Seasonal influenza vaccine effectiveness estimates: Development of a parsimonious case test negative model using a causal approach

Brand-specific estimates of influenza vaccine effectiveness for the 2021-2022 season in Europe: results from the DRIVE multi-stakeholder study platform

Introduction

Development of Robust and Innovative Vaccine Effectiveness (DRIVE) was a European public-private partnership (PPP) that aimed to provide annual, brand-specific estimates of influenza vaccine effectiveness (IVE) for regulatory and public health purposes. DRIVE was launched in 2017 under the umbrella of the Innovative Medicines Initiative (IMI) and conducted IVE studies from its pilot season in 2017-2018 to its final season in 2021-2022.

Methods

In 2021-2022, DRIVE conducted four primary care-based test-negative design (TND) studies (Austria, Italy, Iceland, and England; involving >1,000 general practitioners), nine hospital-based TND studies (France, Iceland, Italy, Romania, and Spain, for a total of 21 hospitals), and one population-based cohort study in Finland. In the TND studies, patients with influenza-like illness (primary care) or severe acute respiratory infection (hospital) were enrolled, and laboratory tested for influenza using RT-PCR. Study contributor-specific IVE was calculated using logistic regression, adjusting for age, sex, and calendar time, and pooled by meta-analysis.

Results

In 2021-2022, pooled confounder-adjusted influenza vaccine effectiveness (IVE) estimates against laboratory-confirmed influenza (LCI) overall and per type and subtype/lineage was produced, albeit with wide confidence intervals (CI). The limited circulation of influenza in Europe did not allow the network to reach the optimal sample size to produce precise IVE estimates for all the brands included. The most significant IVE estimates were 76% (95% CI 23%-93%) for any vaccine and 81% (22%-95%) for Vaxigrip Tetra in adults ≥65 years old and 64% (25%-83%) for Fluenz Tetra in children (TND primary care setting), 85% (12%-97%) for any vaccine in adults 18-64 years (TND hospital setting), and 38% (1%-62%) in children 6 months-6 years (population-based cohort, mixed setting).

Discussion

Over five seasons, DRIVE collected data on >35,000 patients, more than 60 variables, and 13 influenza vaccines. DRIVE demonstrated that estimating brand-specific IVE across Europe is possible, but achieving sufficient sample size to obtain precise estimates for all relevant stratifications remains a challenge. Finally, DRIVE's network of study contributors and lessons learned have greatly contributed to the development of the COVID-19 vaccine effectiveness platform COVIDRIVE.

Investigating confounding in network-based test-negative design influenza vaccine effectiveness studies-Experience from the DRIVE project

BACKGROUND

Establishing a large study network to conduct influenza vaccine effectiveness (IVE) studies while collecting appropriate variables to account for potential bias is important; the most relevant variables should be prioritized. We explored the impact of potential confounders on IVE in the DRIVE multi-country network of sites conducting test-negative design (TND) studies.

METHODS

We constructed a directed acyclic graph (DAG) to map the relationship between influenza vaccination, medically attended influenza infection, confounders, and other variables. Additionally, we used the Development of Robust and Innovative Vaccines Effectiveness (DRIVE) data from the 2018/2019 and 2019/2020 seasons to explore the effect of covariate adjustment on IVE estimates. The reference model was adjusted for age, sex, calendar time, and season. The covariates studied were presence of at least one, two, or three chronic diseases; presence of six specific chronic diseases; and prior healthcare use. Analyses were conducted by site and subsequently pooled.

RESULTS

The following variables were included in the DAG: age, sex, time within influenza season and year, health status and comorbidities, study site, health-care-seeking behavior, contact patterns and social precautionary behavior, socioeconomic status, and pre-existing immunity. Across all age groups and settings, only adjustment for lung disease in older adults in the primary care setting resulted in a relative change of the IVE point estimate >10%.

CONCLUSION

Our study supports a parsimonious approach to confounder adjustment in TND studies, limited to adjusting for age, sex, and calendar time. Practical implications are that necessitating fewer variables lowers the threshold for enrollment of sites in IVE studies and simplifies the pooling of data from different IVE studies or study networks.

Brand-specific influenza vaccine effectiveness estimates during 2019/20 season in Europe - Results from the DRIVE EU study platform

DRIVE (Development of Robust and Innovative Vaccine Effectiveness) is an IMI funded public-private platform that aims to annually estimate brand-specific influenza vaccine effectiveness (IVE), for public health and regulatory purposes. IVE analyses and reporting are conducted by public partners in the consortium. In 2019/20, four primary care-based test-negative design (TND) studies (Austria, England, Italy (n = 2)), eight hospital-based TND studies (Finland, France, Italy, Romania, Spain (n = 4)), and one population-based cohort study (Finland) were conducted. The COVID-19 pandemic affected influenza surveillance in all participating study sites, therefore the study period was truncated on February 29, 2020. Age-stratified (6 m-17y, 18-64y, ≥65y), confounder-adjusted, site-specific adjusted IVE estimates were calculated and pooled through meta-analysis. Parsimonious confounder-adjustment was performed, adjusting the estimates for age, sex and calendar time. TND studies included 3531 cases (351 vaccinated) and 5546 controls (1415 vaccinated) of all ages. IVE estimates were available for 8/11 brands marketed in Europe in 2019. Most children and adults < 64y were captured in primary care setting and the most frequently observed vaccine brand was Vaxigrip Tetra. The estimate against any influenza for Vaxigrip Tetra in primary care setting was 61% (95%CI 38-77) in children and 32% (95%CI -13-59) in adults up to 64y. Most adults ≥ 65y were captured in hospital setting and the most frequently observed brand was Fluad, with an estimate of 52% (95%CI 27-68). The population-based cohort covered 511,854 person-years and two vaccine brands. In children aged 2-6y, the IVE against any influenza was 68% (95%CI 58-75) for Fluenz Tetra and 71% (56-80) for Vaxigrip Tetra. In adults ≥ 65y, IVE against any influenza was 29% (20-36) for Vaxigrip Tetra. DRIVE is a growing platform. Public health institutes with surveillance data and hospitals in countries with high influenza vaccine coverage are encouraged to join DRIVE.

Challenges and Adaptation of a European Influenza Vaccine Effectiveness Study Platform in Response to the COVID-19 Emergence: Experience from the DRIVE Project

The Development of Robust and Innovative Vaccine Effectiveness (DRIVE) project is a public-private partnership aiming to build capacity in Europe for yearly estimation of brand-specific influenza vaccine effectiveness (IVE). DRIVE is a five-year project funded by IMI (Innovative Medicines Initiative). It was initiated as a response to the guidance on influenza vaccines by EMA (European Medicines Agency), which advised vaccine manufacturers to work with public health institutes to set up a joint IVE study platform. The COVID-19 pandemic reached Europe in February 2020 and overlapped with the 2019/2020 influenza season only in the last weeks. However, several elements of the DRIVE study network were impacted. The pandemic specifically affected the study sites' routines and the subsequent assessment of the 2019/20 influenza season. Moreover, the current social distancing measures and lockdown policies across Europe are expected to also limit the circulation of influenza for the 2020/21 season, and therefore the impact of COVID-19 will be higher than in the season 2019/20. Consequently, DRIVE has planned to adapt its study platform to the COVID-19 challenge, encompassing several COVID-19 particularities in the study procedures, data collection and IVE analysis for the 2020/21 season. DRIVE will study the feasibility of implementing these COVID-19 components and establish the foundations of future COVID-19 vaccine effectiveness studies.

Vaccine effectiveness against laboratory-confirmed influenza in Europe - Results from the DRIVE network during season 2018/19

The DRIVE project aims to establish a sustainable network to estimate brand-specific influenza vaccine effectiveness (IVE) annually. DRIVE is a public-private partnership launched in response to EMA guidance that requires effectiveness evaluation from manufacturers for all individual influenza vaccine brands every season. IVE studies are conducted by public partners in DRIVE. Private partners (vaccine manufacturers from the European Federation of Pharmaceutical Industries and Association (EFPIA)) provide written feedback moderated by an independent scientific committee. Test-negative design (TND) case-control studies (4 in primary care and five in hospital) were conducted in six countries in Europe during the 2018/19 season. Site-specific confounder-adjusted vaccine effectiveness (VE) estimates for any vaccine exposure were calculated by age group (<18 years (y), 18-64y and 65 + y) and pooled by setting (primary care, hospital) through random effects meta-analysis. In addition, one population-based cohort study was conducted in Finland. TND studies included 3339 cases and 6012 controls; seven vaccine brands were reported. For ages 65 + y, pooled VE against any influenza strain was estimated at 27% (95%CI 6-44) in hospital setting. Sample size was insufficient for meaningful IVE estimates in other age groups, in the primary care setting, or by vaccine brand. The population-based cohort study included 274,077 vaccinated and 494,337 unvaccinated person-years, two vaccine brands were reported. Brand-specific IVE was estimated for Fluenz Tetra (36% [95%CI 24-45]) for ages 2-6y, Vaxigrip Tetra (54% [43-62]) for ages 6 months to 6y, and Vaxigrip Tetra (30% [25-35]) for ages 65 + y. The results presented are from the second influenza season covered by the DRIVE network. While sample size from the pooled TND studies was still too low for precise (brand-specific) IVE estimates, the network has approximately doubled in size compared to the pilot season. Taking measures to increase sample size is an important focus of DRIVE for the coming years.

The Use of Test-negative Controls to Monitor Vaccine Effectiveness: A Systematic Review of Methodology

BACKGROUND

The test-negative design is an increasingly popular approach for estimating vaccine effectiveness (VE) due to its efficiency. This review aims to examine published test-negative design studies of VE and to explore similarities and differences in methodological choices for different diseases and vaccines.

METHODS

We conducted a systematic search on PubMed, Web of Science, and Medline, for studies reporting the effectiveness of any vaccines using a test-negative design. We screened titles and abstracts and reviewed full texts to identify relevant articles. We created a standardized form for each included article to extract information on the pathogen of interest, vaccine(s) being evaluated, study setting, clinical case definition, choices of cases and controls, and statistical approaches used to estimate VE.

RESULTS

We identified a total of 348 articles, including studies on VE against influenza virus (n = 253), rotavirus (n = 48), pneumococcus (n = 24), and nine other pathogens. Clinical case definitions used to enroll patients were similar by pathogens of interest but the sets of symptoms that defined them varied substantially. Controls could be those testing negative for the pathogen of interest, those testing positive for nonvaccine type of the pathogen of interest, or a subset of those testing positive for alternative pathogens. Most studies controlled for age, calendar time, and comorbidities.

CONCLUSIONS

Our review highlights similarities and differences in the application of the test-negative design that deserve further examination. If vaccination reduces disease severity in breakthrough infections, particular care must be taken in interpreting vaccine effectiveness estimates from test-negative design studies.

Right sizing for vaccine effectiveness studies: how many is enough for reliable estimation?

Background The precision of vaccine effectiveness (VE) estimates is dependent on sample size and sampling methods. In Victoria, participating general practitioners (GPs) are not limited by the number of influenza-like illness (ILI) patients they collect respiratory samples (swabs) from in sentinel surveillance. However, in the context of scarce resources it is of interest to determine the minimum sample size needed for reliable estimates. Methods Following the test-negative design, patients with ILI were recruited by GPs and tested for influenza. Descriptive analyses were conducted to assess possible selection bias introduced by GPs. VE was calculated by logistic regression as [1 – odds ratio] x 100% and adjusted for week of presentation and age. Random 20% and 50% samples were selected without replacement to estimate the effect of swab rates on VE estimates. Results GPs swabbed a smaller proportion of patients aged ≥65 years (45.9%, n=238) than those <5 (75.6%, n=288), 5–17 (67.9%, n=547) and 18–64 (75.6%, n=2662) years. Decreasing the swab rate did not alter VE point estimates significantly. However, it reduced the precision of estimates and in some instances resulted in too small a sample size to estimate VE. Conclusion Imposing a 20% or 50% swabbing rate produces less robust VE estimates. The number of swabs required per year to produce precise estimates should be dictated by seasonal severity, rather than an arbitrary rate. It would be beneficial for GPs to swab patients systematically by age group to ensure there are sufficient data to investigate VE against a particular subtype in a given age group.

Effectiveness of Live Attenuated vs Inactivated Influenza Vaccines in Children During the 2012-2013 Through 2015-2016 Influenza Seasons in Alberta, Canada: A Canadian Immunization Research Network (CIRN) Study

IMPORTANCE

Recent observational studies report conflicting results regarding the effectiveness of live attenuated influenza vaccine (LAIV), particularly against influenza A(H1N1)pdm09.

OBJECTIVE

To compare the effectiveness of LAIV and inactivated influenza vaccine (IIV) against laboratory-confirmed influenza.

DESIGN, SETTING, AND PARTICIPANTS

A test-negative study to estimate influenza vaccine effectiveness (VE) using population-based, linked, individual-level laboratory, health administrative, and immunization data. Data were obtained from 10 169 children and adolescents aged 2 to 17 years (children) who were tested for influenza in inpatient or outpatient settings during periods when influenza was circulating based on a threshold level of 5% weekly test positivity for the province during the 4 influenza seasons spanning from November 11, 2012, to April 30, 2016, in Alberta, Canada. Logistic regression was used to estimate VE by vaccine type, influenza season, and influenza type and subtype. The relative effectiveness of each vaccine type was assessed by comparing the odds of laboratory-confirmed influenza infection for LAIV recipients with that for IIV recipients.

EXPOSURES

The primary exposure was receipt of LAIV or IIV before testing for influenza.

MAIN OUTCOMES AND MEASURES

The primary outcome was influenza case status as determined by reverse-transcriptase polymerase chain reaction testing.

RESULTS

A total of 10 779 respiratory specimens (from 10 169 children) collected and tested for influenza during the 4 influenza seasons were included, with 53.4% from males; the mean (SD) age was 7.0 (4.6) years. Across the 4 influenza seasons, 3161 children tested positive for influenza. Combining the 4 influenza seasons, the adjusted VE against influenza A(H1N1)pdm09 was 69% (95% CI, 56%-78%) for LAIV compared with 79% (95% CI, 70%-86%) for IIV. Vaccine effectiveness against influenza A(H3N2) was 36% (95% CI, 14%-53%) for LAIV and 43% (95% CI, 22%-59%) for IIV. Against influenza B, VE was 74% (95% CI, 62%-82%) for LAIV and 56% (95% CI, 41%-66%) for IIV. There were no significant differences in the odds of influenza infection for LAIV recipients compared with IIV recipients except for influenza B during the 2015-2016 season, when LAIV recipients had lower odds of infection than IIV recipients (odds ratio, 0.36; 95% CI, 0.17-0.76).

CONCLUSIONS AND RELEVANCE

There was no evidence to support the lack of effectiveness of LAIV against influenza A(H1N1)pdm09. These results support administration of either vaccine type in this age group.

Vaccine effectiveness against laboratory-confirmed influenza hospitalizations among young children during the 2010-11 to 2013-14 influenza seasons in Ontario, Canada

Uncertainty remains regarding the magnitude of effectiveness of influenza vaccines for preventing serious outcomes, especially among young children. We estimated vaccine effectiveness (VE) against laboratory-confirmed influenza hospitalizations among children aged 6-59 months. We used the test-negative design in hospitalized children in Ontario, Canada during the 2010-11 to 2013-14 influenza seasons. We used logistic regression models adjusted for age, season, and time within season to calculate VE estimates by vaccination status (full vs. partial), age group, and influenza season. We also assessed VE incorporating prior history of influenza vaccination. We included specimens from 9,982 patient hospitalization episodes over four seasons, with 12.8% testing positive for influenza. We observed variation in VE by vaccination status, age group, and influenza season. For the four seasons combined, VE was 60% (95%CI, 44%-72%) for full vaccination and 39% (95%CI, 17%-56%) for partial vaccination. VE for full vaccination was 67% (95%CI, 48%-79%) for children aged 24-59 months, 48% (95%CI, 12%-69%) for children aged 6-23 months, 77% (95%CI, 47%-90%) for 2010-11, 59% (95%CI, 13%-81%) for 2011-12, 33% (95%CI, -18% to 62%) for 2012-13, and 72% (95%CI, 42%-86%) for 2013-14. VE in children aged 24-59 months appeared similar between those vaccinated in both the current and previous seasons and those vaccinated in the current season only, with the exception of 2012-13, when VE was lower for those vaccinated in the current season only. Influenza vaccination is effective in preventing pediatric laboratory-confirmed influenza hospitalizations during most seasons.

Development and identification of a new Vero cell-based live attenuated influenza B vaccine by a modified classical reassortment method

BACKGROUND

It was to generate a new Vero and cold-adapted live attenuated influenza B vaccine with enough safety and immunogenicity.

METHODS

According to modified classical reassortment method, the donor strain was B/Yunnan/2/2005Vca(B), and the parental virus strain was B/Brisbane/60/2008wt. After co-infection in Vero cells, the prepared antibody serum inhibited the donor strain growth, and screening conditions inhibited the parental virus growth, which induced the growth of the new reassortant virus B/Brisbane/60/2008Vca(B) grow. Through intraperitoneal injection (i.j.) and intranasal injection (n.j.) we evaluated the safety and immunogenicity of the vaccine.

RESULTS

A high-yield of the reassortant virus was produced in Vero cells at 25°C, similar to the donor strains. After sequencing, it was found that B/Brisbane/60/2008Vca(B) Hemagglutinin (HA) and Neuraminidase (NA) gene fragments were from B/Brisbane/60/2008wt, while the other 6 gene fragments were from B/Yunnan/2/2005Vca(B). The n.j. immune pathway experiments showed no significant differences between the treatment and the PBS control group with respect to weight changes (P > 0.5). Furthermore, the new strain had a sufficient geometric mean titter (GMT) against B/Brisbane/60/2008wt.

CONCLUSION

The new reassortant live attenuated influenza B vaccine was safe and having enough immune stimulating ability.

Case-control vaccine effectiveness studies: Data collection, analysis and reporting results

The case-control methodology is frequently used to evaluate vaccine effectiveness post-licensure. The results of such studies provide important insight into the level of protection afforded by vaccines in a 'real world' context, and are commonly used to guide vaccine policy decisions. However, the potential for bias and confounding are important limitations to this method, and the results of a poorly conducted or incorrectly interpreted case-control study can mislead policies. In 2012, a group of experts met to review recent experience with case-control studies evaluating vaccine effectiveness; we summarize the recommendations of that group regarding best practices for data collection, analysis, and presentation of the results of case-control vaccine effectiveness studies. Vaccination status is the primary exposure of interest, but can be challenging to assess accurately and with minimal bias. Investigators should understand factors associated with vaccination as well as the availability of documented vaccination status in the study context; case-control studies may not be a valid method for evaluating vaccine effectiveness in settings where many children lack a documented immunization history. To avoid bias, it is essential to use the same methods and effort gathering vaccination data from cases and controls. Variables that may confound the association between illness and vaccination are also important to capture as completely as possible, and where relevant, adjust for in the analysis according to the analytic plan. In presenting results from case-control vaccine effectiveness studies, investigators should describe enrollment among eligible cases and controls as well as the proportion with no documented vaccine history. Emphasis should be placed on confidence intervals, rather than point estimates, of vaccine effectiveness. Case-control studies are a useful approach for evaluating vaccine effectiveness; however careful attention must be paid to the collection, analysis and presentation of the data in order to best inform evidence-based vaccine policies.