朱妮, 邱琳, 郁会莲, 张雪雷, 贠鹏飞. 2015年陕西省手足口病的空间聚集性及相关影响因素分析[J]. 疾病监测, 2017, 32(10/11): 818-823. DOI: 10.3784/j.issn.1003-9961.2017.10/11.009
引用本文: 朱妮, 邱琳, 郁会莲, 张雪雷, 贠鹏飞. 2015年陕西省手足口病的空间聚集性及相关影响因素分析[J]. 疾病监测, 2017, 32(10/11): 818-823. DOI: 10.3784/j.issn.1003-9961.2017.10/11.009
ZHU Ni, QIU Lin, YU Hui-lian, ZHANG Xue-lei, YUN Peng-fei. Spatial distribution of hand, foot and mouth disease and influence factors in Shaanxi province,2015[J]. Disease Surveillance, 2017, 32(10/11): 818-823. DOI: 10.3784/j.issn.1003-9961.2017.10/11.009
Citation: ZHU Ni, QIU Lin, YU Hui-lian, ZHANG Xue-lei, YUN Peng-fei. Spatial distribution of hand, foot and mouth disease and influence factors in Shaanxi province,2015[J]. Disease Surveillance, 2017, 32(10/11): 818-823. DOI: 10.3784/j.issn.1003-9961.2017.10/11.009

2015年陕西省手足口病的空间聚集性及相关影响因素分析

Spatial distribution of hand, foot and mouth disease and influence factors in Shaanxi province,2015

  • 摘要: 目的 运用空间统计学的方法描述2015年陕西省手足口病发病的空间分布特征,同时采用一般线性回归探讨2015年陕西省手足口病发病的相关影响因素。方法 收集《中国疾病控制信息系统》中2015年陕西省各个县(区)手足口病报告数据,采用Geoda 1.6.7软件进行空间全局和局部自相关分析,SaTScan 9.4.2软件进行空间扫描,分析结果使用ArcGIS 10.2软件进行可视化地图展示。线性回归采用Stata 12.0软件进行分析。结果 2015年陕西省手足口病报告发病率存在空间自相关性(P=0.001);局部空间自相关分析发现区域内存在高-高(或热点区域,主要分布于西安和咸阳部分县区)、低-低(或冷点区域,分布于榆林和延安地区)等关联模式的县(区);规则空间扫描结果显示手足口病发病共形成4个聚集区域,主要分布在西安、咸阳、渭南的部分县(区),与空间自相关分析结果基本一致。多因素线性回归分析结果提示,宏观经济指标和卫生系统指标与手足口病发病存在关联。具体来说,城乡收入比值(=0.264,P=0.001)越大手足口病发病率越高,然而当地卫生机构数(=-15.506,P=0.018)与床位数越多(=-5.108,P=0.029)则手足口病发病率越低。结论 2015年陕西省手足口病报告发病呈非随机分布,具有空间自相关性,西安市、咸阳市和渭南市为发病聚集区域,是手足口病的重点防控地区。为有效降低手足口病发病,政府有关部门应采取相关措施降低城乡收入差异,同时增加当地卫生机构数量及床位数。

     

    Abstract: Objective To understand the spatial distribution of hand, foot and mouth (HFMD) and related influence factors in Shaanxi province, and provide evidence for the prevention and control of HFMD. Methods The incidence data of HFMD in all counties in Shaanxi in 2015 were collected from National Disease Reporting Information System, the spatial autocorrelation was analyzed by using Geoda 1.6.7, and SaTScan 9.4.2 was used to conduct spatial scan statistics.In addition, ArcGIS 10.2 and Stata 12.0 were used to visualize and fit linear regression model respectively. Results The reported HFMD incidence in Shaanxi in 2015 had spatial autocorrelation statistically (P=0.001). The results from local spatial autocorrelation analysis identified high-high pattern area (parts of Xi'an and Xianyang) and low-low pattern area (Yulin and Yan'an). In addition, the results from SaTScan statistics analysis indicated four clustering areas in parts of Xi'an, Xianyang and Weinan respectively. The results of multivariate analysis demonstrated that the macro economy and health system were associated with incidence of HFMD. In detail, urban and rural income ratio was positively associated with the incidence of HFMD (=0.264,P=0.001), but more number of health facilities (=-15.506, P=0.018) and beds in hospitals (=-5.108, P=0.029) were negatively associated with the incidence of HFMD. Conclusion The HFMD cases were not distributed randomly in Shaanxi in 2015,the clustering of the disease were found in Xi'an, Xianyang and Weinan, indicating the prevention and control of HFMD should be strengthened in these areas. It is important to make effort to decrease urban and rural income difference and improve health facilities, including hospital beds, to reduce the incidence of HFMD in Shaanxi.

     

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