title: Estimating influenza vaccine effectiveness using routinely collected laboratory data.
abstract: BACKGROUND
  Estimation of influenza vaccine effectiveness (V/E) is needed early during influenza outbreaks in order to optimise management of influenza--a need which will be even greater in a pandemic situation.
  
  
  OBJECTIVE
  Examine the potential of routinely collected virological surveillance data to generate estimates of V/E in real-time during winter seasons.
  
  
  METHODS
  Integrated clinical and virological community influenza surveillance data collected over three winters 2004/5-2006/7 were used. We calculated the odds of vaccination in persons that were influenza-virus-positive and the odds in those that were negative and provided a crude estimate of V/E. Logistic regression was used to obtain V/E estimates adjusted for confounding variables such as age.
  
  
  RESULTS
  Multivariable analysis suggested that adjustments to the crude V/E estimate were necessary for patient age and month of sampling. The annual adjusted V/E was 2005/6, 67% (95% CI 41% to 82%); 2006/7 55% (26% to 73%) and 2007/8 67% (41% to 82%). The adjusted V/E in persons <65 years was 70% (57% to 78%) and 65 years and over 46% (-17% to 75%). Estimates differed by small insignificant amounts when calculated separately for influenza A and B; by interval between illness onset and swab sample; by analysis for the period November to January in each year compared with February to April and according to viral load.
  
  
  CONCLUSIONS
  We have demonstrated the potential of using routine virological and clinical surveillance data to provide estimates of V/E early in season and conclude that it is feasible to introduce this approach to V/E measurement into evaluation of national influenza vaccination programs.
authors.affiliation: Royal College of General Practitioners Research and Surveillance Centre, Lordswood House, 54 Lordswood Road, Harborne, Birmingham B17 9DB, UK. dfleming@rcgpbhamresunit.nhs.uk
authors.forename: D M
authors.initials: DM
authors.lastname: Fleming
authors.forename: N J
authors.initials: NJ
authors.lastname: Andrews
authors.forename: J S
authors.initials: JS
authors.lastname: Ellis
authors.forename: A
authors.initials: A
authors.lastname: Bermingham
authors.forename: P
authors.initials: P
authors.lastname: Sebastianpillai
authors.forename: A J
authors.initials: AJ
authors.lastname: Elliot
authors.forename: E
authors.initials: E
authors.lastname: Miller
authors.forename: M
authors.initials: M
authors.lastname: Zambon
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D002648:Child; D002675:Child, Preschool; D006801:Humans; D015994:Incidence; D007223:Infant; D009980:Influenza A virus / Q000302:isolation & purification; D009981:Influenza B virus / Q000302:isolation & purification; D007252:Influenza Vaccines; D007251:Influenza, Human / Q000175:diagnosis / Q000453:epidemiology* / Q000517:prevention & control; D008875:Middle Aged; D015999:Multivariate Analysis; D009305:Nasopharynx / Q000821:virology; D011159:Population Surveillance; D060888:Real-Time Polymerase Chain Reaction; D012141:Respiratory Tract Infections / Q000175:diagnosis* / Q000821:virology; D012621:Seasons; D006113:United Kingdom / Q000453:epidemiology; D019562:Viral Load / Q000706:statistics & numerical data
chemical_list: D007252:Influenza Vaccines