Prior studies suggest that the influenza vaccine is protective against some outcomes in hospitalized patients infected with influenza despite vaccination. We utilized surveillance data from Columbus, Ohio to investigate this association over multiple influenza seasons and age groups. Data on laboratory-confirmed influenza-associated hospitalizations were collected as a part of the Influenza Hospitalization Surveillance Project for the 2012–2013, 2013–2014, and 2014–2015 influenza seasons. The association between influenza vaccination status was examined in relation to the outcomes of severe influenza and diagnosis of pneumonia among patients receiving antiviral treatment. Data were analyzed using multivariable logistic regression. We observed no overall association between influenza vaccination status and severe influenza among hospitalized patients. During the 2013–2014 season, those who were vaccinated were 41% less likely to be diagnosed with pneumonia compared with those who were unvaccinated (OR = 0·59 95% CI 0·41–0·86). The influenza vaccine may provide a secondary preventive function against pneumonia among influenza cases requiring hospitalization. However, a protective effect was only observed in 2013–2014, an influenza H1N1 dominant year. Differences in circulating influenza virus strains and vaccine matching to the circulating strains during influenza seasons may impact this association.

Societal and economic impact of influenza is mainly due to influenza infection of specific groups, who are at higher risk of health complications leading up to hospitalisation or death. In this study we applied the health belief model (HBM) to evaluate beliefs and attitudes towards influenza disease and vaccine in community-dwelling high-risk individuals (aged 65 or more or having a chronic disease). We conducted a mixed-method study using data collected through a telephone survey of a household unit sample. We used thematic analysis to map responses to HBM dimensions and Poisson regression to model vaccine non-uptake prevalence. The main self-reported reason not to take the vaccine referred to the susceptibility dimension: ‘considering oneself to be a healthy person’ (29·8%, (95% confidence interval (CI) 22·1–38·7)). Bad experiences after vaccination – barriers dimension – were also commonly reported (17·0%, (95% CI 10·8–23·8)). Vaccine non-uptake prevalence was 22% higher in those who did not consider themselves susceptible to contract flu (Prevalence Ratio (PR) = 1·22, (95% CI 1·0–1·5)) and 18% lower in those who did not consider that the vaccine causes flu symptoms (PR = 0·82, (95% CI 0·68–0·99)). Results suggest that high-risk individuals do not think of themselves susceptible to influenza infection and fear adverse events following immunisation.

Data were pooled from three Australian sentinel general practice influenza surveillance networks to estimate Australia-wide influenza vaccine coverage and effectiveness against community presentations for laboratory-confirmed influenza for the 2012, 2013 and 2014 seasons. Patients presenting with influenza-like illness at participating GP practices were swabbed and tested for influenza. The vaccination odds of patients testing positive were compared with patients testing negative to estimate influenza vaccine effectiveness (VE) by logistic regression, adjusting for age group, week of presentation and network. Pooling of data across Australia increased the sample size for estimation from a minimum of 684 to 3,683 in 2012, from 314 to 2,042 in 2013 and from 497 to 3,074 in 2014. Overall VE was 38% [95% confidence interval (CI) 24–49] in 2012, 60% (95% CI 45–70) in 2013 and 44% (95% CI 31–55) in 2014. For A(H1N1)pdm09 VE was 54% (95% CI–28 to 83) in 2012, 59% (95% CI 33–74) in 2013 and 55% (95% CI 39–67) in 2014. For A(H3N2), VE was 30% (95% CI 14–44) in 2012, 67% (95% CI 39–82) in 2013 and 26% (95% CI 1–45) in 2014. For influenza B, VE was stable across years at 56% (95% CI 37–70) in 2012, 57% (95% CI 30–73) in 2013 and 54% (95% CI 21–73) in 2014. Overall VE against influenza was low in 2012 and 2014 when A(H3N2) was the dominant strain and the vaccine was poorly matched. In contrast, overall VE was higher in 2013 when A(H1N1)pdm09 dominated and the vaccine was a better match. Pooling data can increase the sample available and enable more precise subtype- and age group-specific estimates, but limitations remain.

Socioeconomic factors and the patterns of use of health services associated with influenza and pneumococcal vaccination were studied in people aged ⩾65 years admitted to three general hospitals in Spain between 2005 and 2007. The following data were collected: age, sex, risk of pneumonia, educational level, social class, type of household, physician visits, length of time with the same general practitioner, and influenza and pneumococcal vaccination (23vPPV). Associations between variables were assessed using multivariate logistic regression analysis. In total, 1702 patients were included; 59·9% had received 23vPPV and 65·6% influenza vaccine. Older age (OR 1·04, P<0·001), living with a partner (OR 1·72, P=0·003) and influenza vaccination during the last year (OR 6·64, P<0·001) were associated with 23vPPV. Male sex (OR 1·44, P=0·005), older age (OR 1·02, P=0·009), moderate risk of pneumonia (OR 1·58, P=0·001), living with a partner (OR 1·52, P=0·015) and frequent physician visits during the last year (1–6 annuals visits (OR 2·65, P<0·001); >6 visits (OR 3·83, P<0·001)) were associated with influenza vaccination. Coordination between public health and primary-care services may be necessary to improve vaccine uptake.

Objectives: Influenza (vernacular name, flu) is a viral infection that causes a high consumption of resources. Several studies have been carried out to provide an economic evaluation of the vaccination programs against influenza. Nevertheless, there is still a lack of evidence about the dynamic effects resulting from the reduction of the transmission power. This study considers the impact on contagiousness of alternative strategies against influenza in people aged 50–64 in Italy, France, Germany, and Spain.

Methods: By using the Influsim 2.0 dynamic model, we have determined the social benefits of different coverage levels in every country compared with the ones currently recommended. We have subsequently performed a Budget Impact Analysis to determine whether the currently recommended coverage results from an optimal budget allocation. A probabilistic sensitivity analysis was also conducted.

Results: We found that in Germany, the optimal coverage level is 38.5 percent, in France 32.4 percent, in Italy 32.75 percent, and 28.3 percent in Spain. By extending the coverage level, social saving tends to increase up to 100 percent for France and Italy and up to 80 percent for Germany and Spain.

Conclusions: Decision makers should allocate the budget for vaccination against influenza consistently with the estimation of the optimal coverage level and with the dynamic effects resulting from the reduction of the transmission power.