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杨胜雄, 李军, 汪俊华, 冯军, 杨俊, 黄延, 张彬兵, 毛永佳, 梁祖花, 潘春柳. 基于空间统计学的贵阳市肺结核患者时空模式分析[J]. 疾病监测, 2022, 37(10): 1318-1323. DOI: 10.3784/jbjc.202205070200
引用本文: 杨胜雄, 李军, 汪俊华, 冯军, 杨俊, 黄延, 张彬兵, 毛永佳, 梁祖花, 潘春柳. 基于空间统计学的贵阳市肺结核患者时空模式分析[J]. 疾病监测, 2022, 37(10): 1318-1323. DOI: 10.3784/jbjc.202205070200
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Yang Shengxiong, Li Jun, Wang Junhua, Feng Jun, Yang Jun, Huang Yan, Zhang Binbing, Mao Yongjia, Liang Zuhua, Pan Chunliu. Spatiotemporal pattern of pulmonary tuberculosis patients in Guiyang based on spatial statistics[J]. Disease Surveillance, 2022, 37(10): 1318-1323. DOI: 10.3784/jbjc.202205070200
Citation: Yang Shengxiong, Li Jun, Wang Junhua, Feng Jun, Yang Jun, Huang Yan, Zhang Binbing, Mao Yongjia, Liang Zuhua, Pan Chunliu. Spatiotemporal pattern of pulmonary tuberculosis patients in Guiyang based on spatial statistics[J]. Disease Surveillance, 2022, 37(10): 1318-1323. DOI: 10.3784/jbjc.202205070200
shu

基于空间统计学的贵阳市肺结核患者时空模式分析

Spatiotemporal pattern of pulmonary tuberculosis patients in Guiyang based on spatial statistics

    摘要
  • 摘要:
      目的  以乡镇/街道为研究尺度，分析2010—2020年贵州省贵阳市肺结核病登记病例的时空分布模式，为贵阳市制定精细化的肺结核防控措施提供科学依据。
      方法  采用空间统计学的方法，利用ArcGIS 10.8软件对贵阳市肺结核疫情进行三维空间趋势面分析、全局自相关分析、聚类和异常值分析，并使用SaTScan v10.0.2软件分析时空分布特征。
      结果  2010—2020年贵阳市各乡镇/街道的肺结核年均登记率在南北方向上呈“南低北高”，东西方向上呈“U”形的趋势；历年全局Moran's I系数介于0.26～0.75之间（P＜0.01），呈空间聚集性；“高–高”聚集区和“低–高”异常值区域主要分布于“三县一市”，“高–低”异常值区域和“低–低”聚集区主要分布于市辖区；时空扫描分析结果显示，共发现8个时空聚集区（P＜0.05）。
      结论  贵阳市肺结核患者在乡镇/街道水平上存在显著的时空聚集性，2010—2020年肺结核发病的热点区域发生变化，从“三县一市”逐渐转移至市辖区，应对不同的地区采取针对性的措施，加强监督管理，以有效控制疾病的发生。

     

    Abstract:
      Objective  To analyzes the spatiotemporal distribution model of registered pulmonary tuberculosis (TB) cases at the township/street level in Guiyang, Guizhou province, from 2010 to 2020, and provide evidence for the improvement of local pulmonary TB prevention and control measures.
      Methods  Using the method of spatial statistics, the three-dimensional spatial trend surface analysis, global autocorrelation analysis, clustering and abnormal value, the analysis on the prevalence of pulmonary TB in Guiyang were carried out by software ArcGIS 10.8, and the spatiotemporal distribution of pulmonary TB cases were analyzed by software SaTScan v10.0.2.
      Results  From 2010 to 2020, the annual registration rate of pulmonary TB was low in the south and high in the north, and showed an “U” from east to west in Guiyang. The annual global Moran's I coefficient was between 0.26 and 0.75 (P<0.001), showing spatial clustering. The areas with “high-high” and abnormal “low-high” clustering were mainly distributed in Kaiyang, Xifeng, Xiuwen and Qingzhen, and the areas with abnormal “high-low” and “low-low” clustering were mainly distributed in urban area. The results of spatiotemporal scanning analysis showed that eight spatiotemporal clustering areas were found (P<0.05).
      Conclusion  There were significant spatiotemporal distribution characteristics of pulmonary TB cases at the township/street level in Guiyang. The hot spots of pulmonary TB gradually changed from Kaiyang, Xifeng, Xiuwen and Qingzhen to urban area during 2010−2020. Targeted measures should be taken to strengthen supervision and management of pulmonary TB cases in different areas for the effective control of pulmonary TB.

     

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