@Article{信息:doi 10.2196 / / jmir。3099，作者="Timpka, Toomas and Spreco, Armin and Dahlstr{\"o}m， {\"o} jan and Eriksson, Olle and Gursky, Elin and Ekberg, Joakim and Blomqvist, Eva and Str{"o}mgren, Magnus and Karlsson, David and Eriksson, Henrik and Nyce, James and Hinkula, Jorma and Holm, Einar"，标题=" eHealth数据源在本地流感监测中的表现:一个5年开放队列研究"，期刊="J Med Internet Res"，年="2014"，月="Apr"，日="28"，卷="16"，数="4"，页数="e116"，关键词="流感;传染病监测;互联网;电子健康;谷歌流感趋势;远程护理呼叫中心;网站的使用;开放队列设计;背景:全球对利用全民卫生信息系统(被称为电子卫生资源)的综合征数据来改善传染病监测有着浓厚的兴趣。 Recently, the necessity for these systems to achieve two potentially conflicting requirements has been emphasized. First, they must be evidence-based; second, they must be adjusted for the diversity of populations, lifestyles, and environments. Objective: The primary objective was to examine correlations between data from Google Flu Trends (GFT), computer-supported telenursing centers, health service websites, and influenza case rates during seasonal and pandemic influenza outbreaks. The secondary objective was to investigate associations between eHealth data, media coverage, and the interaction between circulating influenza strain(s) and the age-related population immunity. Methods: An open cohort design was used for a five-year study in a Swedish county (population 427,000). Syndromic eHealth data were collected from GFT, telenursing call centers, and local health service website visits at page level. 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. ", issn="14388871", doi="10.2196/jmir.3099", url="//www.mybigtv.com/2014/4/e116/", url="https://doi.org/10.2196/jmir.3099", url="http://www.ncbi.nlm.nih.gov/pubmed/24776527" }