title: Inactivated influenza vaccines for prevention of community-acquired pneumonia: the limits of using nonspecific outcomes in vaccine effectiveness studies.
abstract: BACKGROUND
  One to 4 million cases of community-acquired pneumonia (CAP) occur annually in the United States, resulting in 600,000 hospitalizations and 45,000 deaths. Influenza infection facilitates secondary bacterial infections, and influenza vaccination may prevent CAP directly by preventing influenza pneumonia or indirectly by preventing secondary bacterial CAP.
  
  
  METHODS
  We investigated how influenza vaccination could affect incidence of CAP using deterministic probability and stochastic simulation models. The models included likely influential factors, including vaccine effectiveness (VE) against influenza, rates of influenza in the unvaccinated, vaccination coverage, and the relative risk (RR) of pneumonia, given influenza infection. To estimate effectiveness of influenza vaccine against CAP, we assumed mean VE against influenza of 55% and vaccine coverage of 38%.
  
  
  RESULTS
  Given our baseline parameters, influenza vaccine had a mean effectiveness against CAP of 7% (95% confidence interval = 0-25%). Effectiveness of influenza vaccine against CAP increased as its effectiveness against influenza increased, as RR of pneumonia after influenza infection increased, and as rates of influenza among unvaccinated persons increased.
  
  
  CONCLUSIONS
  No matter how effective vaccine may be in preventing influenza infection, it is only modestly effective at preventing CAP. Because of the large annual burden of CAP, a vaccine that is only moderately effective in preventing influenza infection has the potential to prevent a substantial number of CAP cases. This modeling approach may be useful for planning influenza vaccine-probe studies and evaluating the effectiveness of other interventions targeted against infections that manifest in nonspecific outcomes.
authors.affiliation: National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, Atlanta, GA 30333, USA. JFerdinands@cdc.gov
authors.forename: Jill M
authors.initials: JM
authors.lastname: Ferdinands
authors.forename: Paul
authors.initials: P
authors.lastname: Gargiullo
authors.forename: Michael
authors.initials: M
authors.lastname: Haber
authors.forename: Matthew
authors.initials: M
authors.lastname: Moore
authors.forename: Edward A
authors.initials: EA
authors.lastname: Belongia
authors.forename: David K
authors.initials: DK
authors.lastname: Shay
mesh_terms: D017714:Community-Acquired Infections / Q000453:epidemiology / Q000517:prevention & control; D003198:Computer Simulation; D006801:Humans; D015994:Incidence; D007252:Influenza Vaccines; D008954:Models, Biological; D015233:Models, Statistical; D011014:Pneumonia / Q000453:epidemiology / Q000517:prevention & control*; D012107:Research Design; D013269:Stochastic Processes; D016896:Treatment Outcome; D014481:United States / Q000453:epidemiology; D015164:Vaccines, Inactivated
chemical_list: D007252:Influenza Vaccines; D015164:Vaccines, Inactivated