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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,Sa TScan 9.4.2软件进行空间扫描,分析结果使用Arc GIS 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(β=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(β=-15.506,P=0.018)与床位数越多(β=-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。
Objective To describe the spatial distribution characteristics of hand, foot and mouth disease in Shaanxi Province in 2015 by using the method of spatial statistics, and to explore the related factors of hand-foot-mouth disease in Shaanxi Province in 2015 by using general linear regression. Methods Hand-foot-mouth disease data of various counties (districts) in China’s Disease Control Information System in 2015 were collected. Geoda 1.6.7 software was used for spatial and local autocorrelation analysis. Sa TScan 9.4.2 software was used for spatial scanning, The results were visualized using Arc GIS 10.2 software. Linear regression was analyzed using Stata 12.0 software. Results There was a spatial autocorrelation between hand-foot-mouth disease reported in Shaanxi Province in 2015 (P = 0.001). Local autocorrelation analysis showed that there was a “high-high” (or hot spot) region in Xi’an and Xianyang Counties (districts), county cities (districts), low places (or cold spots) distributed in Yulin and Yan’an areas, and regular spatial scanning results showed that there were altogether 4 agglomeration areas Distribution in Xi’an, Xianyang, Weinan some counties (districts), and spatial autocorrelation analysis results are basically the same. The results of multivariate linear regression analysis suggested that macroeconomic indicators and indicators of the health system were associated with the incidence of HFMD. Specifically, the greater the ratio of urban-rural income (β = 0.264, P = 0.001), the higher the incidence of HFMD. However, the higher the number of local health institutions (β = -15.506, P = 0.018) 5.108, P = 0.029), then the incidence of HFMD was lower. Conclusion The incidence of HFMD in Shaanxi Province in 2015 was non-randomly distributed, with spatial autocorrelation. Xi’an, Xianyang and Weinan were the focal areas for disease incidence, which were the key prevention and control areas for HFMD. In order to effectively reduce the incidence of hand-foot-mouth disease, relevant government departments should take relevant measures to reduce urban-rural income disparity and increase the number of local health agencies and the number of beds.