J. Med. Internet Res. - Performance of eHealth data sources in local influenza surveillance: a 5-year open cohort study.


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CKGROUND: There is abundant global interest in using syndromic data from population-wide health information systems--referred to as eHealth resources--to improve infectious disease surveillance. 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.

Resumo Limpo

ckground abund global interest use syndrom data populationwid health inform systemsref ehealth resourcesto improv infecti diseas surveil recent necess system achiev two potenti conflict requir emphas first must evidencebas second must adjust divers popul lifestyl environmentsobject primari object examin correl data googl flu trend gft computersupport telenurs center health servic websit influenza case rate season pandem influenza outbreak secondari object investig associ ehealth data media coverag interact circul influenza strain agerel popul immunitymethod open cohort design use fiveyear studi swedish counti popul syndrom ehealth data collect gft telenurs call center local health servic websit visit page level data mass media coverag influenza collect major region newspap perform ehealth data surveil measur correl effect size time lag clinic diagnos influenza casesresult local media coverag data influenza case rate show correl larg effect size influenza phn outbreak r ci p sever season hn outbreak r ci p media coverag preced case rate one week correl gft influenza case data show larg effect size outbreak largest season hn outbreak r ci p preced time lag decreas two week first outbreak one week phn pandem telenurs data influenza case data show correl larg effect size outbreak season b h outbreak time lag decreas two week season hn outbreak r ci p none p hn outbreak r ci p larg effect size also observ websit visit influenza case dataconclus correl ehealth data influenza case rate swedish counti show larg effect size throughout fiveyear period time lag signal ehealth data influenza rate chang research need analyt method adjust ehealth surveil system shift media coverag variat agegroup relat immun virus strain result can use inform develop alertgener ehealth surveil system can subject prospect evalu routin public health practic

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