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目的探讨深圳市流行性腮腺炎时空聚集性特征和变化趋势。方法应用回顾性时空重排扫描统计量和空间自相关系数分析深圳市2009年1月1日~2011年12月31日流行性腮腺炎时空聚集性,利用地理信息系统(geographic information system,GIS)技术表达相应的聚集区域。结果回顾性时空扫描分析确定了23个可能的发病聚集区域,一类聚集区域发生在2011年11月27日~12月26日,聚集范围包括沙头角和海山街道(P<1.0×10-17),二类聚集区域包括22个,2009年探测到6个,包括4个聚集区域和2个聚集点,2010年探测到7个,包括1个聚集区域和6个聚集点,2011年探测到9个聚集点。呈现大范围聚集区域减少,小范围的聚集点逐渐增多的趋势。23个聚集区主要出现在4~7月份和11~2月份,与深圳市和广东省腮腺炎疫情高峰出现时段相一致。局部自相关分析发现的高-高聚集区域为:2009年海山、沙头角、莲塘、南头和龙岗街道;2011年盐田、海山、平湖和福永街道,结果与时空扫描结果基本一致。结论时空重排扫描统计量结合地理信息系统,能够更加直观、全面地展示了发病聚集区域,为以后开展针对性的预防控制措施,提供了科学参考依据。
Objective To investigate the characteristics of spatial and temporal aggregation of mumps in Shenzhen and its changing trend. Methods The retrospective spatio-temporal rearrangement scan statistics and spatial autocorrelation coefficients were used to analyze spatial-temporal aggregation of mumps from January 1, 2009 to December 31, 2011. Using geographic information system (GIS) Technical expression of the corresponding gathering area. Results Retrospective spatio-temporal scanning analysis identified 23 possible areas of onset of aggregation, with a cluster of aggregates occurring from November 27 to December 26, 2011, with aggregates ranging from Sha Tau Kok and seamount streets (P <1.0 × 10-17 ), Including 22 for the second type of agglomeration, 6 for the year 2009, including 4 agglomerations and 2 agglomerations, 7 for 2010, including 1 agglomeration and 6 agglomerations, which were detected in 2011 9 gathering points. Showing a large area to reduce the aggregation area, the aggregation point of small-scale gradually increasing trend. The 23 gathering areas mainly appeared from April to July and from November to February, which was consistent with the peak period of mumps epidemic in Shenzhen and Guangdong Provinces. The high-high concentration areas discovered by the local autocorrelation analysis are: seamounts, Sha Tau Kok, Liantang, Nantou and Longgang streets in 2009; Yantian, seamounts, Pinghu and Fuyong streets in 2011. The results are basically the same as the results of space-time scanning. Conclusions Spatial and temporal rearrangement scan statistics combined with geographic information system can reveal the aggregation area more intuitively and comprehensively and provide scientific reference for future preventive and control measures.