title: Accuracy of data on influenza vaccination status at four Vaccine Safety Datalink sites.
abstract: BACKGROUND
  Studies of influenza vaccination using electronic medical records rely on accurate classification of vaccination status. Vaccinations not entered into electronic records would be unavailable for study.
  
  
  OBJECTIVE
  This study evaluated the sensitivity and negative predictive value (NPV) of electronic records for influenza vaccination and factors associated with failure to capture vaccinations.
  
  
  METHODS
  In four diverse medical care organizations in the Vaccine Safety Datalink, those aged 50-79 years with no influenza vaccination record during the 2007-2008 season were surveyed by telephone, and electronic records were analyzed in 2008. The sensitivity and NPV of electronic records were estimated, using survey responses as the gold standard. Logistic regression models determined associations between 1-NPV and demographic factors, risk of influenza complications, and healthcare utilization levels.
  
  
  RESULTS
  Data were obtained for 933 survey participants and 1,085,916 medical care organization members. Sites varied significantly in the sensitivity (51%, 68%, 79%, 89%) and NPV (46%, 62%, 66%, 87%) of electronic records. In multivariate analysis, the rate of failure to capture vaccinations was significantly higher for those aged 65-79 years than for those aged 50-64 years at three sites. Of vaccinations not captured by electronic records, 58% were reportedly administered in nontraditional settings, usually workplaces; the rest were given within the sites.
  
  
  CONCLUSIONS
  Influenza vaccination studies relying on electronic records may misclassify substantial proportions of vaccinated individuals as unvaccinated, producing biased estimates of vaccine effectiveness. Sites with limited sensitivity to capture vaccinations administered within their organization should seek possible remedies. More complete capture of vaccinations administered to older patients and in nontraditional settings would further reduce misclassification.
authors: Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Massachusetts 02215-3920, USA. Sharon_Greene@harvardpilgrim.org
authors: Sharon K
authors: SK
authors: Greene
authors: Ping
authors: P
authors: Shi
authors: M Maya
authors: MM
authors: Dutta-Linn
authors: Jo Ann
authors: JA
authors: Shoup
authors: Virginia L
authors: VL
authors: Hinrichsen
authors: Paula
authors: P
authors: Ray
authors: James D
authors: JD
authors: Nordin
authors: Leslie
authors: L
authors: Kuckler
authors: Eric S
authors: ES
authors: Weintraub
authors: W Katherine
authors: WK
authors: Yih
mesh_terms: D000368:Aged; D015982:Bias; D003430:Cross-Sectional Studies; D003625:Data Collection / Q000592:standards; D005260:Female; D006306:Health Surveys; D006801:Humans; D007251:Influenza, Human / Q000517:prevention & control*; D020407:Internet; D016015:Logistic Models; D008297:Male; D016347:Medical Records Systems, Computerized / Q000592:standards; D008875:Middle Aged; D012680:Sensitivity and Specificity; D014481:United States; D014611:Vaccination / Q000706:statistics & numerical data*