江苏省2011-2021年病原学阳性肺结核时空分布特征分析

陈可; 虞浩; 竺丽梅; 刘巧; 王蓓

Abstract

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].Chinese journal of Epidemiology,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

Received:September 15, 2023

DOI：10.3760/cma.j.cn112338-20230915-00161

KeyWord: 病原学阳性结核，肺时空分布

English Key Word: Etiologically positive Tuberculosis, pulmonary Spatial-temporal distribution

FundProject: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）

Author Name	Affiliation	E-mail
Chen Ke	School of Public Health, Southeast University, Nanjing 210009, China
Yu Hao	Institute for Chronic Infectious Disease Prevention and Control, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing 210009, China
Zhu Limei	Institute for Chronic Infectious Disease Prevention and Control, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing 210009, China
Liu Qiao	Institute for Chronic Infectious Disease Prevention and Control, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing 210009, China	liuqiaonjmu@163.com
Wang Bei	School of Public Health, Southeast University, Nanjing 210009, China	wangbeilxb@seu.edu.cn

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Abstract:

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。

English Abstract:

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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