title: Are influenza-associated morbidity and mortality estimates for those ≥ 65 in statistical databases accurate, and an appropriate test of influenza vaccine effectiveness?
abstract: OBJECTIVE
  To assess the accuracy of estimates using statistical databases of influenza-associated morbidity and mortality, and precisely measure influenza vaccine effectiveness.
  
  
  RESULTS
  Laboratory testing of influenza is incomplete. Death certificates under-report influenza. Statistical database models are used as an alternative to randomised controlled trials (RCTs) to assess influenza vaccine effectiveness. Evidence of the accuracy of influenza morbidity and mortality estimates was sought from: (1) Studies comparing statistical models. For four studies Poisson and ARIMA models produced higher estimates than Serfling, and Serfling higher than GLM. Which model is more accurate is unknown. (2) Studies controlling confounders. Fourteen studies mostly controlled one confounder (one controlled comorbidities), and limited control of confounders limits accuracy.
  (1) Studies of regions with increasing vaccination rates. Of five studies two controlled for confounders and one found a positive vaccination effect. Three studies did not control confounders and two found no effect of vaccination. (2) Studies controlling multiple confounders. Of thirteen studies only two found a positive vaccine effect and no mortality differences between vaccinees and non-vaccinees in non-influenza seasons, showing confounders were controlled. Key problems are insufficient testing for influenza, using influenza-like illness, heterogeneity of seasonal and pandemic influenza, population aging, and incomplete confounder control (co-morbidities, frailty, vaccination history) and failure to demonstrate control of confounders by proving no mortality differences between vaccinees and non-vaccinees in non-influenza seasons.
  
  
  CONCLUSIONS
  Improving model accuracy requires proof of no mortality differences in pre-influenza periods between the vaccinated and non-vaccinated groups, and reduction in influenza morbidity and mortality in seasons with a good vaccine match, more virulent strains, in the younger elderly with less immune senescence, and specific outcomes (laboratory-confirmed outcomes, pneumonia deaths). Proving influenza vaccine effectiveness requires appropriately powered RCTs, testing participants with RT-PCR tests, and comprehensively monitoring morbidity and mortality.
authors.affiliation: Department of Family Medicine, University of Calgary, Health Sciences Centre, 3330 Hospital Drive NW, Calgary, AB, Canada T2N 4N1. Electronic address: rthomas@ucalgary.ca.
authors.forename: Roger E
authors.initials: RE
authors.lastname: Thomas
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D016208:Databases, Factual; D062665:Epidemiological Monitoring; D006801:Humans; D007252:Influenza Vaccines / Q000008:administration & dosage* / Q000276:immunology*; D007251:Influenza, Human / Q000453:epidemiology* / Q000401:mortality*; D015233:Models, Statistical; D016896:Treatment Outcome
chemical_list: D007252:Influenza Vaccines
keywords: Databases; Influenza; Influenza vaccine effectiveness; Morbidity and mortality; RCTs; Statistical models