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西班牙巴塞罗那2000 - 2011年百日咳再现的回顾性时空聚集分析

Retrospective space-time cluster analysis of whooping cough, re-emergence in Barcelona, Spain, 2000-2011.

作者信息

Solano Rubén, Gómez-Barroso Diana, Simón Fernando, Lafuente Sarah, Simón Pere, Rius Cristina, Gorrindo Pilar, Toledo Diana, Caylà Joan A

出版信息

Geospat Health. 2014 May;8(2):455-61. doi: 10.4081/gh.2014.34.

Abstract

A retrospective, space-time study of whooping cough cases reported to the Public Health Agency of Barcelona, Spain between the years 2000 and 2011 is presented. It is based on 633 individual whooping cough cases and the 2006 population census from the Spanish National Statistics Institute, stratified by age and sex at the census tract level. Cluster identification was attempted using space-time scan statistic assuming a Poisson distribution and restricting temporal extent to 7 days and spatial distance to 500 m. Statistical calculations were performed with Stata 11 and SatScan and mapping was performed with ArcGis 10.0. Only clusters showing statistical significance (P <0.05) were mapped. The most likely cluster identified included five census tracts located in three neighbourhoods in central Barcelona during the week from 17 to 23 August 2011. This cluster included five cases compared with the expected level of 0.0021 (relative risk = 2436, P <0.001). In addition, 11 secondary significant space-time clusters were detected with secondary clusters occurring at different times and localizations. Spatial statistics is felt to be useful by complementing epidemiological surveillance systems through visualizing excess in the number of cases in space and time and thus increase the possibility of identifying outbreaks not reported by the surveillance system.

摘要

本文呈现了一项对2000年至2011年间向西班牙巴塞罗那公共卫生机构报告的百日咳病例的回顾性时空研究。该研究基于633例个体百日咳病例以及西班牙国家统计局2006年的人口普查数据,按普查区层面的年龄和性别进行分层。尝试使用时空扫描统计量识别聚集性,假定为泊松分布,将时间范围限制为7天,空间距离限制为500米。使用Stata 11和SatScan进行统计计算,使用ArcGis 10.0进行绘图。仅绘制显示统计学显著性(P<0.05)的聚集性。识别出的最可能的聚集性包括2011年8月17日至23日当周位于巴塞罗那市中心三个街区的五个普查区。该聚集性包括5例病例,而预期水平为0.0021(相对风险=2436,P<0.001)。此外,还检测到11个次要的显著时空聚集性,次要聚集性出现在不同时间和地点。空间统计被认为是有用的,通过在时空上可视化病例数的超额情况来补充流行病学监测系统,从而增加识别监测系统未报告的疫情的可能性。

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