Excess mortality due to seasonal influenza is substantial, yet quantitative estimates of the benefit of annual vaccination programs on influenza-associated mortality are lacking. We estimated the numbers of deaths averted by vaccination in four age groups (0.5 to 4, 5 to 19, 20 to 64 and ≥65 yrs.) for the nine influenza seasons from 2005/6 through 2013/14. These estimates were obtained using a Monte Carlo approach applied to weekly U.S. age group-specific estimates of influenza-associated excess mortality, monthly vaccination coverage estimates and summary seasonal influenza vaccine effectiveness estimates to obtain estimates of the number of deaths averted by vaccination. The estimates are conservative as they do not include indirect vaccination effects. From August, 2005 through June, 2014, we estimated that 40,127 (95% confidence interval [CI] 25,694 to 59,210) deaths were averted by influenza vaccination. We found that of all studied seasons the most deaths were averted by influenza vaccination during the 2012/13 season (9398; 95% CI 2,386 to 19,897) and the fewest during the 2009/10 pandemic (222; 95% CI 79 to 347). Of all influenza-associated deaths averted, 88.9% (95% CI 83 to 92.5%) were in people ≥65 yrs. old. The estimated number of deaths averted by the US annual influenza vaccination program is considerable, especially among elderly adults and even when vaccine effectiveness is modest, such as in the 2012/13 season. As indirect effects ("herd immunity") of vaccination are ignored, these estimates represent lower bound estimates and are thus conservative given valid excess mortality estimates.

Background: The test-negative design (TND) to evaluate influenza vaccine effectiveness is based on patients seeking care for acute respiratory infection, with those who test positive for influenza as cases and the test-negatives serving as controls. This design has not been validated for the inpatient setting where selection bias might be different from an outpatient setting. Methods: We derived mathematical expressions for vaccine effectiveness (VE) against laboratory-confirmed influenza hospitalizations and used numerical simulations to verify theoretical results exploring expected biases under various scenarios. We explored meaningful interpretations of VE estimates from inpatient TND studies. Results: VE estimates from inpatient TND studies capture the vaccine-mediated protection of the source population against laboratory-confirmed influenza hospitalizations. If vaccination does not modify disease severity, these estimates are equivalent to VE against influenza virus infection. If chronic cardiopulmonary individuals are enrolled because of non-infectious exacerbation, biased VE estimates (too high) will result. If chronic cardiopulmonary disease status is adjusted for accurately, the VE estimates will be unbiased. If chronic cardiopulmonary illness cannot be adequately be characterized, excluding these individuals may provide unbiased VE estimates. Conclusions: The inpatient TND offers logistic advantages and can provide valid estimates of influenza VE. If highly vaccinated patients with respiratory exacerbation of chronic cardiopulmonary conditions are eligible for study inclusion, biased VE estimates will result unless this group is well characterized and the analysis can adequately adjust for it. Otherwise, such groups of subjects should be excluded from the analysis.

SUMMARY: Death certificate reports and laboratory-confirmed influenza deaths probably underestimate paediatric deaths attributable to influenza. Using US mortality data for persons aged <18 years who died during 28 September 2003 to 2 October 2010, we estimated influenza-attributable deaths using a generalized linear regression model based on seasonal covariates, influenza-certified deaths (deaths for which influenza was a reported cause of death), and occurrence during the 2009 pandemic period. Of 32 783 paediatric deaths in the death categories examined, 853 (3%) were influenza-certified. The estimated number of influenza-attributable deaths over the study period was 1·8 [95% confidence interval (CI) 1·3–2·8] times higher than the number of influenza-certified deaths. Influenza-attributable deaths were 2·1 (95% CI 1·5–3·4) times higher than influenza-certified deaths during the non-pandemic period and 1·1 (95% CI 1·0–1·8) times higher during the pandemic. Overall, US paediatric deaths attributable to influenza were almost twice the number reported by death certificate codes in the seasons prior to the 2009 pandemic.

Vaccination history may confound estimates of influenza vaccine effectiveness (VE) when two conditions are present: (1) Influenza vaccination is associated with vaccination history and (2) vaccination modifies the risk of natural infection in the following seasons, either due to persisting vaccination immunity or due to lower previous risk of natural infection. Analytic arguments are used to define conditions for confounding of VE estimates by vaccination history. Simulation studies, both with accurate and inaccurate assessment of current and previous vaccination status, are used to explore the potential magnitude of these biases when using different statistical models to address confounding by vaccination history. We found a potential for substantial bias of VE estimates by vaccination history if infection- and/or vaccination-derived immunity persisted from one season to the next and if vaccination uptake in individuals was seasonally correlated. Full adjustment by vaccination history, which is usually not feasible, resulted in unbiased VE estimates. Partial adjustment, i.e. only by prior season's vaccination status, significantly reduced confounding bias. Misclassification of vaccination status, which can also lead to substantial bias, interferes with the adjustment of VE estimates for vaccination history. Confounding by vaccination history may bias VE estimates, but even partial adjustment by only the prior season's vaccination status substantially reduces confounding bias. Misclassification of vaccination status may compromise VE estimates and efforts to adjust for vaccination history.

The emergence of West Nile virus (WNV) in North America has been associated with high mortality in the native avifauna and has raised concerns about the long-term impact of WNV on bird populations. Here, we present results from a longitudinal analysis of annual counts of six bird species, using North American Breeding Bird Survey data from ten states (1994 to 2010). We fit overdispersed Poisson models to annual counts. Counts from successive years were linked by an autoregressive process that depended on WNV transmission intensity (annual West Nile neuroinvasive disease reports) and was adjusted by El Niño Southern Oscillation events. These models were fit using a Markov chain Monte Carlo algorithm.