title: An Evidence Synthesis Approach to Estimating the Proportion of Influenza Among Influenza-like Illness Patients.
abstract: Estimation of the national-level incidence of seasonal influenza is notoriously challenging. Surveillance of influenza-like illness is carried out in many countries using a variety of data sources, and several methods have been developed to estimate influenza incidence. Our aim was to obtain maximally informed estimates of the proportion of influenza-like illness that is true influenza using all available data.
  
  
  
  We combined data on weekly general practice sentinel surveillance consultation rates for influenza-like illness, virologic testing of sampled patients with influenza-like illness, and positive laboratory tests for influenza and other pathogens, applying Bayesian evidence synthesis to estimate the positive predictive value (PPV) of influenza-like illness as a test for influenza virus infection. We estimated the weekly number of influenza-like illness consultations attributable to influenza for nine influenza seasons, and for four age groups.
  
  
  
  The estimated PPV for influenza in influenza-like illness patients was highest in the weeks surrounding seasonal peaks in influenza-like illness rates, dropping to near zero in between-peak periods. Overall, 14.1% (95% credible interval [CrI]: 13.5%, 14.8%) of influenza-like illness consultations were attributed to influenza infection; the estimated PPV was 50% (95% CrI: 48%, 53%) for the peak weeks and 5.8% during the summer periods.
  
  
  
  The model quantifies the correspondence between influenza-like illness consultations and influenza at a weekly granularity. Even during peak periods, a substantial proportion of influenza-like illness-61%-was not attributed to influenza. The much lower proportion of influenza outside the peak periods reflects the greater circulation of other respiratory pathogens relative to influenza.
authors.affiliation: From the Centre for Infectious Disease Control, National Institute for Public Health and the Environment, Bilthoven, The Netherlands.
authors.forename: Scott A
authors.initials: SA
authors.lastname: McDonald
authors.forename: Michiel
authors.initials: M
authors.lastname: van Boven
authors.forename: Jacco
authors.initials: J
authors.lastname: Wallinga
mesh_terms: D000328:Adult; D017677:Age Distribution; D000368:Aged; D001499:Bayes Theorem; D002675:Child, Preschool; D003140:Communicable Disease Control; D004196:Disease Outbreaks; D019317:Evidence-Based Medicine; D005260:Female; D006801:Humans; D015994:Incidence; D007223:Infant; D007252:Influenza Vaccines / Q000008:administration & dosage*; D007251:Influenza, Human / Q000453:epidemiology* / Q000517:prevention & control; D008297:Male; D008875:Middle Aged; D009426:Netherlands / Q000453:epidemiology; D011237:Predictive Value of Tests; D012141:Respiratory Tract Infections / Q000175:diagnosis / Q000453:epidemiology*; D018571:Sentinel Surveillance; D017678:Sex Distribution; D014611:Vaccination / Q000706:statistics & numerical data
chemical_list: D007252:Influenza Vaccines