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.     

The impact of selection bias on vaccine effectiveness estimates from test-negative studies

IntroductionEstimates of vaccine effectiveness (VE) from test-negative studies may be subject to selection bias. In the context of influenza VE, we used simulations to identify situations in which meaningful selection bias can occur. We also analyzed observational study data for evidence of selection bias.MethodsFor the simulation study, we defined a hypothetical population whose members are at risk for acute respiratory illness (ARI) due to influenza and other pathogens. An unmeasured “healthcare seeking proclivity” affects both probability of vaccination and probability of seeking care for an ARI. We varied the direction and magnitude of these effects and identified situations where meaningful bias occurred. For the observational study, we reanalyzed data from the United States Influenza VE Network, an ongoing test-negative study. We compared “bias-naïve” VE estimates to bias-adjusted estimates, which used data from the source populations to correct for sampling bias.ResultsIn the simulation study, an unmeasured care-seeking proclivity could create selection bias if persons with influenza ARI were more (or less) likely to seek care than persons with non-influenza ARI. However, selection bias was only meaningful when rates of care seeking between influenza ARI and non-influenza ARI were very different. In the observational study, the bias-naïve VE estimate of 55% (95% CI, 47-–62%) was trivially different from the bias-adjusted VE estimate of 57% (95% CI, 49-–63%).ConclusionsIn combination, these studies suggest that while selection bias is possible in test-negative VE studies, this bias in unlikely to be meaningful under conditions likely to be encountered in practice. Researchers and public health officials can continue to rely on VE estimates from test-negative studies.     

The test-negative design for estimating influenza vaccine effectiveness

Objective

The test-negative design has emerged in recent years as the preferred method for estimating influenza vaccine effectiveness (VE) in observational studies. However, the methodologic basis of this design has not been formally developed.

Methods

In this paper we develop the rationale and underlying assumptions of the test-negative study. Under the test-negative design for influenza VE, study subjects are all persons who seek care for an acute respiratory illness (ARI). All subjects are tested for influenza infection. Influenza VE is estimated from the ratio of the odds of vaccination among subjects testing positive for influenza to the odds of vaccination among subjects testing negative.

Results

With the assumptions that (a) the distribution of non-influenza causes of ARI does not vary by influenza vaccination status, and (b) VE does not vary by health care-seeking behavior, the VE estimate from the sample can generalized to the full source population that gave rise to the study sample. Based on our derivation of this design, we show that test-negative studies of influenza VE can produce biased VE estimates if they include persons seeking care for ARI when influenza is not circulating or do not adjust for calendar time.

Conclusions

The test-negative design is less susceptible to bias due to misclassification of infection and to confounding by health care-seeking behavior, relative to traditional case-control or cohort studies. The cost of the test-negative design is the additional, difficult-to-test assumptions that incidence of non-influenza respiratory infections is similar between vaccinated and unvaccinated groups within any stratum of care-seeking behavior, and that influenza VE does not vary across care-seeking strata.     

Estimates of 2012/13 influenza vaccine effectiveness using the case test-negative control design with different influenza negative control groups

Background

In recent years several reports of influenza vaccine effectiveness (VE) have been made early for public health decision. The majority of these studies use the case test-negative control design (TND), which has been showed to provide, under certain conditions, unbiased estimates of influenza VE. Nevertheless, discussions have been taken on the best influenza negative control group to use. The present study aims to contribute to the knowledge on this field by comparing influenza VE estimates using three test-negative controls: all influenza negative, non-influenza respiratory virus and pan-negative.

Methods

Incident ILI patients were prospectively selected and swabbed by a sample of general practitioners. Cases were ILI patients tested positive for influenza and controls ILI patients tested negative for influenza. The influenza negative control group was divided into non-influenza virus control group and pan-negative control group. Data were collected on vaccination status and confounding factors. Influenza VE was estimated as one minus the odds ratio of been vaccinated in cases versus controls adjusted for confounding effect by logistic regression.

Results

Confounder adjusted influenza VE against medically attended laboratory-confirmed influenza was 68.4% (95% CI: 20.7–87.4%) using all influenza negatives controls, 82.1% (95% CI: 47.6–93.9%) using non-influenza controls and 49.4% (95% CI: −44.7% to 82.3%) using pan-negative controls.

Conclusions

Influenza VE estimates differed according to the influenza negative control group used. These results are in accordance with the expected under the hypothesis of differential viral interference between influenza vaccinated and unvaccinated individuals. Given the wide importance of TND study further studies should be conducted in order to clarify the observed differences.     

Assessment of temporally-related acute respiratory illness following influenza vaccination

BackgroundA barrier to influenza vaccination is the misperception that the inactivated vaccine can cause influenza. Previous studies have investigated the risk of acute respiratory illness (ARI) after influenza vaccination with conflicting results. We assessed whether there is an increased rate of laboratory-confirmed ARI in post-influenza vaccination periods.MethodsWe conducted a cohort sub-analysis of children and adults in the MoSAIC community surveillance study from 2013 to 2016. Influenza vaccination was confirmed through city or hospital registries. Cases of ARI were ascertained by twice-weekly text messages to household to identify members with ARI symptoms. Nasal swabs were obtained from ill participants and analyzed for respiratory pathogens using multiplex PCR. The primary outcome measure was the hazard ratio of laboratory-confirmed ARI in individuals post-vaccination compared to other time periods during three influenza seasons.ResultsOf the 999 participants, 68.8% were children, 30.2% were adults. Each study season, approximately half received influenza vaccine and one third experienced ≥1 ARI. The hazard of influenza in individuals during the 14-day post-vaccination period was similar to unvaccinated individuals during the same period (HR 0.96, 95% CI [0.60, 1.52]). The hazard of non-influenza respiratory pathogens was higher during the same period (HR 1.65, 95% CI [1.14, 2.38]); when stratified by age the hazard remained higher for children (HR 1·71, 95% CI [1.16, 2.53]) but not for adults (HR 0.88, 95% CI [0.21, 3.69]).ConclusionAmong children there was an increase in the hazard of ARI caused by non-influenza respiratory pathogens post-influenza vaccination compared to unvaccinated children during the same period. Potential mechanisms for this association warrant further investigation. Future research could investigate whether medical decision-making surrounding influenza vaccination may be improved by acknowledging patient experiences, counseling regarding different types of ARI, and correcting the misperception that all ARI occurring after vaccination are caused by influenza.     

The effect of seasonal influenza vaccine on medically-attended influenza and non-influenza respiratory viruses infections at primary care level,Hong Kong SAR, 2017/18 to 2019/20

Effectiveness of seasonal influenza vaccine (SIV) varies with the degree of matching with the vaccine and circulating viruses. We continued our SIV effectiveness against medically-attended influenza-like illness (ILI) under the Department of Health Hong Kong’s sentinel private medical practitioners (PMP) network, using the test-negative case-control design, for the 2018/19 and 2019/20 season. In addition, we studied the potential interference between SIV and ILI caused by non-influenza respiratory viruses (NIRV) based on data collated from 2017/18 to 2019/20 seasons. 3404 patients were analysed. Across the 2017/18 to 2019/20 seasons, the vaccine effectiveness (VE) of SIV was 44% (95% CI 30–56%) against pan-negative controls, 57% (95%CI. 42–68%) against NIRV controls and 50% (95%CI 38–59%) against both. SIV was moderately effective against medically-attended ILI caused by influenza A/B in both 2018/19 and 2019/20 winter seasons (53.2% (95%CI 36.7–65.5%) and 41.8% (95%CI 6.3–64.1%), respectively). The VE against the main circulating subtype, influenza A(H1), was higher for the 2018/19 season (57.2% (95%CI 39.8–69.9%), compared to 34.6% (95%CI −9.6–61.4%) in the 2019/20 season). When compared to pan negative controls, those with single NIRV infections were similarly likely to have received SIV (OR 1.05 (95%CI 0.72–1.54) within the influenza season; OR 0.97 (95%CI 0.73–1.29) when including non-influenza seasons). Analyses by type of virus showed no increased risk of SIV identified among those with single infections of EV/RV, HMPV and parainfluenza but a 2-fold increased risk was shown for those with single infections of adenovirus and parainfluenza virus (adenovirus: OR 2.54 (95%CI 1.24–5.14) within influenza season and OR 1.78 (95%CI 1.01–3.09) for the whole period; parainfluenza virus: OR 2.01 (95%CI 1.22–3.29) within influenza season and OR 1.89 (95%CI 1.29–2.76) for the whole period). SIV programme and surveillance of influenza and NIRV, including SARS-CoV-2, should continue during the COVID-19 pandemic.     

Influenza vaccination coverage among persons seeking outpatient medical care for acute respiratory illness in five states in the United States, 2011–2012 through 2018–2019

BackgroundIn the United States (U.S.), annual influenza vaccination has been recommended for all persons aged ≥6 months with the Healthy People 2020 coverage target of 70%. However, vaccination coverage has remained around 42–49% during the past eight influenza seasons. We sought to quantify influenza vaccination coverage and factors associated with vaccination in persons seeking outpatient medical care for an acute respiratory illness (ARI).MethodsWe enrolled outpatients aged ≥6 months with ARI from >50 U.S. clinics from 2011 to 2012 through 2018–2019 influenza seasons and tested for influenza with molecular assays. Vaccination status was based on documented receipt of the current season’s influenza vaccine. We estimated vaccination coverage among influenza-negative study participants by study site, age, and season, and compared to state-level influenza coverage estimates in the general population based on annual immunization surveys. We used multivariable logistic regression to examine factors independently associated with receipt of influenza vaccines.ResultsWe enrolled 45,424 study participants with ARI who tested negative for influenza during the study period. Annual vaccination coverage among influenza-negative ARI patients and the general population in the participating states averaged 55% (range: 47–62%), and 52% (range: 46–54%), respectively. Among enrollees, coverage was highest among adults aged ≥65 years (82%; range, 80–85%) and lowest among adolescents aged 13–17 years (38%; range, 35–41%). Factors significantly associated with non-vaccination included non-White race, no college degree, exposure to cigarette smoke, absence of high-risk conditions, and not receiving prior season influenza vaccine.ConclusionsInfluenza vaccination coverage over eight seasons among outpatients with non-influenza respiratory illness was slightly higher than coverage in the general population but 15% lower than national targets. Increased efforts to promote vaccination especially in groups with lower coverage are warranted to attain optimal health benefits of influenza vaccine.     

Evidence of bias in estimates of influenza vaccine effectiveness in seniors

BACKGROUND: Numerous observational studies have reported that seniors who receive influenza vaccine are at substantially lower risk of death and hospitalization during the influenza season than unvaccinated seniors. These estimates could be influenced by differences in underlying health status between the vaccinated and unvaccinated groups. Since a protective effect of vaccination should be specific to influenza season, evaluation of non-influenza periods could indicate the possible contribution of bias to the estimates observed during influenza season. METHODS: We evaluated a cohort of 72,527 persons 65 years of age and older followed during an 8 year period and assessed the risk of death from any cause, or hospitalization for pneumonia or influenza, in relation to influenza vaccination, in periods before, during, and after influenza seasons. Secondary models adjusted for covariates defined primarily by diagnosis codes assigned to medical encounters. RESULTS: The relative risk of death for vaccinated persons compared with unvaccinated persons was 0.39 [95% confidence interval (95% CI), 0.33-0.47] before influenza season, 0.56 (0.52-0.61) during influenza season, and 0.74 (0.67-0.80) after influenza season. The relative risk of pneumonia hospitalization was 0.72 (0.59-0.89) before, 0.82 (0.75-0.89) during, and 0.95 (0.85-1.07) after influenza season. Adjustment for diagnosis code variables resulted in estimates that were further from the null, in all time periods. CONCLUSIONS: The reductions in risk before influenza season indicate preferential receipt of vaccine by relatively healthy seniors. Adjustment for diagnosis code variables did not control for this bias. In this study, the magnitude of the bias demonstrated by the associations before the influenza season was sufficient to account entirely for the associations observed during influenza season.     

Comparison of test-negative and syndrome-negative controls in SARS-CoV-2 vaccine effectiveness evaluations for preventing COVID-19 hospitalizations in the United States

BackgroundTest-negative design (TND) studies have produced validated estimates of vaccine effectiveness (VE) for influenza vaccine studies. However, syndrome-negative controls have been proposed for differentiating bias and true estimates in VE evaluations for COVID-19. To understand the use of alternative control groups, we compared characteristics and VE estimates of syndrome-negative and test-negative VE controls.MethodsAdults hospitalized at 21 medical centers in 18 states March 11–August 31, 2021 were eligible for analysis. Case patients had symptomatic acute respiratory infection (ARI) and tested positive for SARS-CoV-2. Control groups were test-negative patients with ARI but negative SARS-CoV-2 testing, and syndrome-negative controls were without ARI and negative SARS-CoV-2 testing. Chi square and Wilcoxon rank sum tests were used to detect differences in baseline characteristics. VE against COVID-19 hospitalization was calculated using logistic regression comparing adjusted odds of prior mRNA vaccination between cases hospitalized with COVID-19 and each control group.Results5811 adults (2726 cases, 1696 test-negative controls, and 1389 syndrome-negative controls) were included. Control groups differed across characteristics including age, race/ethnicity, employment, previous hospitalizations, medical conditions, and immunosuppression. However, control-group-specific VE estimates were very similar. Among immunocompetent patients aged 18–64 years, VE was 93 % (95 % CI: 90–94) using syndrome-negative controls and 91 % (95 % CI: 88–93) using test-negative controls.ConclusionsDespite demographic and clinical differences between control groups, the use of either control group produced similar VE estimates across age groups and immunosuppression status. These findings support the use of test-negative controls and increase confidence in COVID-19 VE estimates produced by test-negative design studies.     

Impacts on influenza A(H1N1)pdm09 infection from cross-protection of seasonal trivalent influenza vaccines and A(H1N1)pdm09 vaccines: systematic review and meta-analyses

Cross-protection by seasonal trivalent influenza vaccines (TIVs) against pandemic influenza A H1N1 2009 (now known as A[H1N1]pdm09) infection is controversial; and the vaccine effectiveness (VE) of A(H1N1)pdm09 vaccines has important health-policy implications. Systematic reviews and meta-analyses are needed to assess the impacts of both seasonal TIVs and A(H1N1)pdm09 vaccines against A(H1N1)pdm09.We did a systematic literature search to identify observational and/or interventional studies reporting cross-protection of TIV and A(H1N1)pdm09 VE from when the pandemic started (2009) until July 2011. The studies fulfilling inclusion criteria were meta-analysed. For cross-protection and VE, respectively, we stratified by vaccine type, study design and endpoint. Seventeen studies (104,781 subjects) and 10 studies (2,906,860 subjects), respectively, reported cross-protection of seasonal TIV and VE of A(H1N1)pdm09 vaccines; six studies (17,229 subjects) reported on both. Thirteen studies (95,903 subjects) of cross-protection, eight studies (859,461 subjects) of VE, and five studies (9,643 subjects) of both were meta-analysed and revealed: (1) cross-protection for confirmed illness was 19% (95% confident interval=13-42%) based on 13 case-control studies with notable heterogeneity. A higher cross-protection of 34% (9-52%) was found in sensitivity analysis (excluding five studies with moderate/high risk of bias). Further exclusion of studies that recruited early in the pandemic (when non-recipients of TIV were more likely to have had non-pandemic influenza infection that may have been cross-protective) dramatically reduced heterogeneity. One RCT reported cross-protection of 38% (19-53%) for confirmed illness. One case-control study reported cross-protection of 50% (40-59%) against hospitalisation. (2) VE of A(H1N1)pdm09 for confirmed illness was 86% (73-93%) based on 11 case-control studies and 79% (22-94%) based on two cohort studies; VE against medically-attended ILI was 32% (8-50%) in one cohort study. TIVs provided moderate cross-protection against both laboratory-confirmed A(H1N1)pdm09 illness (based on eight case-control studies with low risk of bias and one RCT) and also hospitalisation. A finding of increased risk from seasonal vaccine was limited to cases recruited early in the pandemic. A(H1N1)pdm09 vaccines were highly effective against confirmed A(H1N1)pdm09 illness. Although cross-protection was less than the direct effect of strain-specific vaccination against A(H1N1)pdm09, TIV was generally beneficial before A(H1N1)pdm09 vaccine was available.     

The case test-negative design for studies of the effectiveness of influenza vaccine

Background

A modification to the case–control study design has become popular to assess vaccine effectiveness (VE) against viral infections. Subjects with symptomatic illness seeking medical care are tested by a highly specific polymerase chain reaction (PCR) assay for the detection of the infection of interest. Cases are subjects testing positive for the virus; those testing negative represent the comparison group. Influenza and rotavirus VE studies using this design are often termed “test-negative case-control” studies, but this design has not been formally described or evaluated. We explicitly state several assumptions of the design and examine the conditions under which VE estimates derived with it are valid and unbiased.

Methods

We derived mathematical expressions for VE estimators obtained using this design and examined their statistical properties. We used simulation methods to test the validity of the estimators and illustrate their performance using an influenza VE study as an example.

Results

Because the marginal ratio of cases to non-cases is unknown during enrollment, this design is not a traditional case-control study; we suggest the name “case test-negative” design. Under sets of increasingly general assumptions, we found that the case test-negative design can provide unbiased VE estimates. However, differences in health care-seeking behavior among cases and non-cases by vaccine status, strong viral interference, or modification of the probability of symptomatic illness by vaccine status can bias VE estimates.

Conclusions

Vaccine effectiveness estimates derived from case test-negative studies are valid and unbiased under a wide range of assumptions. However, if vaccinated cases are less severely ill and seek care less frequently than unvaccinated cases, then an appropriate adjustment for illness severity is required to avoid bias in effectiveness estimates. Viral interference will lead to a non-trivial bias in the vaccine effectiveness estimate from case test-negative studies only when incidence of influenza is extremely high and duration of transient non-specific immunity is long.     

Can routinely collected laboratory and health administrative data be used to assess influenza vaccine effectiveness? Assessing the validity of the Flu and Other Respiratory Viruses Research (FOREVER) Cohort

BackgroundLinking data on laboratory specimens collected during clinical practice with health administrative data permits highly powered vaccine effectiveness (VE) studies to be conducted at relatively low cost, but bias from using convenience samples is a concern. We evaluated the validity of using such data for estimating VE.MethodsWe created the Flu and Other Respiratory Viruses Research (FOREVER) Cohort by linking individual-level data on respiratory virus laboratory tests, hospitalizations, emergency department visits, and physician services. For community-dwelling adults aged > 65 years, we assessed the presence and magnitude of information and selection biases, generated VE estimates under various conditions, and compared our VE estimates with those from other studies.ResultsWe included 65,648 unique testing episodes obtained from 54,434 individuals during the 2010–11 to 2015–16 influenza seasons. To examine information bias, we found the proportion testing positive for influenza for patients with unknown interval from illness onset to specimen collection was more similar to patients for whom illness onset date was ≤ 7 days before specimen collection than to patients for whom illness onset was > 7 days before specimen collection. To assess the presence of selection bias, we found the likelihood of influenza testing was comparable between vaccinated and unvaccinated individuals, although the adjusted odds ratios were significantly greater than 1 for some healthcare settings and during some influenza seasons. Over 6 seasons, VE estimates ranged between 36% (95%CI, 27–44%) in 2010–11 and 5% (95%CI, –2, 11%) in 2014–15. VE estimates were similar under a range of conditions, but were consistently higher when accounting for misclassification of vaccination status through a quantitative sensitivity analysis. VE estimates from the FOREVER Cohort were comparable to those from other studies.ConclusionsRoutinely collected laboratory and health administrative data contained in the FOREVER Cohort can be used to estimate influenza VE in community-dwelling older adults.     

Incidence of medically attended influenza infection and cases averted by vaccination, 2011/2012 and 2012/2013 influenza seasons

BackgroundWe estimated the burden of outpatient influenza and cases prevented by vaccination during the 2011/2012 and 2012/2013 influenza seasons using data from the United States Influenza Vaccine Effectiveness (US Flu VE) Network.MethodsWe defined source populations of persons who could seek care for acute respiratory illness (ARI) at each of the five US Flu VE Network sites. We identified all members of the source population who were tested for influenza during US Flu VE influenza surveillance. Each influenza-positive subject received a sampling weight based on the proportion of source population members who were tested for influenza, stratified by site, age, and other factors. We used the sampling weights to estimate the cumulative incidence of medically attended influenza in the source populations. We estimated cases averted by vaccination using estimates of cumulative incidence, vaccine coverage, and vaccine effectiveness.ResultsCumulative incidence of medically attended influenza ranged from 0.8% to 2.8% across sites during 2011/2012 and from 2.6% to 6.5% during the 2012/2013 season. Stratified by age, incidence ranged from 1.2% among adults 50 years of age and older in 2011/2012 to 10.9% among children 6 months to 8 years of age in 2012/2013. Cases averted by vaccination ranged from 4 to 41 per 1000 vaccinees, depending on the study site and year.ConclusionsThe incidence of medically attended influenza varies greatly by year and even by geographic region within the same year. The number of cases averted by vaccination varies greatly based on overall incidence and on vaccine coverage.     

Effectiveness of pandemic and seasonal influenza vaccines in preventing pandemic influenza-associated hospitalization

Vaccines are leading pharmacological measures for limiting the impact of pandemic influenza in the community. The objective of this study was to investigate the effectiveness of influenza (pandemic and seasonal) vaccines in preventing pandemic influenza-associated hospitalization. We conducted a multicenter matched case-control study in 36 Spanish hospitals. Patients hospitalized with confirmed pandemic influenza between November 2009 and February 2010 and two hospitalized controls per case, matched according to age, date of hospitalization and province of residence, were selected. Multivariate analysis was performed using conditional logistic regression. Subjects were considered vaccinated if they had received the vaccine >14 days (seasonal influenza vaccine) or >7 days (pandemic influenza vaccine) before the onset of symptoms (cases) or the onset of symptoms of the matched case (controls). For the pandemic influenza vaccine, vaccination effectiveness (VE) was estimated taking into account only patients recruited from November 23, 2009, seven days after the beginning of the pandemic influenza vaccination campaign. 638 cases and 1250 controls were included. The adjusted VE of the pandemic vaccine in the ≥18 years age group was 74.2% (95% CI, 29-90) and that of the influenza seasonal vaccine 15.0% (-34 to 43). The recommendation of influenza vaccination should be reinforced as a regular measure to reduce influenza-associated hospitalization during pandemics and seasonal epidemics.     

Influenza vaccine effectiveness among patients with high-risk medical conditions in the United States, 2012–2016

BackgroundAnnual influenza vaccination has been recommended for persons with high-risk conditions since the 1960s. However, few estimates of influenza vaccine effectiveness (VE) for persons with high-risk conditions are available.MethodsData from the U.S. Influenza Vaccine Effectiveness Network from 2012 to 2016 were analyzed to compare VE of standard-dose inactivated vaccines against medically-attended influenza among patients aged ≥6 months with and without high-risk medical conditions. Patients with acute respiratory illness were tested for influenza by RT-PCR. Presence of high-risk conditions and vaccination status were obtained from medical records. VE by influenza virus type/subtype and age group was calculated for patients with and without high-risk conditions using the test-negative design. Interaction terms were used to test for differences in VE by high-risk conditions.ResultsOverall, 9643 (38%) of 25,369 patients enrolled during four influenza seasons had high-risk conditions; 2213 (23%) tested positive for influenza infection. For all ages, VE against any influenza was lower among patients with high-risk conditions (41%, 95% CI: 35–47%) than those without (48%, 95% CI: 43–52%; P-for-interaction = 0.02). For children aged <18 years, VE against any influenza was 51% (95% CI: 39–61%) and 52% (95% CI: 39–61%) among those with and without high-risk conditions, respectively (P-for-interaction = 0.54). For adults aged ≥18 years, VE against any influenza was 38% (95% CI: 30–45%) and 44% (95% CI: 38–50%) among those with and without high-risk conditions, respectively (P-for-interaction = 0.21). For both children aged <18 and adults aged ≥18 years, VEs against illness related to influenza A(H3N2), A(H1N1)pdm09, and influenza B virus infection were similar among those with and without high-risk conditions.ConclusionsInfluenza vaccination provided protection against medically-attended influenza among patients with high-risk conditions, at levels approaching those observed among patients without high-risk conditions. Results from our analysis support recommendations of annual vaccination for patients with high-risk conditions.     

Are influenza-associated morbidity and mortality estimates for those ≥65 in statistical databases accurate,and an appropriate test of influenza vaccine effectiveness?

PurposesTo assess the accuracy of estimates using statistical databases of influenza-associated morbidity and mortality, and precisely measure influenza vaccine effectiveness.Principal resultsLaboratory testing of influenza is incomplete. Death certificates under-report influenza. Statistical database models are used as an alternative to randomised controlled trials (RCTs) to assess influenza vaccine effectiveness. Evidence of the accuracy of influenza morbidity and mortality estimates was sought from: (1) Studies comparing statistical models. For four studies Poisson and ARIMA models produced higher estimates than Serfling, and Serfling higher than GLM. Which model is more accurate is unknown. (2) Studies controlling confounders. Fourteen studies mostly controlled one confounder (one controlled comorbidities), and limited control of confounders limits accuracy.Evidence for vaccine effectiveness was sought from(1) Studies of regions with increasing vaccination rates. Of five studies two controlled for confounders and one found a positive vaccination effect. Three studies did not control confounders and two found no effect of vaccination. (2) Studies controlling multiple confounders. Of thirteen studies only two found a positive vaccine effect and no mortality differences between vaccinees and non-vaccinees in non-influenza seasons, showing confounders were controlled.Key problems are insufficient testing for influenza, using influenza-like illness, heterogeneity of seasonal and pandemic influenza, population aging, and incomplete confounder control (co-morbidities, frailty, vaccination history) and failure to demonstrate control of confounders by proving no mortality differences between vaccinees and non-vaccinees in non-influenza seasons.Major conclusionsImproving model accuracy requires proof of no mortality differences in pre-influenza periods between the vaccinated and non-vaccinated groups, and reduction in influenza morbidity and mortality in seasons with a good vaccine match, more virulent strains, in the younger elderly with less immune senescence, and specific outcomes (laboratory-confirmed outcomes, pneumonia deaths).Proving influenza vaccine effectiveness requires appropriately powered RCTs, testing participants with RT-PCR tests, and comprehensively monitoring morbidity and mortality.     

Interim influenza vaccine effectiveness: A good proxy for final estimates in Spain in the seasons 2010–2014

IntroductionThe agreement between interim and final influenza vaccine effectiveness (VE) estimates would support the use of interim assessments as a proxy for final VE results to guide health authorities in influenza prevention. We aimed to compare interim/final VE estimates in Spain.MethodsWe used a test-negative case-control study (cycEVA) for 2010/11–2013/14 seasons. Sensitivity analyses were carried out by type/subtype of influenza virus and by target groups for vaccination.ResultsIn general, interim estimates were higher compared to end-season estimates. Interim and final VE differences were higher for the target groups compared to all population. Subtype-specific interim/final VE estimates showed greater concordance (3–13%) than for any virus (7–24%).ConclusionIn Spain, interim influenza VE estimates over 2010–2014 were a good proxy of the final protection of the vaccine. Interim and final estimates showed greater concordance for all population and if performed subtype-specific.