文章摘要
李婷,杨长虹,何金戈,李运葵,肖月,李京,王丹霞,陈闯,吴建林.四川省凉山彝族自治州2011-2016年痰涂片阳性肺结核疫情时空分布特征[J].中华流行病学杂志,2017,38(11):1518-1522
四川省凉山彝族自治州2011-2016年痰涂片阳性肺结核疫情时空分布特征
Spatial-temporal distribution of smear positive pulmonary tuberculosis in Liangshan Yi autonomous prefecture, Sichuan province, 2011-2016
收稿日期:2017-04-17  出版日期:2017-11-11
DOI:10.3760/cma.j.issn.0254-6450.2017.11.016
中文关键词: 肺结核;痰涂片阳性;空间自相关;时空扫描;时空聚集
英文关键词: Tuberculosis;Smear positive;Spatial autocorrelation;Temporal-spatial scan;Temporal-spatial cluster
基金项目:
作者单位E-mail
李婷 610041 成都, 四川省疾病预防控制中心结核病预防控制所  
杨长虹 610041 成都, 四川省疾病预防控制中心公共卫生信息所  
何金戈 610041 成都, 四川省疾病预防控制中心结核病预防控制所 Hejinge@163.com 
李运葵 610041 成都, 四川省疾病预防控制中心结核病预防控制所  
肖月 610041 成都, 四川省疾病预防控制中心结核病预防控制所  
李京 610041 成都, 四川省疾病预防控制中心结核病预防控制所  
王丹霞 610041 成都, 四川省疾病预防控制中心结核病预防控制所  
陈闯 610041 成都, 四川省疾病预防控制中心结核病预防控制所  
吴建林 610041 成都, 四川省疾病预防控制中心中心办公室  
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中文摘要:
      目的 分析2011-2016年四川省凉山彝族自治州(凉山州)痰涂片阳性肺结核(涂阳肺结核)登记的时空分布特征。方法 数据来源于中国疾病预防控制信息系统结核病管理信息系统,整理2011-2016年凉山州618个乡镇(街道)涂阳肺结核患者登记信息和人口数据,采用ArcGIS 10.2软件构建地理信息数据库、实现分析结果的可视化,采用OpenGeoda 1.2.0软件做全局与局部空间自相关分析,采用SaTScan 9.4.1软件做时空扫描分析。结果 2011-2016年凉山州涂阳肺结核登记率总体呈下降趋势,从56.97/10万(2 666例)下降至21.11/10万(1 038例)。全局空间自相关分析结果显示,各年份Moran's I值在0.25~0.45之间(P值均为0.000),局部自相关分析结果显示,2011-2016年分别有43、34、37、34、42和61个乡镇(街道)处于"高-高"聚集区,以雷波县所辖的乡镇居多。时空扫描分析探测到1个一级聚类区(聚集中心为美姑县巴古乡)和2个二级聚类区(聚集区中心分别为会理县六民乡和河口乡),P值均为0.000。结论 2011-2016年凉山州涂阳肺结核疫情呈现较明显的时空聚集性,形成结核病高发区域即"热点"区域,主要位于凉山州东北部的雷波县和美姑县的部分乡镇。应确定重点乡镇,实施结核病的精准防控策略。
英文摘要:
      Objective To analyze the spatial and temporal distribution of smear positive pulmonary tuberculosis (PTB) in Liangshan Yi autonomous prefecture in Sichuan province from 2011 to 2016. Methods The registration data of PTB in 618 townships of Liangshan from 2011 to 2016 were collected from "Tuberculosis Management Information System of National Disease Prevention and Control Information System". Software ArcGIS 10.2 was used to establish the geographic information database and realize the visualization of the analysis results. Software OpenGeoda 1.2.0 was used to conduct the analyses on global indication of spatial autocorrelation (GISA) and local indication of spatial autocorrelation (LISA). Software SaTScan 9.4.1 was used for spatio-temporal scanning analysis. Results From 2011 to 2016, the registration rate of smear positive PTB in Liangshan declined from 56.97/100 000 (2 666 cases) to 21.11/100 000 (1 038 cases). The global spatial autocorrelation coefficient Moran's I ranged from 0.25 to 0.45 and the difference was significant (all P=0.000). Local autocorrelation analysis showed that "high-high" area covered 43, 34, 37, 34, 42 and 61 townships from 2011 to 2016, respectively, mainly in Leibo county. Spatial temporal clustering analysis found one class Ⅰ clustering in the area around Bagu township of Meigu county and two class Ⅱ clustering in the areas around Liumin and Hekou township of Huili county, respectively (all P=0.000). Conclusion Obvious spatial temporal clustering of smear positive PTB distribution was found in Liangshan from 2011-2016. Hot spot areas with serious smear positive PTB epidemic and high spread risk were mainly found in northeastern Liangshan, including townships in Leibo and Meigu counties. Targeted TB prevention and control should be conducted in these areas.
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