目的:分析2018~2022年海南岛肺结核报告发病率时空聚集性及其影响因素，探索海南岛肺结核高发的时间及地区，为科学制定结核病的防治措施提供理论依据。 方法:在中国疾病预防控制信息系统的传染病报告信息管理系统中，下载2018~2022年海南岛肺结核报告病例数据。采用ArcGIS 10.8软件对海南岛肺结核报告发病率的时空分布绘制环形地图，进行空间自相关分析和热点分析。采用SaTScan10.1.2软件进行时空扫描分析。采用地理探测器对肺结核年均报告发病率进行分异及因子探测和交互作用探测分析。 结果:2018~2022年海南岛肺结核报告发病率呈逐年降低趋势（χ² = 437.00，P < 0.001），全岛年均报告发病率为87.39/10万。全局空间自相关分析显示，Moran's I值均大于0，但无统计学意义（Z<1.96，P>0.05），呈现随机分布。局部空间自相关及热点分析显示，海南岛肺结核热点区域分布在乐东县、陵水县、万宁市、琼中县、五指山市、保亭县和三亚市。时空扫描显示肺结核报告发病率存在明显的空间聚集性，共检测到3个时空聚集区，集中在海南岛中南部地区，差异具有统计学意义（P<0.05）。因子探测结果显示，各因子q值从高到低排序为O₃（0.703）、PM₁₀（0.543）、PM_2.5（0.396）、人均GDP（0.359）、CO（0.309）、人口密度（0.226）、SO₂（0.178）、NO₂（0.172）。交互作用探测显示O₃与人口密度的交互作用对肺结核空间分层性的解释力最高（0.894）。肺结核发病的空间分异不是由单因子决定的，更大程度取决于多因子的综合作用。 结论:2018~2022年海南岛肺结核报告发病率存在明显的时空聚集性，分布在黎苗族地区及中南部山区。暴露于大气污染物、经济状况欠发达地区、流动人口集中地区等因素影响肺结核的发病率。提示应完善肺结核防治体系建设，加强结核病防治知识教育活动。建立流动人口数据库及信息共享平台，掌握肺结核流行态势。设立肺结核监测哨点，重点关注少数民族及流动人口集中地区，加强结核病监测力度。

Objective:To analyze the spatial‑temporal clustering and influencing factors of reported incidence of tuberculosis （TB） in Hainan Island from 2018 to 2022， explore the high‑risk time and areas of TB in Hainan Island， and provide theoretical basis for scientific prevention and control measures of TB. Methods:From the Infectious Disease Reporting Information Management System of China Disease Prevention and Control Information System， the data of reported cases of TB in Hainan Island from 2018 to 2022 were downloaded. ArcGIS 10.8 software was used to draw the ring map of the spatial‑temporal distribution of TB in Hainan Island， and spatial autocorrelation analysis and hot spot analysis were conducted. Software SaTScan10.1.2 was used for spatial‑temporal scan analysis. Factor detection and interaction detection were used to analyze the annual reported incidence of TB by geographical detector. Results:The reported incidence of TB in Hainan Island showed a downward trend from 2018 to 2022 （χ=437.00， P<0.001）， and the average annual reported incidence rate was 87.39/100 000. Global spatial autocorrelation analysis showed that the Moran's I values were greater than 0， but it was not statistically significant （Z<1.96， P>0.05）， which showed a random distribution. Local spatial autocorrelation and hot spot analysis showed that the hot spots of TB in Hainan Island were distributed in Ledong， Lingshui， Wanning， Qiongzhong， Wuzhishan， Baoting， and Sanya. Spatial and temporal scanning showed that there were obvious spatial clustering areas of reported incidence of TB， and 3 spatial‑temporal clustering areas were detected， with statistical significance （P<0.05）， which were concentrated in the central and southern areas of Hainan Island. The results of factor detection showed that the q value of each factor from high to low was O₃ （0.703）， PM₁₀ （0.543）， PM_2.5 （0.396）， per capita GDP （0.359）， CO （0.309）， population density （0.226）， SO₂ （0.178）， NO₂ （0.172）. The interaction between O₃ and population density had the highest explanatory power （0.894） on the spatial stratification of TB. The spatial variation of TB incidence was not determined by a single factor， but depended more on the comprehensive effect of multiple factors. Conclusion:The reported incidence of TB in Hainan Island from 2018 to 2022 has an obvious spatial‑temporal clustering， which is distributed in Li and Miao ethnic areas and the central and southern mountainous areas. Exposure to air pollutants， less developed areas， and concentrated floating population affect the incidence of TB. The construction of TB prevention and control system should be improved，and the education activities of TB prevention and control knowledge should be strengthened. It is necessary to establish a database and information sharing platform for floating population to grasp the epidemic situation of TB. In order to strengthen TB surveillance， TB surveillance sentinel sites should be set up， focusing on the minority and floating population concentrated areas.

海南省重点研发计划项目（ZDYF2021GXJS018）