title: Predictive performance of telenursing complaints in influenza surveillance: a prospective cohort study in Sweden.
abstract: Syndromic data sources have been sought to improve the timely detection of increased influenza transmission. This study set out to examine the prospective performance of telenursing chief complaints in predicting influenza activity. Data from two influenza seasons (2007/08 and 2008/09) were collected in a Swedish county (population 427,000) to retrospectively determine which grouping of telenursing chief complaints had the largest correlation with influenza case rates. This grouping was prospectively evaluated in the three subsequent seasons. The best performing telenursing complaint grouping in the retrospective algorithm calibration was fever (child, adult) and syncope (r=0.66; p<0.001). In the prospective evaluation, the performance of 14-day predictions was acceptable for the part of the evaluation period including the 2009 influenza pandemic (area under the curve (AUC)=0.84; positive predictive value (PPV)=0.58), while it was strong (AUC=0.89; PPV=0.93) for the remaining evaluation period including only influenza winter seasons. We recommend the use of telenursing complaints for predicting winter influenza seasons. The method requires adjustments when used during pandemics.
authors.affiliation: Department of Public Health, ostergotland County Council, Linkoping, Sweden.
authors.forename: T
authors.initials: T
authors.lastname: Timpka
authors.forename: A
authors.initials: A
authors.lastname: Spreco
authors.forename: O
authors.initials: O
authors.lastname: Eriksson
authors.forename: Ö
authors.initials: Ö
authors.lastname: Dahlström
authors.forename: E A
authors.initials: EA
authors.lastname: Gursky
authors.forename: M
authors.initials: M
authors.lastname: Strömgren
authors.forename: E
authors.initials: E
authors.lastname: Holm
authors.forename: J
authors.initials: J
authors.lastname: Ekberg
authors.forename: J
authors.initials: J
authors.lastname: Hinkula
authors.forename: J M
authors.initials: JM
authors.lastname: Nyce
authors.forename: H
authors.initials: H
authors.lastname: Eriksson
mesh_terms: D000328:Adult; D000465:Algorithms; D019540:Area Under Curve; D002648:Child; D004196:Disease Outbreaks; D005334:Fever / Q000209:etiology; D063005:Health Information Systems; D006801:Humans; D015994:Incidence; D053118:Influenza A Virus, H1N1 Subtype / Q000302:isolation & purification; D053122:Influenza A Virus, H3N2 Subtype / Q000302:isolation & purification; D007251:Influenza, Human / Q000175:diagnosis / Q000453:epidemiology*; D058873:Pandemics; D011159:Population Surveillance / Q000379:methods*; D011237:Predictive Value of Tests; D011446:Prospective Studies; D012189:Retrospective Studies; D012621:Seasons; D013548:Sweden / Q000453:epidemiology; D055817:Telenursing