TY - JOUR AU - Timpka, Toomas AU - Spreco, Armin AU - Dahlström， Örjan AU -埃里克森，Olle AU - Gursky, Elin AU - Ekberg, Joakim AU - Blomqvist, Eva AU - Strömgren, Magnus AU - Karlsson, David AU -埃里克森，Henrik AU - Nyce, James AU - Hinkula, Jorma AU - Holm，Einar PY - 2014 DA - 2014年04月28日TI -电子卫生数据源在本地流感监测中的表现:一个5年开放队列研究JO - J医学互联网Res SP - e116 VL - 16 IS - 4kw -流感KW -传染病监测KW -互联网KW -电子卫生KW -谷歌流感趋势KW -远程护理呼叫中心KW -网站使用KW -开放队列设计KW -公共卫生AB -背景:全球对利用全民卫生信息系统(称为电子卫生资源)的综合征数据来改进传染病监测有着浓厚的兴趣。最近，人们强调了这些系统实现两个潜在冲突需求的必要性。首先，它们必须基于证据;其次，它们必须根据人口、生活方式和环境的多样性进行调整。目的:主要目的是检验来自谷歌流感趋势(GFT)、计算机支持的远程护理中心、卫生服务网站的数据与季节性和大流行性流感暴发期间流感发病率之间的相关性。次要目标是调查电子健康数据、媒体报道以及流行流感毒株与年龄相关人群免疫力之间的相互作用之间的关系。方法:采用开放式队列设计，在瑞典一个县(人口427,000)进行了一项为期五年的研究。从GFT、远程护理呼叫中心和当地卫生服务网站页面级访问收集综合征电子健康数据。 Data on mass media coverage of influenza was collected from the major regional newspaper. The performance of eHealth data in surveillance was measured by correlation effect size and time lag to clinically diagnosed influenza cases. Results: Local media coverage data and influenza case rates showed correlations with large effect sizes only for the influenza A (A) pH1N1 outbreak in 2009 (r=.74, 95% CI .42-.90; P<.001) and the severe seasonal A H3N2 outbreak in 2011-2012 (r=.79, 95% CI .42-.93; P=.001), with media coverage preceding case rates with one week. Correlations between GFT and influenza case data showed large effect sizes for all outbreaks, the largest being the seasonal A H3N2 outbreak in 2008-2009 (r=.96, 95% CI .88-.99; P<.001). The preceding time lag decreased from two weeks during the first outbreaks to one week from the 2009 A pH1N1 pandemic. Telenursing data and influenza case data showed correlations with large effect sizes for all outbreaks after the seasonal B and A H1 outbreak in 2007-2008, with a time lag decreasing from two weeks for the seasonal A H3N2 outbreak in 2008-2009 (r=.95, 95% CI .82-.98; P<.001) to none for the A p H1N1 outbreak in 2009 (r=.84, 95% CI .62-.94; P<.001). Large effect sizes were also observed between website visits and influenza case data. Conclusions: Correlations between the eHealth data and influenza case rates in a Swedish county showed large effect sizes throughout a five-year period, while the time lag between signals in eHealth data and influenza rates changed. Further research is needed on analytic methods for adjusting eHealth surveillance systems to shifts in media coverage and to variations in age-group related immunity between virus strains. The results can be used to inform the development of alert-generating eHealth surveillance systems that can be subject for prospective evaluations in routine public health practice. SN - 14388871 UR - //www.mybigtv.com/2014/4/e116/ UR - https://doi.org/10.2196/jmir.3099 UR - http://www.ncbi.nlm.nih.gov/pubmed/24776527 DO - 10.2196/jmir.3099 ID - info:doi/10.2196/jmir.3099 ER -