文章摘要
陈可,虞浩,竺丽梅,刘巧,王蓓.江苏省2011-2021年病原学阳性肺结核时空分布特征分析[J].中华流行病学杂志,2024,45(4):513-519
江苏省2011-2021年病原学阳性肺结核时空分布特征分析
Spatial-temporal distribution characteristics of etiologically positive pulmonary tuberculosis in Jiangsu Province from 2011 to 2021
收稿日期:2023-09-15  出版日期:2024-04-17
DOI:10.3760/cma.j.cn112338-20230915-00161
中文关键词: 病原学阳性  结核,肺  时空分布
英文关键词: Etiologically positive  Tuberculosis, pulmonary  Spatial-temporal distribution
基金项目:国家自然科学基金(82003516);江苏省卫生健康委员会科研面上项目(M2020020)
作者单位E-mail
陈可 东南大学公共卫生学院, 南京 210009  
虞浩 江苏省疾病预防控制中心慢性传染病防治所, 南京 210009  
竺丽梅 江苏省疾病预防控制中心慢性传染病防治所, 南京 210009  
刘巧 江苏省疾病预防控制中心慢性传染病防治所, 南京 210009 liuqiaonjmu@163.com 
王蓓 东南大学公共卫生学院, 南京 210009 wangbeilxb@seu.edu.cn 
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中文摘要:
      目的 以县(市/区)为单位,分析2011-2021年江苏省病原学阳性肺结核时空分布特征,为江苏省肺结核防控策略的实施和调整提供科学依据。方法 利用中国疾病预防控制信息系统中的结核病管理信息系统收集2011-2021年江苏省病原学阳性肺结核患者登记数据。各县(市/区)常住人口数据来自于各设区(市)各年份江苏省统计年鉴。通过Geoda 1.18.0软件进行全局、局部空间自相关分析,探索其空间聚集性。采用SaTScan 10.1软件进行时空扫描统计分析探讨时空聚集性,利用ArcGIS 10.7软件进行可视化作图。结果 2011-2021年江苏省共登记病原学阳性肺结核病例128 240人,年均登记率为13.99/10万,2017年后登记率整体呈上升趋势(趋势χ2=63.49,P<0.001),病原学阳性率整体呈上升趋势(趋势χ2=3 710.86,P<0.001)。各年度Moran's I值为0.107~0.343,整体呈空间聚集性分布。局部空间自相关分析结果显示,江苏省病原学阳性肺结核登记率各年均存在“高-高”聚集区,呈动态分布,多分布在江苏省中部及南部地区,其中2015年最多(7个),2011年最少(1个)。时空扫描分析共探索到4个时空聚集区域(均P<0.001),其中一级聚集区覆盖3个县(市/区),分别为苏州市的常熟市、太仓市、相城区,聚集时间为2011-2015年;二级聚集区覆盖24个县(市/区),主要覆盖淮安市、宿迁市、盐城市等江苏省中、北部区域;三级聚集区覆盖26个县(市/区),四级聚集区为南京市高淳区,聚集时间为2017-2021年。结论 江苏省2011-2021年病原学阳性肺结核登记率在县(市/区)水平整体存在明显时空聚集特征,聚集区域不仅包含经济相对落后的苏北地区,也包含经济发展较好的苏南地区,应根据不同地区具体情况,多措并举,采取针对性肺结核防控措施。
英文摘要:
      Objective To analyze the spatial-temporal distribution of etiologically positive pulmonary tuberculosis (PTB) at the county (city, district) unit in Jiangsu Province from 2011 to 2021 to provide evidence for the implementation and adjustment of prevention and control strategies of PTB in Jiangsu Province. Methods The registration data of etiologically positive PTB patients in Jiangsu Province from 2011 to 2021 were collected from the Tuberculosis Management Information System in the China Information System of Disease Control and Prevention. Data on the permanent population were from the statistical yearbook of each county (city, district) in Jiangsu Province. Geoda 1.18.0 software was used to analyze the global and local spatial autocorrelation and explore the spatial clustering. SaTScan 10.1 software was used to analyze the spatial-temporal clusters, and ArcGIS 10.7 software was used to visualize the spatial-temporal clusters. Results A total of 128 240 etiological positive PTB cases were registered in Jiangsu Province from 2011 to 2021, with an average annual registration rate of 13.99/100 000. The registration rate showed an overall upward trend (trend χ2=63.49, P<0.001) after 2017, and the etiologically positive rate showed an overall upward trend (trend χ2=3 710.86, P<0.001). The annual Moran's I values ranged from 0.107 to 0.343, which showed a spatial clustering distribution. The results of local spatial autocorrelation analysis showed that there were "high-high" clustering areas in Jiangsu Province each year, showing a dynamic distribution, and most of the areas were distributed in the central and southern regions of Jiangsu Province, with the largest number (7) in 2015 and the smallest number (1) in 2011. A total of 4 spatial-temporal clustering areas were explored by spatial-temporal scanning analysis (all P<0.001), among which the first-level clustering area covered 3 counties (cities, districts), namely Changshu, Taicang, and Xiangcheng District of Suzhou, and the clustering time was from 2011 to 2015. The secondary clustering areas covered 24 counties (cities, districts), mainly covering Jiangsu's central and northern regions, such as Huai'an, Suqian, and Yancheng. The third-level clustering areas covered 26 counties (cities, districts); the fourth-level clustering area was the Gaochun District of Nanjing, with the clustering period from 2017 to 2021. Conclusions From 2011 to 2021, the etiologically positive PTB registration rate at the county (city, district) level in Jiangsu Province had obvious spatial-temporal clustering characteristics. The clustering areas included the northern areas with relatively backward economies and the southern areas with better economic development. Multiple measures should be taken to prevent and control PTB according to the specific situation in different regions.
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