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山东大学学报 (医学版) ›› 2021, Vol. 59 ›› Issue (2): 108-113.doi: 10.6040/j.issn.1671-7554.0.2020.1105

• 公共卫生与管理学 • 上一篇    下一篇

日喀则市2011至2018年肺结核空间流行特征及预测分析

张倍1,2,张修磊3, 巴桑片多2,尼玛次仁2,石大春2,次仁加布2,尹亭亭1,胡军4,5   

  1. 1. 潍坊医学院公共卫生学院公共卫生学系, 山东 潍坊 261053;2. 日喀则市疾病预防控制中心, 西藏 日喀则 857000;3. 山东省胸科医院结核内科, 山东 济南 250101;4. 山东省疾病预防控制中心, 山东 济南 250014;5. 国家卫生健康委员会卫生经济与政策研究重点实验室(山东大学), 山东 济南 250012
  • 发布日期:2021-03-05
  • 通讯作者: 胡军. E-mail:sunnyhj@163.com
  • 基金资助:
    西藏自治区自然科学基金(XZ2017ZRG-83)

Spatial epidemiological characteristics and prediction of tuberculosis in Shigatse City from 2011 to 2018

ZHANG Bei1,2, ZHANG Xiulei3, BASANG Pianduo2, NIMA Ciren2, SHI Dachun2, CIREN Jiabu2, YIN Tingting1, HU Jun4,5   

  1. 1. Department of Public Health, School of Public Health, Weifang Medical University, Weifang 261053, Shandong, China;
    2. Shigatse Center for Disease Control and Prevention, Shigatse 857000, Tibet, China;
    3. Tuberculosis Medicine, Shandong Provincial Chest Hospital, Jinan 250101, Shandong, China;
    4. Shandong Center for Disease Control and Prevention, Jinan 250014, Shandong, China;
    5. Key Laboratory of Health Economics and Policy Research, National Health Commission(Shandong University), Jinan 250012, Shandong, China
  • Published:2021-03-05

摘要: 目的 分析2011至2018年日喀则市肺结核的空间分布和传播特征,发现其分布、聚集情况与热点区域,为肺结核防控提供科学依据。 方法 运用空间自相关分析、热点分析和Kriging插值预测对日喀则市2011至2018年肺结核发病状况进行空间分布描述、分析和预测。 结果 日喀则市2011至2018年肺结核高发病率地区主要分布在江孜县、康马县、拉孜县、萨迦县;低发病率地区主要分布在仲巴县、萨嘎县、南木林县。全局自相关Morans I指数为0.331 2,Z=2.65,P=0.008 3。局部自相关LISA聚集图显示,高-高聚集区域为江孜县、白朗县;低-低聚集区域为萨嘎县。热点分析结果相对局部自相关结果出现新冷点区域仲巴县。Kriging插值预测结果显示,未来将出现新的高风险区域拉孜县与新的低风险区域南木林县、亚东县及其邻近县区,交叉评价指标结果M-PE=0.003 2、MS-PE=0.038 2、RMSS-PE=0.962 2、RMS-PE=0.063 3、ASE-PE=0.066 5。 结论 2011至2018年日喀则市肺结核发病空间不均衡分布,全市肺结核疫情呈现正空间自相关,存在局部空间自相关,热点与冷点并存,并且未来高发病风险区域与低发病风险区域将持续扩大,应有针对性开展防控工作。

关键词: 肺结核, 地理信息系统, 热点分析, 预测

Abstract: Objective To analyze the spatial distribution and transmission characteristics of tuberculosis in Shigatse City from 2011 to 2018, explore the distribution and aggregation conditions and hot spots, provide scientific basis for prevention and control. Methods Spatial autocorrelation analysis, hotspot analysis and Kriging interpolation prediction were used to describe, analyze and predict the spatial distribution of tuberculosis incidence in Shigatse City from 2011 to 2018. Results From 2011 to 2018, the areas with high incidence of tuberculosis in Shigatse City were mainly distributed in Gyangzê County, Kangmar County, Lazi County and Saga. The low incidence areas were mainly distributed in Zhongba County, Saga County and Namling County. Global autocorrelation Morans I index is 0.331 2, Z=2.65, P=0.008 3. The local autocorrelation LISA aggregation map showed that the high-high aggregation areas were Gyangzê County and Bainang County. Low-low concentration area was Saga County. Compared with the local autocorrelation results, the hot spot analysis results showed a new cold point in Zhongba County. The Kriging interpolation prediction results showed that in the future, there would be a new high-risk area in Lazi County and new low-risk areas in Namling County, Yadong County and its neighboring counties. The cross-evaluation index results showed that M-PE=0.003 2, MS-PE=0.038 2, RSS-PE=0.962 2, RMS-PE=0.063 3, and ASE-PE=0.066 5. Conclusion From 2011 to 2018, the spatial distribution of tuberculosis incidence in Shigatse City is unbalanced, and the tuberculosis epidemic in Shigatse City presents positive spatial autocorrelation, with local spatial autocorrelation, hot spots and cold spots co-existing. In the future, areas with high risk and low risk will continue to expand. Therefore, targeted prevention and control work should be carried out.

Key words: Tuberculosis, Geographic information systems, Hot spot analysis, Prediction

中图分类号: 

  • R181.3
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