摘要
目的了解2017—2019年四川省肺结核年均登记率的时空分布特征,为制定防控策略提供科学依据。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2017—2019年四川省183个县(区)肺结核患者登记信息和人口信息。利用地理信息系统(GIS)软件ArcGIS 10.3的自然断点分级法和空间自相关分析方法(包括全局自相关分析法和局部自相关分析法,检验水准a均为0.05),时空扫描软件SaTScan 9.4.1的时空扫描方法(检验水准a=0.05),分析各县(区)肺结核登记率时空分布特征。结果四川省3年肺结核患者平均登记率为55.45/10万,最高地区分布在北偏西部、中南部的14个县(区),最低地区分布在中部地区的39个县(区)。全局莫兰指数为0.305 8,P值为<0.001,提示整体存在正向空间自相关,呈聚集分布,而不是随机分布。局部自相关分析结果显示,置信区间在95%及以上认为是热点的地区有23个县(区),分布在北偏西部和中南部,认为是冷点的地区有74个县(区),分布在中部。时空扫描分析探测到9个有统计学意义的肺结核高发时空聚集区,其中1级聚集区位于以凉山州美姑县为中心的12个县(区)(分布在中南部),2级聚类区位于以阿坝州壤塘县为中心的6个县(区)(分布在北偏西部)。结论 2017—2019年四川省肺结核登记率呈现较明显的时空聚集性,形成肺结核的"热点"区域,即北偏西部和中南部地区,肺结核的防控应重点关注上述地区。
Objective To understand the spatial-temporal distribution about annual average registered incidence of pulmonary tuberculosis(TB) cases in Sichuan Province from 2017 to 2019, in order to provide scientific evidence for strategies of pulmonary TB prevention and control. Methods The registration data and demographic data of the whole population and pulmonary TB cases in 183 counties(districts) of Sichuan from 2017 to 2019 were collected from Tuberculosis Management Information System of National Disease Prevention and Control Information System. The method of Natural breakout classification and spatial autocorrelation analysis(including global autocorrelation analysis and local autocorrelation analysis with a test level of ɑ=0.05) were performed by Software ArcGIS 10.3 of the geographic information system and the method of temporal-spatial scan(test level ɑ=0.05) was used by Software SaTScan 9.4.1. Based on the methods,the study analyzed the spatial-temporal distribution characteristics of annual average registered incidence of pulmonary TB cases of different counties. Result The average registration rate of pulmonary TB in Sichuan in 3 years was 55.45/100 000. The regions with the highest registration rate were distributed in the north west and south central areas, including 14 counties, and those with the lowest registration rate were distributed in the central areas,including 39 counties. The global Moran’s I was 0.305 8(P<0.001), which indicated the positive spatial autocorrelation of the whole distribution and the aggregated distribution, rather than random distribution. Local autocorrelation analysis showed that there were 23 counties should be considered as hot spots in confidence interval 95% or above,which distributed in the north west and south central areas, and 74 counties should be considered as cold spots, which distributed in the central areas. Spatial temporal scanning analysis found 9 accumulation areas with statistical significance as high incidence areas of pulmonary TB. Among them, Class Ⅰ clustering regions located in 12 counties(districts) centered on Meigu County of Liangshan Prefecture, distributed in south central areas. Class Ⅱ clustering regions located in 6 counties(districts) centered on Rangtang County of Aba Prefecture, distributed in the north west areas. Conclusions The Obvious spatial temporal clustering of pulmonary TB registration rate was found in Sichuan from 2017 to 2019, with hot spots in the north west and south central areas.Pulmonary TB prevention and control should focus on these areas.
作者
夏勇
夏岚
陈闯
李婷
逯嘉
王丹霞
XIA Yong;XIA Lan;CHEN Chuang;LI Ting;LU Jia;WANG Dan-xia(Sichuan Center for Disease Control and Prevention,Chengdu 610041,Sichuan Province,China)
出处
《预防医学情报杂志》
CAS
2022年第3期307-313,318,共8页
Journal of Preventive Medicine Information
关键词
肺结核
空间自相关
时空扫描
时空聚集
pulmonary tuberculosis
spatial autocorrelation
temporal-spatial scan
temporal-spatial cluster
