Performance of eHealth Data Sources in Local Influenza Surveillance: A 5-Year Open Cohort Study

Abstract

BackgroundThere 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.ObjectiveThe 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.MethodsAn 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.ResultsLocal 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.ConclusionsCorrelations 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.

Epidemiological and statistical basis for detection and prediction of influenza epidemics

“…Two streams of data used for routine influenza surveillance in Östergötland County, Sweden (population 445,000 inhabitants) were used in this study: data on clinical influenza-diagnoses and data on syndromic chief complaints from a national telenursing service. The latter data source had previously been found to provide indications of increased influenza activity up to 2 weeks ahead of the former (Timpka et al 2014a(Timpka et al , 2014b.…”

“…Data from this learning set were used to determine the grouping of telenursing chief complaints with the largest correlation strength and best lead time between influenza-diagnosis data and telenursing data. The optimal combination of chief complaints was found to be fever (adult, child), and the most favorable lead time was 14 days (with telenursing data preceding influenza-diagnosis data) (Timpka et al 2014a(Timpka et al , 2014b. Using the predicted peak timing, the peak intensity prediction component was applied to influenza-diagnosis data from the corresponding epidemics to estimate the peak intensity on the predicted peak day.…”

“…First, telenursing data are available on a daily level, which is not the case for, e.g., GFT data. Second, the 2-week (when using weekly counts) and 14-day (when using daily counts) preceding time lag between telenursing chief complaints and influenza-diagnosis cases were most strongly associated among the available eHealth data sources previously investigated (Timpka et al 2014a(Timpka et al , 2014b. Because the main aim of this thesis was to predict the start and the peak timing of an epidemic as early as possible, these two motives were crucial.…”

“…When the peak is found in the syndromic data, the 14 days preceding the influenza-diagnosis data (Timpka et al 2014b) is utilized to find the influenzadiagnosis peak. In other words, if the peak in the syndromic data appears on day 0, the influenza-diagnosis peak is assumed to appear on day 14.…”

“…Another reason for choosing telenursing chief complaints as syndromic data is that complaints such as fever and cough are less likely to be affected by media publicity than data reflecting Internet activity. However, there is a risk that telenursing data may not be fully appropriate if the circulating virus strains mainly affect older age groups, because the use of telenursing services in these groups is lower (Timpka et al 2014b). An alternative would be to use data on over-the-counter drug sales (Liu et al 2013), but the availability of these data in real time is limited in many countries, including Sweden.…”