文章摘要
李学朝,李娟生,孟蕾,白亚娜,于德山,刘小宁,刘新凤,蒋小娟,任晓卫,杨筱婷,申希平,张继巍.甘肃省2009-2015年发热呼吸道症候群主要病原的Bayes判别分析法分类研究[J].中华流行病学杂志,2017,38(8):1094-1097
甘肃省2009-2015年发热呼吸道症候群主要病原的Bayes判别分析法分类研究
Study on the classification of dominant pathogens related to febrile respiratory syndrome,based on the method of Bayes discriminant analysis
收稿日期:2016-12-21  出版日期:2017-08-12
DOI:10.3760/cma.j.issn.0254-6450.2017.08.019
中文关键词: 发热呼吸道症候群;Bayes判别分析;病原体
英文关键词: Febrile respiratory syndrome;Bayes discriminate analysis;Pathogeny
基金项目:国家科技重大专项(2012ZX10004-208)
作者单位E-mail
李学朝 730000 兰州大学公共卫生学院流行病与卫生统计学研究所  
李娟生 730000 兰州大学公共卫生学院流行病与卫生统计学研究所 lijsh16@163.com 
孟蕾 730000 兰州, 甘肃省疾病预防控制中心 mleicdc@163.com 
白亚娜 730000 兰州大学公共卫生学院流行病与卫生统计学研究所  
于德山 730000 兰州, 甘肃省疾病预防控制中心  
刘小宁 730000 兰州大学公共卫生学院流行病与卫生统计学研究所  
刘新凤 730000 兰州, 甘肃省疾病预防控制中心  
蒋小娟 730000 兰州, 甘肃省疾病预防控制中心  
任晓卫 730000 兰州大学公共卫生学院流行病与卫生统计学研究所  
杨筱婷 730000 兰州, 甘肃省疾病预防控制中心  
申希平 730000 兰州大学公共卫生学院流行病与卫生统计学研究所  
张继巍 730000 兰州大学公共卫生学院流行病与卫生统计学研究所  
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中文摘要:
      目的 了解甘肃省发热呼吸道症候群主要病原体,构建区分主要病原感染病例的Bayes判别函数。方法 以2009-2015年甘肃省各哨点医院监测的发热呼吸道症候群病例为样本,描述其病原谱构成,确定主要病原;并在样本病例中筛选有意义的临床指标进行Bayes判别分析。结果 在发热呼吸道症候群病毒检测中,流感病毒和鼻病毒检测阳性率较高(13.79%和8.63%),分别占病毒总阳性例数的54.38%和13.73%;细菌中以肺炎链球菌和流感嗜血杆菌检测阳性率较高(44.41%和18.07%),分别占细菌总阳性例数的66.21%和24.55%。运用筛选后的11个临床指标建立判别函数,其初始验证正确率为73.1%、交叉验证为70.6%。结论 甘肃省发热呼吸道症候群主要病原体为流感病毒、鼻病毒、肺炎链球菌及流感嗜血杆菌;Bayes判别分析在主要病原感染的分类诊断中有较高的正确率,具有一定的应用价值。
英文摘要:
      Objective To understand the dominant pathogens of febrile respiratory syndrome (FRS) patients in Gansu province and to establish the Bayes discriminant function in order to identify the patients infected with the dominant pathogens. Methods FRS patients were collected in various sentinel hospitals of Gansu province from 2009 to 2015 and the dominant pathogens were determined by describing the composition of pathogenic profile. Significant clinical variables were selected by stepwise discriminant analysis to establish the Bayes discriminant function. Results In the detection of pathogens for FRS, both influenza virus and rhinovirus showed higher positive rates than those caused by other viruses (13.79%, 8.63%), that accounting for 54.38%, 13.73% of total viral positive patients. Most frequently detected bacteria would include Streptococcus pneumoniae, and haemophilus influenza (44.41%, 18.07%) that accounting for 66.21% and 24.55% among the bacterial positive patients. The original-validated rate of discriminant function, established by 11 clinical variables, was 73.1%, with the cross-validated rate as 70.6%. Conclusion Influenza virus, Rhinovirus, Streptococcus pneumoniae and Haemophilus influenzae were the dominant pathogens of FRS in Gansu province. Results from the Bayes discriminant analysis showed both higher accuracy in the classification of dominant pathogens, and applicative value for FRS.
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