2010～2016年我国（不含港澳台）梅毒时空分布及影响因素

Spatial-temporal distribution of syphilis and its influencing factors in China (excluding Hong Kong, Macao and Taiwan) from 2010 to 2016

目的探讨2010~2016年我国(不含港澳台)梅毒疫情的时空分布特征及社会影响因素。方法应用地理信息软件ArcGIS结合空间数据分析软件GeoDa对2010~2016年我国(不含港澳台)梅毒疫情的空间聚集状况进行分析,应用空间数据处理软件SaTScan对2010~2016年梅毒疫情进行回顾性时空扫描分析,应用Stata对梅毒疫情进行面板数据回归分析。结果全局空间自相关分析表明,2011~2015年我国(不含港澳台)梅毒发病无空间聚集性(Moran′s I=0.0444~0.1099,P> 0.05),但是,2010年(Moran′s I=0.2524,P <0.05)及2016年(Moran′s I=0.1932,P <0.05)我国(不含港澳台)梅毒发病存在空间聚集性。局部自相关分析结果表明,2010年江苏、上海和福建处于高-高区域,2013~2016年甘肃均处于低-高区域,2011~2012年我国(不含港澳台)均未处于高-高区域。回顾性时空扫描分析发现1个一级聚类区域(上海、浙江,RR=2.49)及6个二级聚类区域(广西、新疆、重庆、辽宁、内蒙及宁夏,RR=1.44~3.50)。面板数据回归分析结果表明,人均GDP和居民消费水平是梅毒发病的影响因素(P <0.05)。结论梅毒疫情具有明显的时空聚集特征,并且受社会人口因素的影响,探讨其时空聚集特征可为梅毒的防控策略和评价防控措施效果提供积极的参考。

Objective To explore spatial-temporal distribution characteristics of syphilis epidemic situation in China (excluding Hong Kong, Macao and Taiwan) from 2010 to 2016 and its social influencing factors. Methods Spatial aggregation of syphilis epidemic situation in China (excluding Hong Kong, Macao and Taiwan) from 2010 to 2016 was analyzed by using geographic information software ArcGIS and spatial data analysis software GeoDa. Spatial data processing software SaTScan was used to retrospectively analyze syphilis epidemic situation from 2010 to 2016. Stata was used to carry out panel data regression analysis of syphilis epidemic situation. Results Global spatial autocorrelation analysis showed that there was no spatial aggregation of syphilis incidence in China (excluding Hong Kong, Macao and Taiwan) from 2011 to 2015 (Moran′s I = 0.0444 to 0.1099, P > 0.05), however, in 2010 (Moran′s I = 0.2524, P < 0.05) and 2016 (Moran′s I = 0.1932, P < 0.05), syphilis incidence in China (excluding Hong Kong, Macao and Taiwan) was spatially aggregated. The results of local autocorrelation analysis showed that Jiangsu, Shanghai and Fujian were in the high-high region in 2010, Gansu was in the low-high region in 2013-2016, and all regions in China (excluding Hong Kong, Macao and Taiwan) were not in the high-high region in 2011-2012. Retrospective spatio-temporal scanning analysis revealed that there was one primary clustering region (Shanghai, Zhejiang, RR = 2.49) and six secondary clustering regions (Guangxi, Xinjiang, Chongqing, Liaoning, Inner Mongolia and Ningxia, RR = 1.44-3.50). The results of panel data regression analysis showed that per capita GDP and consumption level were the influencing factors of the incidence of syphilis (P < 0.05). Conclusion The epidemic situation of syphilis has obvious characteristics of spatial-temporal aggregation and is affected by social demographic factors. Discussing the characteristics of spatial-temporal aggregation of syphilis can provide a positive reference for syphilis control strategy and evaluation of the effect of prevention and control measures.