Influenza Vaccine Effectiveness Estimates Research Articles

Maximum likelihood estimation of influenza vaccine effectiveness against transmission from the household and from the community.

Influenza vaccination is recommended as the best way to protect against influenza infection and illness. Due to seasonal changes in influenza virus types and subtypes, a new vaccine must be produced, and vaccine effectiveness (VE) must be estimated, annually. Since 2010, influenza vaccination has been recommended universally in the United States, making randomized clinical trials unethical. Recent studies have used a monitored household cohort study design to determine separate VE estimates against influenza transmission from the household and community. We developed a probability model and accompanying maximum likelihood procedure to estimate vaccine-related protection against transmission of influenza from the household and the community. Using agent-based stochastic simulations, we validated that we can obtain maximum likelihood estimates of transmission parameters and VE close to their true values. Sensitivity analyses to examine the effect of deviations from our assumptions were conducted. We used our method to estimate transmission parameters and VE from data from a monitored household study in Michigan during the 2012-2013 influenza season and were able to detect a significant protective effect of influenza vaccination against community-acquired transmission.

On the bias of estimates of influenza vaccine effectiveness from test-negative studies.

Estimates of the effectiveness of influenza vaccines are commonly obtained from a test-negative design (TND) study, where cases and controls are patients seeking care for an acute respiratory illness who test positive and negative, respectively, for influenza infection. Vaccine effectiveness (VE) estimates from TND studies are usually interpreted as vaccine effectiveness against medically-attended influenza (MAI). However, it is also important to estimate VE against any influenza illness (symptomatic influenza (SI)) as individuals with SI are still a public health burden even if they do not seek medical care. We present a numerical method to evaluate the bias of TND-based estimates of influenza VE with respect to MAI and SI. We consider two sources of bias: (a) confounding bias due to a (possibly unobserved) covariate that is associated with both vaccination and the probability of the outcome of interest and (b) bias resulting from the effect of vaccination on the probability of seeking care. Our results indicate that (a) VE estimates may suffer from substantial confounding bias when a confounder has a different effect on the probabilities of influenza and non-influenza ARI, and (b) when vaccination reduces the probability of seeking care against influenza ARI, then estimates of VE against MAI may be unbiased while estimates of VE against SI may be have a substantial positive bias.

Low influenza vaccine effectiveness and the effect of previous vaccination in preventing admission with A(H1N1)pdm09 or B/Victoria-Lineage in patients 60 years old or older during the 2015/2016 influenza season.

The 2015/2016 influenza season was characterized in Europe by the circulation of A(H1N1)pdm09 clade 6B.1 and B/Victoria-lineage influenza viruses. The components of the vaccines used in the current and past two seasons in the Valencia region were similar but not well matched to the 2015/2016 dominant influenza-circulating strains. We estimate influenza vaccine effectiveness (IVE) and interference of previous vaccination in preventing admission with A(H1N1)pdm09 or B/Victoria-lineage in this particular season. The Valencia Hospital Network for the Study of Influenza runs an active surveillance hospital-based study to collect clinical and virological data from consecutive admissions possibly related to influenza. Combined nasopharyngeal and pharyngeal swabs are analyzed by reverse transcription polymerase chain reaction, and the hemagglutinin is sequenced in a sample of positive influenza specimens. Vaccination is ascertained consulting a population vaccine information system. We estimate IVE using a test-negative approach. During the 2015-2016 season, we recruited 1049 eligible admissions of patients 60 years or older, and 187 tested positive for influenza. The adjusted IVE in preventing admission with A(H1N1)pdm09 was 20.2%; 95% confidence interval (CI) -21.3-47.5% and -33.2%; 95% CI, -140.1-26.1% in preventing admission with B/Victoria-lineage. The majority of A(H1N1)pdm09 sequenced viruses belonged to the emerging 6B.1 subclade, defined by S162N and I216T mutations in the hemagglutinin protein. When we restricted our analysis to those not vaccinated in the previous year, unadjusted IVE was 84.9% (95% CI 9.9-100.0) overall, 77.9% (-32.7-100.0%) in preventing A(H1N1)pdm09 and 48.8% (-219.5-100.0%) in preventing B/Yamagata-lineage admission. Our findings indicate that IVE was low in preventing A(H1N1)pdm09 and strongly correlated with vaccination in the previous season. No effect in preventing admission with B/Victoria-lineage was observed. For the 2015/2016 season, IVE was low due to a mismatch and lack of concordance between the circulating and vaccine viruses.

Low interim influenza vaccine effectiveness, Australia, 1 May to 24 September 2017.

In 2017, influenza seasonal activity was high in the southern hemisphere. We present interim influenza vaccine effectiveness (VE) estimates from Australia. Adjusted VE was low overall at 33% (95% confidence interval (CI): 17 to 46), 50% (95% CI: 8 to 74) for A(H1)pdm09, 10% (95% CI: -16 to 31) for A(H3) and 57% (95% CI: 41 to 69) for influenza B. For A(H3), VE was poorer for those vaccinated in the current and prior seasons.

Estimating influenza vaccine effectiveness: Evolution of methods to better understand effects of confounding in older adults.

Older adults are at high risk for serious complications of influenza illness and loss of vaccine-mediated protection. It is increasingly recognized that in addition to age, multiple chronic conditions and associated frailty contribute to the decline in vaccine effectiveness in this population. However, observational studies have been fraught with issues of confounding related to the degree of frailty and functional decline, measures of which are not included in standard administrative health care databases that are used to calculate vaccine effectiveness. This issue has led to the identification of confounding by indication or from "healthy vaccinee" bias, which respectively lead to underestimates or overestimates of influenza vaccine effectiveness. In addition, the sensitivity and specificity of the criteria used to define influenza-like illness declines with increasing age due to atypical presentations of illness and the inability to distinguish between influenza and other respiratory viruses. The test-negative case:control design has emerged as a method to estimate influenza vaccine effectiveness by comparing vaccination rates in those with laboratory-confirmed influenza to those with other acute viral respiratory illnesses. This review provides a perspective on how test-negative case:control study designs and new insights into mechanisms of protection have considerably strengthened influenza vaccination policy decisions for older adults that have historically been undermined by the conclusions of observational studies.

Estimation of seasonal influenza vaccine effectiveness using data collected in primary care in France: comparison of the test-negative design and the screening method

ObjectivesWe discussed which method between the test-negative design (TND) and the screening method (SM) could provide more robust real-time and end-of-season vaccine effectiveness (VE) estimates using data collected from routine influenza surveillance in primary care. MethodsWe used data collected during two influenza seasons, 2014–15 and 2015–16. Using the SM, we estimated end-of-season VE in preventing medically attended influenza-like illness and laboratory-confirmed influenza among the population at risk. Using the TND, we estimated end-of-season VE in preventing influenza among both the general and the at-risk population. We estimated real-time VE using both methods. ResultsFor the SM, the overall adjusted end-of-season VE was 24% (95% confidence interval (CI), 16 to 32) and 12% (95% CI, −16 to 33) during season 2014–15, and 53% (95% CI, 44 to 60) and 47% (95% CI, 23 to 64) during season 2015–16, in preventing influenza-like illness and laboratory-confirmed influenza, respectively. For the TND, the overall adjusted end-of-season VE was −17% (95% CI, −79 to 24) and −38% (95% CI, −199 to 13) in 2014–15, and 10% (95% CI, −31 to 39) and 18% (95% CI, −33 to 50) in 2015–16, among the general and at-risk population, respectively. Real-time VE estimates obtained through the TND showed more variability across each season and lower precision than those estimated with the SM. ConclusionsAlthough the worldwide use of the TND allows for comparison of overall VE estimates among countries, the SM performs better in providing robust real-time VE estimates among the population at risk.

Controlling confounding by frailty when estimating influenza vaccine effectiveness using predictors of dependency in activities of daily living.

To improve control of confounding by frailty when estimating the effect of influenza vaccination on all-cause mortality by controlling for a published set of claims-based predictors of dependency in activities of daily living (ADL). Using Medicare claims data, a cohort of beneficiaries >65years of age was followed from September 1, 2007, to April 12, 2008, with covariates assessed in the 6months before follow-up. We estimated Cox proportional hazards models of all-cause mortality, with influenza vaccination as a time-varying exposure. We controlled for common demographics, comorbidities, and health care utilization variables and then added 20 ADL dependency predictors. To gauge residual confounding, we estimated pre-influenza season hazard ratios (HRs) between September 1, 2007 and January 5, 2008, which should be 1.0 in the absence of bias. A cohort of 2235140 beneficiaries was created, with a median follow-up of 224days. Overall, 52% were vaccinated and 4% died during follow-up. During the pre-influenza season period, controlling for demographics, comorbidities, and health care use resulted in a HR of 0.66 (0.64, 0.67). Adding the ADL dependency predictors moved the HR to 0.68 (0.67, 0.70). Controlling for demographics and ADL dependency predictors alone resulted in a HR of 0.68 (0.66, 0.70). Results were consistent with those in the literature, with significant uncontrolled confounding after adjustment for demographics, comorbidities, and health care use. Adding ADL dependency predictors moved HRs slightly closer to the null. Of the comorbidities, health care use variables, and ADL dependency predictors, the last set reduced confounding most. However, substantial uncontrolled confounding remained.

Influenza Vaccine Effectiveness in the United States during the 2015–2016 Season

BackgroundThe A(H1N1)pdm09 virus strain used in the live attenuated influenza vaccine was changed for the 2015–2016 influenza season because of its lack of effectiveness in young children in 2013–2014. The Influenza Vaccine Effectiveness Network evaluated the effect of this change as part of its estimates of influenza vaccine effectiveness in 2015–2016.MethodsWe enrolled patients 6 months of age or older who presented with acute respiratory illness at ambulatory care clinics in geographically diverse U.S. sites. Using a test-negative design, we estimated vaccine effectiveness as (1−OR)×100, in which OR is the odds ratio for testing positive for influenza virus among vaccinated versus unvaccinated participants. Separate estimates were calculated for the inactivated vaccines and the live attenuated vaccine.ResultsAmong 6879 eligible participants, 1309 (19%) tested positive for influenza virus, predominantly for A(H1N1)pdm09 (11%) and influenza B (7%). The effectiveness of the influenza vaccine against any influenza illness was 48% (95% confidence interval [CI], 41 to 55; P<0.001). Among children 2 to 17 years of age, the inactivated influenza vaccine was 60% effective (95% CI, 47 to 70; P<0.001), and the live attenuated vaccine was not observed to be effective (vaccine effectiveness, 5%; 95% CI, −47 to 39; P=0.80). Vaccine effectiveness against A(H1N1)pdm09 among children was 63% (95% CI, 45 to 75; P<0.001) for the inactivated vaccine, as compared with −19% (95% CI, −113 to 33; P=0.55) for the live attenuated vaccine.ConclusionsInfluenza vaccines reduced the risk of influenza illness in 2015–2016. However, the live attenuated vaccine was found to be ineffective among children in a year with substantial inactivated vaccine effectiveness. Because the 2016–2017 A(H1N1)pdm09 strain used in the live attenuated vaccine was unchanged from 2015–2016, the Advisory Committee on Immunization Practices made an interim recommendation not to use the live attenuated influenza vaccine for the 2016–2017 influenza season. (Funded by the Centers for Disease Control and Prevention and the National Institutes of Health.)

Early estimates of 2016/17 seasonal influenza vaccine effectiveness in primary care in France

BackgroundThe ongoing 2016/17 influenza epidemic in France is characterized by the circulation of A(H3N2) viruses, known to cause more severe illness among at risk populations. ObjectivesThe purpose of our study was to provide early influenza vaccine effectiveness (IVE) estimates for the ongoing influenza epidemic in France and compare these estimates over the six post-pandemic IVE. Study designWe used clinical and virological data collected in primary care by the French Sentinelles network. IVE in preventing influenza infection was estimated by the test-negative design method. The screening method was used to estimate IVE in preventing medically-attended influenza-like illness among target groups (<65year with chronic diseases and ≥65 years) since 2010/11 influenza epidemic. ResultsEarly IVE estimates in primary care against influenza A(H3N2) were 48% (95% confidence interval (CI): 22–66) overall and 39% (95% CI: −17 to 69) among elderly (aged 65 and older). In comparison to the last six epidemics, 2016/17 early IVE in preventing influenza-like illness among target groups showed VE estimates higher to those reported during the 2011/12 and 2014/15 epidemics. ConclusionsThe moderate 2016/17 IVE estimates were higher than those estimated during influenza A(H3N2) epidemics with vaccine mismatch.

2015/16 seasonal vaccine effectiveness against hospitalisation with influenza A(H1N1)pdm09 and B among elderly people in Europe: results from the I-MOVE+ project.

We conducted a multicentre test-negative case–control study in 27 hospitals of 11 European countries to measure 2015/16 influenza vaccine effectiveness (IVE) against hospitalised influenza A(H1N1)pdm09 and B among people aged ≥ 65 years. Patients swabbed within 7 days after onset of symptoms compatible with severe acute respiratory infection were included. Information on demographics, vaccination and underlying conditions was collected. Using logistic regression, we measured IVE adjusted for potential confounders. We included 355 influenza A(H1N1)pdm09 cases, 110 influenza B cases, and 1,274 controls. Adjusted IVE against influenza A(H1N1)pdm09 was 42% (95% confidence interval (CI): 22 to 57). It was 59% (95% CI: 23 to 78), 48% (95% CI: 5 to 71), 43% (95% CI: 8 to 65) and 39% (95% CI: 7 to 60) in patients with diabetes mellitus, cancer, lung and heart disease, respectively. Adjusted IVE against influenza B was 52% (95% CI: 24 to 70). It was 62% (95% CI: 5 to 85), 60% (95% CI: 18 to 80) and 36% (95% CI: -23 to 67) in patients with diabetes mellitus, lung and heart disease, respectively. 2015/16 IVE estimates against hospitalised influenza in elderly people was moderate against influenza A(H1N1)pdm09 and B, including among those with diabetes mellitus, cancer, lung or heart diseases.

Estimating Influenza Vaccine Effectiveness With the Test-Negative Design Using Alternative Control Groups: A Systematic Review and Meta-Analysis.

One important assumption in case-control studies is that control selection should be independent of exposure. Nevertheless, it has been hypothesized that virus interference might lead to a correlation between receipt of influenza vaccination and increased risk of infection with other respiratory viruses. We investigated whether such a phenomenon might affect a study design commonly used to estimate influenza vaccine effectiveness (VE). We searched publications in MEDLINE, PubMed, and Web of Science. We identified 12 studies using the test-negative design (2011–2017) that reported VE estimates separately derived by 3 alternative control groups: 1) all patients testing negative for influenza (FLU), VEFLU−; 2) patients who tested positive for other/another respiratory virus (ORV), VEORV+; and 3) patients who tested negative for all viruses in the panel (PAN), VEPAN−. These included VE estimates from 7 countries for all age groups from 2003/2004 to 2013/2014. We observed no difference in vaccination coverage between the ORV-positive and PAN-negative control groups. A total of 63 VEFLU− estimates, 62 VEORV+ estimates, and 33 VEPAN− estimates were extracted. Pooled estimates of the difference in VE (ΔVE) were very similar between groups. In meta-regression, no association was found between the selection of control group and VE estimates. In conclusion, we did not find any differences in VE estimates based on the choice of control group.

Application of an Individual-Based Transmission Hazard Model for Estimation of Influenza Vaccine Effectiveness in a Household Cohort.

Household cohort studies are an important design for the study of respiratory virus transmission. Inferences from these studies can be improved through the use of mechanistic models to account for household structure and risk as an alternative to traditional regression models. We adapted a previously described individual-based transmission hazard (TH) model and assessed its utility for analyzing data from a household cohort maintained in part for study of influenza vaccine effectiveness (VE). Households with ≥4 individuals, including ≥2 children <18 years of age, were enrolled and followed during the 2010-2011 influenza season. VE was estimated in both TH and Cox proportional hazards (PH) models. For each individual, TH models estimated hazards of infection from the community and each infected household contact. Influenza A(H3N2) infection was laboratory-confirmed in 58 (4%) subjects. VE estimates from both models were similarly low overall (Cox PH: 20%, 95% confidence interval: -57, 59; TH: 27%, 95% credible interval: -23, 58) and highest for children <9 years of age (Cox PH: 40%, 95% confidence interval: -49, 76; TH: 52%, 95% credible interval: 7, 75). VE estimates were robust to model choice, although the ability of the TH model to accurately describe transmission of influenza presents continued opportunity for analyses.

Influenza vaccine effectiveness estimates in the Dutch population from 2003 to 2014: The test-negative design case-control study with different control groups

Information about influenza vaccine effectiveness (IVE) is important for vaccine strain selection and immunization policy decisions. The test-negative design (TND) case-control study is commonly used to obtain IVE estimates. However, the definition of the control patients may influence IVE estimates. We have conducted a TND study using the Dutch Sentinel Practices of NIVEL Primary Care Database which includes data from patients who consulted the General Practitioner (GP) for an episode of acute influenza-like illness (ILI) or acute respiratory infection (ARI) with known influenza vaccination status. Cases were patients tested positive for influenza virus. Controls were grouped into those who tested (1) negative for influenza virus (all influenza negative), (2) negative for influenza virus, but positive for respiratory syncytial virus, rhinovirus or enterovirus (non-influenza virus positive), and (3) negative for these four viruses (pan-negative). We estimated the IVE over all epidemic seasons from 2003/2004 through 2013/2014, pooled IVE for influenza vaccine partial/full matched and mismatched seasons and the individual seasons using generalized linear mixed-effect and multiple logistic regression models. The overall IVE adjusted for age, GP ILI/ARI diagnosis, chronic disease and respiratory allergy was 35% (95% CI: 15–48), 64% (95% CI: 49–75) and 21% (95% CI: −1 to 39) for all influenza negative, non-influenza virus positive and pan-negative controls, respectively. In both the main and subgroup analyses IVE estimates were the highest using non-influenza virus positive controls, likely due to limiting inclusion of controls without laboratory-confirmation of a virus causing the respiratory disease.

Do influenza and pneumococcal vaccines prevent community-acquired respiratory infections among older people with diabetes and does this vary by chronic kidney disease? A cohort study using electronic health records

ObjectiveWe aimed to estimate the effectiveness of influenza and 23-valent pneumococcal polysaccharide vaccination on reducing the burden of community-acquired lower respiratory tract infection (LRTI) among older people with diabetes, and...

Mid-season real-time estimates of seasonal influenza vaccine effectiveness in persons 65 years and older in register-based surveillance, Stockholm County, Sweden, and Finland, January 2017.

Systems for register-based monitoring of vaccine effectiveness (VE) against laboratory-confirmed influenza (LCI) in real time were set up in Stockholm County, Sweden, and Finland, before start of the 2016/17 influenza season, using population-based cohort studies. Both in Stockholm and Finland, an early epidemic of influenza A(H3N2) peaked in week 52, 2016. Already during weeks 48 to 50, analyses of influenza VE in persons 65 years and above showed moderately good estimates of around 50%, then rapidly declined by week 2, 2017 to 28% and 32% in Stockholm and Finland, respectively. The sensitivity analyses, where time since vaccination was taken into account, could not demonstrate a clear decline, neither by calendar week nor by time since vaccination. Most (68%) of the samples collected from vaccinated patients belonged to the 3C.2a1 subclade with the additional amino acid substitution T135K in haemagglutinin (64%) or to subclade 3C.2a with the additional haemagglutinin substitutions T131K and R142K (36%). The proportion of samples containing these alterations increased during the studied period. These substitutions may be responsible for viral antigenic change and part of the observed VE drop. Another possible cause is poor vaccine immunogenicity in older persons. Improved influenza vaccines are needed, especially for the elderly.

Influenza Vaccine Effectiveness in the Netherlands from 2003/2004 through 2013/2014: The Importance of Circulating Influenza Virus Types and Subtypes.

Influenza vaccine effectiveness (IVE) varies over different influenza seasons and virus (sub)types/lineages. To assess the association between IVE and circulating influenza virus (sub)types/lineages, we estimated the overall and (sub)type specific IVE in the Netherlands. We conducted a test-negative case control study among subjects with influenza-like illness or acute respiratory tract infection consulting the Sentinel Practices over 11 influenza seasons (2003/2004 through 2013/2014) in the Netherlands. The adjusted IVE was estimated using generalized linear mixed modelling and multiple logistic regression. In seven seasons vaccine strains did not match the circulating viruses. Overall adjusted IVE was 40% (95% CI 18 to 56%) and 20% (95% CI -5 to 38%) when vaccine (partially)matched and mismatched the circulating viruses, respectively. When A(H3N2) was the predominant virus, IVE was 38% (95% CI 14 to 55%). IVE against infection with former seasonal A(H1N1) virus was 83% (95% CI 52 to 94%), and with B virus 67% (95% CI 55 to 76%). In conclusion IVE estimates were particularly low when vaccine mismatched the circulating viruses and A(H3N2) was the predominant influenza virus subtype. Tremendous effort is required to improve vaccine production procedure and to explore the factors that influence the IVE against A(H3N2) virus.

Real-time real-world analysis of seasonal influenza vaccine effectiveness: method development and assessment of a population-based cohort in Stockholm County, Sweden, seasons 2011/12 to 2014/15.

Real-world estimates of seasonal influenza vaccine effectiveness (VE) are important for early detection of vaccine failure. We developed a method for evaluating real-time in-season vaccine effectiveness (IVE) and overall seasonal VE. In a retrospective, register-based, cohort study including all two million individuals in Stockholm County, Sweden, during the influenza seasons from 2011/12 to 2014/15, vaccination status was obtained from Stockholm’s vaccine register. Main outcomes were hospitalisation or primary care visits for influenza (International Classification of Disease (ICD)-10 codes J09-J11). VE was assessed using Cox multivariate stratified and non-stratified analyses adjusting for age, sex, socioeconomic status, comorbidities and previous influenza vaccinations. Stratified analyses showed moderate VE in prevention of influenza hospitalisations among chronically ill adults ≥ 65 years in two of four seasons, and lower but still significant VE in one season; 53% (95% confidence interval (CI): 33–67) in 2012/13, 55% (95% CI: 25–73) in 2013/14 and 18% (95% CI: 3–31) in 2014/15. In conclusion, seasonal influenza vaccination was associated with substantial reductions in influenza-specific hospitalisation, particularly in adults ≥ 65 years with underlying chronic conditions. With the use of population-based patient register data on influenza-specific outcomes it will be possible to obtain real-time estimates of seasonal influenza VE.

Interim estimates of the effectiveness of influenza vaccination against influenza-associated hospitalization in children in Hong Kong, 2015-16.

From 1 September 2015 through 31 January 2016, we enrolled 2068 children 6 months to 17 years of age admitted to hospital with a febrile acute respiratory infection in our test‐negative study. Information on receipt of 2015–16 northern hemisphere inactivated influenza vaccination was elicited from parents or legal guardians. Using conditional logistic regression adjusting for age and matching on calendar time, we estimated influenza vaccine effectiveness against hospitalization with influenza A or B to be 79.2% (95% confidence interval: 42.0%–92.4%). Annual influenza vaccination should be more widely used in children in Hong Kong.

Pooled influenza vaccine effectiveness estimates for Australia, 2012-2014.

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.

Waning protection of influenza vaccine against mild laboratory confirmed influenza A(H3N2) and B in Spain, season 2014–15

IntroductionThe 2014/15 influenza season in Spain was dominated by the circulation of drifted A(H3N2) and co-circulation of B viruses. We present the final estimates of influenza vaccine effectiveness (IVE) against confirmed influenza A(H3N2) and B its evolution along the season and with time since vaccination. MethodsWe used data collected on influenza like illness patients (ILI), systematically swabbed for the presence of influenza viruses within the Spanish Influenza Sentinel Surveillance System (SISS) and a restricted observational study (cycEVA). We used a test negative case–control design to compare influenza confirmed cases with negative controls. We estimated the IVE through a logistic regression model adjusting for potential confounders. The evolution of IVE was studied in early and late stages of the epidemic, and in different time intervals between receiving influenza vaccination and the onset of symptoms. ResultsAt the end of the season we have found low and moderate IVE point estimates against influenza A(H3N2) and B, respectively, in all ages and target groups for vaccination. An IVE decreased from an early value of 37% to a late of −76% against influenza A(H3N2), and similarly, 84% vs −4% against Influenza B. When the onset of symptoms occurred more than three months after vaccination, the decrease of IVE was slower and milder against influenza B than against influenza A(H3N2). No significant change in the percentage of circulating drifted influenza A(H3N2) strains belonging to the 3c.2a and 3c.3a clades could be identified through the season. ConclusionsIn a season dominated by drifted A(H3N2) circulating virus, the vaccine offered little or no protection against A(H3N2) infection but had a moderate protective effect against influenza B. Efforts should be put in developing influenza vaccines that maintain their protective capabilities throughout the season and could stimulate a potentially broad immune response against diverse influenza strains.