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.
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: Carlos G Grijalva; Yuwei Zhu; Derek J Williams; Wesley H Self; Krow Ampofo; Andrew T Pavia; Chris R Stockmann; Jonathan McCullers; Sandra R Arnold; Richard G Wunderink; Evan J Anderson; Stephen Lindstrom; Alicia M Fry; Ivo M Foppa; Lyn Finelli; Anna M Bramley; Seema Jain; Marie R Griffin; Kathryn M Edwards Journal: JAMA Date: 2015-10-13 Impact factor: 56.272
Authors: Eric J Chow; Melissa A Rolfes; Ruth L Carrico; Stephen Furmanek; Julio A Ramirez; Jill M Ferdinands; Alicia M Fry; Manish M Patel Journal: Open Forum Infect Dis Date: 2020-06-30 Impact factor: 3.835
Authors: Benjamin J Ridenhour; Michael A Campitelli; Jeffrey C Kwong; Laura C Rosella; Ben G Armstrong; Punam Mangtani; Andrew J Calzavara; David K Shay Journal: PLoS One Date: 2013-10-16 Impact factor: 3.240
Authors: Barameht Piralam; Sara M Tomczyk; Julia C Rhodes; Somsak Thamthitiwat; Christopher J Gregory; Sonja J Olsen; Prabda Praphasiri; Pongpun Sawatwong; Sathapana Naorat; Somrak Chantra; Peera Areerat; Cameron P Hurst; Matthew R Moore; Charung Muangchana; Henry C Baggett Journal: Am J Trop Med Hyg Date: 2015-10-26 Impact factor: 2.345