6个县(市、区)(LLR=457.51,RR=9.25,P<0.001)。3) 2013年1月至2014年1月,以潍坊市安丘市为中心点,共覆盖28个县(市、区)(LLR=142.59, RR=4.97,P<0.001)。结论 山东省发热伴血小板减少综合征疫情分布存在明显的时空聚集特征,主要集中在泰安、莱芜、烟台、威海的相关县(市、区),是我省预防控制该病的重点区域。

Temporal-spatial Analysis of severe fever with thrombocytopenia syndrome (SFTS) in Shandong Province,China

To analyze the spatial and temporal characteristics of severe fever with thrombocytopenia syndrome (SFTS) in Shandong Province for providing scientific basis for the development of related regional public health strategies. Based on the information of SFTS cases derived from the National Disease Reporting Information System during 2010-2016 in Shandong Province, combined with population and geographical data, a geographic information database was established. The spatial autocorrelation analysis was carried out by OpenGeoDa 1.2.0 software, and space-time scan statistics method based on the Poisson Model for the space-time clusters analysis by SaTScan 9.4 software was used. The results showed that the incidence was evidently increased, a total of 2 319 cases of SFTS patients were reported from 2010-2016, with an average incidence rate and fatality rate of 0.34/100,000 and 9.92% respectively. The Global Moran’s I index was positive and increased from 2012 to 2016, showing that there was a positive correlation between space and the incidence of SFTS(P<0.05).The results of local autocorrelation analysis showed that the high-high(H-H) clustering areas of SFTS were mainly located in central and northeast districts of Shandong province. Spatio-temporal scanning analysis identified three clustering areas, the most likely cluster was existed in 16 counties (districts),which were mainly located in Yantai (12) and Weihai (4) (LLR=677.15, RR=11.58, P<0.001). All the H-H counties(districts) were included in the three cluster regions. In conclusion, the incidence of SFTS increased obviously in Shandong from 2010 to 2016. The incidence of SFTS in counties(districts) appeared clustering features in both dimensions of time and space. Prevention and control measures should be focused in key areas.