赵啸, 刘民, 李松林, 代小秋, 李玉玲, 海山·卡德尔拜, 鲁炳怀, 朱凤霞, 赵旭. 发热门诊就诊患者的时空聚集性分析[J]. 疾病监测, 2013, 28(4): 256-259. DOI: 10.3784/j.issn.1003-9961.2013.4.003
引用本文: 赵啸, 刘民, 李松林, 代小秋, 李玉玲, 海山·卡德尔拜, 鲁炳怀, 朱凤霞, 赵旭. 发热门诊就诊患者的时空聚集性分析[J]. 疾病监测, 2013, 28(4): 256-259. DOI: 10.3784/j.issn.1003-9961.2013.4.003
ZHAO Xiao, LIU Min, LI Song-lin, DAI Xiao-qiu, LI Yu-ling, HAISHAN Kadeerbai, LU Bing-huai, ZHU Feng-xia, ZHAO Xu. Time-place clustering of outpatients in fieber illness clinic[J]. Disease Surveillance, 2013, 28(4): 256-259. DOI: 10.3784/j.issn.1003-9961.2013.4.003
Citation: ZHAO Xiao, LIU Min, LI Song-lin, DAI Xiao-qiu, LI Yu-ling, HAISHAN Kadeerbai, LU Bing-huai, ZHU Feng-xia, ZHAO Xu. Time-place clustering of outpatients in fieber illness clinic[J]. Disease Surveillance, 2013, 28(4): 256-259. DOI: 10.3784/j.issn.1003-9961.2013.4.003

发热门诊就诊患者的时空聚集性分析

Time-place clustering of outpatients in fieber illness clinic

  • 摘要: 目的 研究发热症状的时空分布特征,探索呼吸道传染病疫情的时空分布特点及时空聚集性特征,为呼吸道传染病的早期预警提供依据。 方法 利用北京市朝阳区一所三级甲等医院发热门诊所建立的症状监测系统,收集2009年4月1日00:00时至2010年3月31日24:00时期间的就诊患者的病历资料,应用回顾性时空重排扫描统计量方法进行时空扫描分析,并对其中流行性感冒(流感)样病例、流感患者、发热待查患者分别进行分析。 结果 以50%时间周期进行扫描,发热门诊就诊患者、流感样病例、流感患者、发热待查患者的分布均可探测到聚集区域,经检验差异有统计学意义,聚集区域集中在北京市朝阳区中部。流感样病例与流感患者聚集区域接近,区域中点距离为0.92 km,大部分重叠。 结论 对发热门诊就诊患者的时空聚集性探测,可以准确发现呼吸道传染病患者的聚集区域,有利于早期预警。发热门诊的症状监测系统对呼吸道传染病的防控具有较为重要的价值。

     

    Abstract: Objective To understand the time and place distribution of fieber illness clinic visits and provide evidence for the early warning of respiratory infectious disease. Methods Medical records of the patients visiting fieber illness clinics from 00:00 April 1, 2009 to 24:00 March 31, 2010 were collected through a syndrome surveillance system in a grade 3 general hospital in Chaoyang district in Beijing, and the time-place scan statistic analysis was conducted with retrospective time place permutation scan statistic method. Influenza-like cases, influenza cases and cases of fever with unknown origin were analyzed respectively. Results The analysis of 50% of study period found the clustering of outpatients visiting fieber illness clinic, ILI cases, influenza cases and the cases with unknown origin, the difference had statistical significance. The clustering were mainly in the central area of Chaoyang district. The clustering of ILI cases and influenza cases were close to each other, the distance between the central points of 2 clustering was 0.92 km and 2 clustering were overlapped mostly. Conclusion Time-place clustering analysis of fieber illness clinic visits can accurately find the significant clustering of respiratory disease patients, which is important for the early warning of the diseases. Syndrome surveillance in fieber illness clinic visits is valuable for the prevention and control of respiratory infectious disease.

     

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