| 饶华祥,蔡芝锋,徐莉立,石燕.青海省2014-2016年肺结核空间分布特征及可视化分析[J].中华流行病学杂志,2018,39(3):347-351 |
| 青海省2014-2016年肺结核空间分布特征及可视化分析 |
| Spatial distribution characteristics of tuberculosis and its visualization in Qinghai province, 2014-2016 |
| 收稿日期:2017-08-19 出版日期:2018-03-21 |
| DOI：10.3760/cma.j.issn.0254-6450.2018.03.019 |
| 中文关键词: 肺结核 空间自相关 可视化 |
| 英文关键词: Tuberculosis Spatial autocorrelation Visualization |
| 基金项目:国家科技重大专项（2012ZX10004-208） |
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| 中文摘要: |
| 目的 分析青海省2014－2016年肺结核的空间分布模式，识别聚集区域，为防控策略和措施的制定提供参考依据。方法 从中国疾病预防控制信息系统收集2014－2016年青海省报告的临床诊断和实验室确诊肺结核病例，采用ArcGIS 10.2.2软件进行肺结核年报告发病率地图可视化展示、三维趋势分析和局部Getis-Ord Gi*空间自相关分析，运用OpenGeoDa 1.2.0软件计算全局Moran’s I空间自相关统计量，描述和分析2014－2016年青海省肺结核的空间分布规律以及高发病率热点地区。结果 2014－2016年青海省肺结核年报告发病率分别为101.16/10万、123.26/10万和128.70/10万，呈上升趋势（趋势χ2=187.21，P<0.001）。三维趋势分析显示青海省肺结核年报告发病率由北向南逐渐升高，东西方向呈明显的中间高两边低的弧形变化趋势。全局Moran’s I空间自相关分析显示各地区间肺结核年报告发病率呈中等强度的空间聚集性（Moran’s I值分别为0.631 3、0.605 4和0.587 3，P<0.001）。局部Gi*分析显示高发病率聚集区域主要集中在青海省西南部的玉树藏族自治州（玉树州）与果洛藏族自治州（果洛州）所辖的部分县（区），低发病率聚集区域集中在西宁市的湟中县、城东区和城北区以及海西蒙古族藏族自治州（海西州）的大柴旦行委，其余地区年报告发病率处于中等水平。结论 2014－2016年青海省肺结核疫情呈现上升趋势；地区间年报告发病率并非随机分布，呈明显的空间聚集性，玉树州和果洛州为高发病率重点防控区域；空间聚集性分析为全省肺结核防控措施的制定提供了重要线索和依据。 |
| 英文摘要: |
| Objective To analyze the spatial distribution of tuberculosis (TB) and identify the clustering areas in Qinghai province from 2014 to 2016, and provide evidence for the prevention and control of TB. Methods The data of pulmonary TB cases confirmed by clinical and laboratory diagnosis in Qinghai during this period were collected from National Disease Reporting Information System. The visualization of annual reported incidence, three-dimensional trend analysis and local Getis-Ord Gi* spatial autocorrelation analysis of TB were performed by using software ArcGIS 10.2.2, and global Moran's I spatial autocorrelation analysis were analyzed by using software OpenGeoDa 1.2.0 to describe and analyze the spatial distribution characteristics and high incidence areas of TB in Qinghai from 2014 to 2016. Results A total of 20 609 pulmonary TB cases were reported in Qinghai during this period. The reported incidences were 101.16/100 000, 123.26/100 000 and 128.70/100 000 respectively, an increasing trend with year was observed (trend χ2=187.21, P<0.001). The three-dimensional trend analysis showed that the TB incidence increased from northern area to southern area, and up-arch trend from the east to the west. Global Moran's I spatial autocorrelation analysis showed that annual reported TB incidence in different areas had moderate spatial clustering (Moran's I values were 0.631 3, 0.605 4, and 0.587 3, P<0.001). And local Gi* analysis showed that there were some areas with high TB incidences, such as 10 counties of Yushu and Guoluo prefectures (Gande, Banma and Dari counties, etc., located in the southwest of Qinghai), and some areas with low TB incidences, such as Huangzhong county, Chengdong district and Chengbei district of Xining city and Dachaidan county of Haixi prefecture, and the reported TB incidences in the remaining areas were moderate. Conclusion The annual reported TB incidence increased year by year in Qinghai from 2014 to 2016. The distribution of TB cases showed obvious spatial clustering, and Yushu and Guoluo prefectures were the key areas in TB prevention and control. In addition, the spatial clustering analysis could provide the important evidence for the development of TB prevention and control measures in Qinghai. |
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