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Trial details imported from ClinicalTrials.gov

For full trial details, please see the original record at https://clinicaltrials.gov/study/NCT05110911




Registration number
NCT05110911
Ethics application status
Date submitted
27/10/2021
Date registered
8/11/2021
Date last updated
1/04/2025

Titles & IDs
Public title
Does Repeat Influenza Vaccination Constrain Influenza Immune Responses and Protection
Scientific title
Does Repeat Influenza Vaccination Constrain Influenza Immune Responses and Protection
Secondary ID [1] 0 0
1R01AI41534
Universal Trial Number (UTN)
Trial acronym
Linked study record

Health condition
Health condition(s) or problem(s) studied:
Influenza, Human 0 0
SARS-CoV-2 Infection 0 0
Condition category
Condition code
Infection 0 0 0 0
Other infectious diseases
Respiratory 0 0 0 0
Other respiratory disorders / diseases

Intervention/exposure
Study type
Observational
Patient registry
Target follow-up duration
Target follow-up type
Description of intervention(s) / exposure
Treatment: Other - Influenza vaccination: Fluarix Tetra, Vaxigrip Tetra, Fluquadri, Fluad Quad, Afluia Quad, Flucelvax Quad
Treatment: Other - SARS-CoV-2 vaccination: Comirnaty or Vaxzevria

Healthcare Workers - Eligible participants will be recruited from 1 of 6 participating hospitals in Australia and will meet the following criteria: personnel (including staff, honorary staff, students and volunteers) located at a participating hospital or healthcare service at the time of recruitment who would be eligible for the hospital's free vaccination programme; be aged =18 years old and =60 years old; have a mobile phone that can receive and send SMS messages; willing and able to provide blood samples; available for follow-up over the next 7 months; able and willing to complete the informed consent process.

There are no restrictions on the type of healthcare worker (HCW) that can be recruited into the study in terms of their job role. HCW will be any hospital staff, including clinical, research, administrative and support staff.


Treatment: Other: Influenza vaccination: Fluarix Tetra, Vaxigrip Tetra, Fluquadri, Fluad Quad, Afluia Quad, Flucelvax Quad
Influenza vaccine made available to healthcare workers at the participating healthcare sites, as part of their free vaccination campaigns for healthcare workers.

Treatment: Other: SARS-CoV-2 vaccination: Comirnaty or Vaxzevria
SARS-CoV-2 vaccine made available to healthcare workers at the participating healthcare sites, as part of their free vaccination campaigns for healthcare workers.

Intervention code [1] 0 0
Treatment: Other
Comparator / control treatment
Control group

Outcomes
Primary outcome [1] 0 0
Seropositivity post-vaccination (influenza vaccine)
Assessment method [1] 0 0
Seropositivity among vaccination groups will be calculated and compared using logistic regression, with seropositivity coded as 1 if the titre =40, and 0 if the titre is \<40. We will test for trend among vaccination groups, assuming seropositivity will be lowest in the most highly vaccinated.
Timepoint [1] 0 0
Post-vaccination blood draws are at 14-21 days post vaccination. Collected each year 2020-2023 post annual influenza vaccination.
Primary outcome [2] 0 0
Seropositivity post-season (influenza vaccine)
Assessment method [2] 0 0
Seropositivity among vaccination groups will be calculated and compared using logistic regression, with seropositivity coded as 1 if the titre =40, and 0 if the titre is \<40. We will test for trend among vaccination groups, assuming seropositivity will be lowest in the most highly vaccinated.
Timepoint [2] 0 0
End of the season blood draws are in October or November each year, at the conclusion of Australia's annual influenza season. Vaccination usually occurs in April or May. Collected each year 2020-2023 post annual influenza season.
Primary outcome [3] 0 0
Fold-rise in geometric mean antibody titre (GMT) pre- to post-vaccination
Assessment method [3] 0 0
The changes in GMT from pre- to post-vaccination. Seroconversion is defined as samples with 4-fold increases in hemagglutination inhibition (HI) titre.
Timepoint [3] 0 0
Changes from day 0 to day 14-21 post influenza vaccination. Collected each year 2020-2023 pre and post annual influenza vaccination.
Primary outcome [4] 0 0
Fold-change in geometric mean antibody titre (GMT) post-vaccination to post-season
Assessment method [4] 0 0
The changes in GMT from post-vaccination to post-season.
Timepoint [4] 0 0
Changes from day 14-21 to post-season. Influenza season in Australia is approximately May to November. Pre-vaccination to post-season is approximately April or May to October or November each year. Collected each year 2020-2023.
Primary outcome [5] 0 0
Seroconversion fraction post-vaccination
Assessment method [5] 0 0
The proportion of samples with 4-fold increases in hemagglutination inhibition (HI) titre. Seroconversion post-vaccination will be calculated and compared among vaccination groups by logistic regression, with seroconversion coded as 1 if the fold-rise in titre is =4 and 0 if the fold-rise in titre is \<4. We will test for trend, assuming seroconversion will be lowest in the most highly vaccinated.
Timepoint [5] 0 0
Changes from day 0 to day 14-21 post influenza vaccination. Collected each year 2020-2023 pre and post annual influenza vaccination.
Secondary outcome [1] 0 0
Healthcare workers (HCWs) PCR-positive for influenza at the end of each season
Assessment method [1] 0 0
Proportion of HCWs that are PCR-positive for influenza at the end of each season.
Timepoint [1] 0 0
Influenza season in Australia is approximately May to November. Follow up for PCR-positives from approximately April/May to October/November each year from 2020-2023.
Secondary outcome [2] 0 0
Influenza attack rate at the end of each season
Assessment method [2] 0 0
Evidence of influenza infection will be based on RT-PCR-confirmed infection, only, as serological evidence may be biased in vaccinees who elicit a good antibody response to vaccination. Attack rates will be calculated for each vaccination group as the number of cases during the person-time at risk.
Timepoint [2] 0 0
Person-time at risk, during influenza season. Influenza season in Australia is approximately May to November. Follow up for PCR-positives from approximately April/May to October/November each year from 2020-2023.
Secondary outcome [3] 0 0
Vaccine efficacy (VE)
Assessment method [3] 0 0
VE will be estimated using a Cox proportional hazards regression model comparing the risk of influenza infection (coded as 1 for infected or 0 for uninfected) among healthcare workers (HCWs) by vaccination status: VE = (1-HRadj) × 100%. If there are sufficient cases, the model will be adjusted for potential confounders (e.g. age group), and factors that may modify the risk of infection. Using virus characterization data, we will assess if failures are associated with antigenic mismatch.
Timepoint [3] 0 0
Person-time at risk, during influenza season. Influenza season in Australia is approximately May to November. Follow up for PCR-positives from approximately April/May to October/November each year from 2020-2023.
Secondary outcome [4] 0 0
Duration of illness (influenza)
Assessment method [4] 0 0
The number of days ill with influenza (count) will be compared among vaccination groups, adjusted for age. Because of the excess of 0 counts (people who never get infected), zero-inflated negative binomial regression will be used.
Timepoint [4] 0 0
Days ill, during influenza season. Influenza season in Australia is approximately May to November. Follow up for PCR-positives from approximately April/May to October/November each year from 2020-2023.
Secondary outcome [5] 0 0
Haemagglutinin (HA) antibody landscapes for vaccine-naïve and highly-vaccinated healthcare workers (HCWs)
Assessment method [5] 0 0
By collating the results of many antibody assays to historical influenza strains, it is possible to visualize the landscape of an individual's responses to vaccination and infection. We are using strains going back to 1968 when A(H3N2) emerged in humans.
Timepoint [5] 0 0
Bloods on day 0, day 7, day 14-21 post influenza vaccination and end of season. Collected each year 2020-2023 pre and post annual influenza vaccination and end of influenza season.
Secondary outcome [6] 0 0
Haemagglutinin (HA) antibody landscapes for infected versus uninfected healthcare workers (HCWs)
Assessment method [6] 0 0
By collating the results of many antibody assays to historical influenza strains, it is possible to visualize the landscape of an individual's responses to vaccination and infection. We are using strains going back to 1968 when A(H3N2) emerged in humans.
Timepoint [6] 0 0
Bloods on day 7 and day 14-21 post influenza infection. Collected each year 2020-2023 along with pre and post annual influenza vaccination and end of influenza season bloods.
Secondary outcome [7] 0 0
Enumeration of cells
Assessment method [7] 0 0
Enumeration of influenza haemagglutinin (HA)-reactive B cells, and of subsets with phenotypic markers indicative of activation, and of memory versus naïve status, for vaccine-naïve, highly vaccinated and infected healthcare workers (HCWs) (i.e. we are comparing frequency fold-change/ratio between groups highly vaccinated and infrequently vaccinated).
Timepoint [7] 0 0
Bloods on day 0 and day 14-21 post influenza vaccination and post infection. The key indicator is the frequency of these B cells on day 14 post-vaccination relative to pre-vaccination frequencies. Collected each year 2020-2023.
Secondary outcome [8] 0 0
B cells
Assessment method [8] 0 0
B cell receptor gene usage by influenza haemagglutinin (HA)-reactive B cells recovered post vaccination and post infection from selected vaccine naïve, highly vaccinated and infected healthcare workers (HCWs) with distinct antibody response profiles. In depth characterization of HA antigenic sites recognized by serum antibodies from selected HCW including vaccine non-responders who lack seroprotection, and vaccine serological responders who fail to be protected. This analysis will largely be performed on B cells detected on day 7 post vaccination, when there is the greatest potential to differentiate between vaccine reactive B cells that have come from naïve versus memory pools.
Timepoint [8] 0 0
Blood draws on day 7 post influenza vaccination and post infection. Collected each year 2020-2023.
Secondary outcome [9] 0 0
Quantify biological mechanisms that shape the antibody response
Assessment method [9] 0 0
Models of antibody dynamics and individual-level exposures will be develop to quantify the different aspects of the antibody response that generated observed immunological profiles.
Timepoint [9] 0 0
Bloods on day 0, day 7, day 14-21 post influenza vaccination, day 7, day 14-21 post infection and end of season. Collected each year 2020-2023.
Secondary outcome [10] 0 0
Estimate protective titres
Assessment method [10] 0 0
As the model is refined we will identify a minimum set of titres against past or forward strains that capture the underlying 'smooth' antibody landscape and provide a reliable correlate of protection.
Timepoint [10] 0 0
Bloods on day 0, day 7, day 14-21 post influenza vaccination, day 7, day 14-21 post infection and end of season. Collected each year 2020-2023.
Secondary outcome [11] 0 0
Optimal influenza vaccination strategy for healthcare workers (HCWs) under different vaccine availability
Assessment method [11] 0 0
With our model in place, we will compare the performance of current vaccination programs with simulated alternatives to predict the impact of repeated vaccination and circulating virus on vaccine efficacy (VE) under different scenarios. In particular, we will examine the potential impact of: highly-valent vaccines, which include more than a single strain for each subtype; universal vaccines that generate a broadly cross-reactive response against conserved influenza epitopes; and near-universal vaccines that produce a broader response, but still have potential to generate effects such as antibody focusing or seniority, which could reduce effectiveness.
Timepoint [11] 0 0
Bloods on day 0, day 7, day 14-21 post influenza vaccination, day 7, day 14-21 post infection and end of season. Collected each year 2020-2023.
Secondary outcome [12] 0 0
Estimated SARS-CoV-2 attack rates among symptomatic and asymptomatic healthcare workers (HCWs)
Assessment method [12] 0 0
Symptomatic attack (incidence) rates will be calculated as the number of cases testing positive by RT-PCR during the person-time at risk. The asymptomatic incidence proportion will be calculated as the number of HCWs with evidence of sero-conversion and no acute respiratory infection reported among all HCWs followed during the same period.
Timepoint [12] 0 0
Follow-up period 2020-2023.
Secondary outcome [13] 0 0
Case-hospitalization risk
Assessment method [13] 0 0
The hospitalization risk (or incidence proportion) will be calculated as the number of healthcare workers (HCWs) hospitalized due to COVID-19 among all HCW with either asymptomatic or symptomatic evidence of infection during the same period.
Timepoint [13] 0 0
Follow-up period 2020-2023.
Secondary outcome [14] 0 0
Risk factors for asymptomatic, mild and severe SARS-CoV-2 infection
Assessment method [14] 0 0
The predictors of severe infection will be estimated using a Cox proportional hazards regression model comparing the risk of COVID-19 illness (coded as 1 for hospitalised or 0 for infected but not hospitalised) among HCWs. If there are sufficient cases, various predictors of severity will be explored in either univariate or multivariate analysis. Predictors may include age, presence of comorbidities, and viral load.
Timepoint [14] 0 0
Follow-up period 2020-2023.
Secondary outcome [15] 0 0
Estimated SARS-CoV-2 antibody titre associated with protection
Assessment method [15] 0 0
We will compare post-season geometric mean titres between those with asymptomatic and symptomatic infections. We will attempt to establish serological correlates of protection for SARS-CoV-2, using a Bayesian implementation of logistic regression that we have used for influenza cohort studies.
Timepoint [15] 0 0
Follow-up period 2020-2023.
Secondary outcome [16] 0 0
Estimated SARS-CoV-2 antibody kinetics over time
Assessment method [16] 0 0
Sera collected more frequently will be assessed for antibody titre and the titres compared over time. Geometric mean titres will be calculated and plotted to allow visual inspection of the antibody kinetics, overall and within groups (e.g. age groups, severity of infection). The mean rate of decay will be calculated using linear regression. Because little is known about the decay kinetics, various models will be explored to identify the model with best fit, based on visual inspection of the data and model fitting diagnostics. Viral load will be included in analyses comparing asymptomatic, mild and severe infections. If possible we will explore the interactions of viral load with demographic (e.g. age) or medical (e.g. heart disease) characteristics.
Timepoint [16] 0 0
Bloods on day 3, day 7, day 14-21, day 30 post infection and end of season. Daily swabs during symptomatic infection to two days post resolution of symptoms. Follow-up period 2020-2023.
Secondary outcome [17] 0 0
Identification of key behavioural drivers of transmission
Assessment method [17] 0 0
Using social contacts data, we will attempt to infer the transmission dynamics for our healthcare worker (HCW) participants between each round of sample collection. We will use mathematical models social mixing data with infection risk to untangle specific behaviours/contact scaling that may be driving transmission. These models may be extended to include genetic sequencing data, which has been previously used to reconstruct transmission clusters.
Timepoint [17] 0 0
Follow-up period 2020-2023.
Secondary outcome [18] 0 0
Estimated duration of viral shedding and viral load in SARS-CoV-2 infection over time
Assessment method [18] 0 0
We will estimate the average duration of viral shedding and viral load over time and correlation with severity.
Timepoint [18] 0 0
During symptomatic infection to two days post resolution of symptoms. Follow-up period 2020-2023.
Secondary outcome [19] 0 0
Enumeration of SARS-CoV-2-reactive B and T cells and identification of dominant epitopes
Assessment method [19] 0 0
Mean antibody concentration will be calculated in innate immune responses.
Timepoint [19] 0 0
Bloods on day 3, day 7, day 14-21, day 30 post infection and end of season. Follow-up period 2020-2023.
Secondary outcome [20] 0 0
Gene expression
Assessment method [20] 0 0
Identification of genes that are differentially expressed on day 7 compared to day 0 for each vaccine formulation, focusing on innate immune associated genes.
Timepoint [20] 0 0
Changes from day 0 to day 7 post vaccination. Follow-up period 2020-2023.
Secondary outcome [21] 0 0
Enumeration of SARS-CoV-2-reactive B and T cells induced by each vaccine formulation
Assessment method [21] 0 0
Mean antibody concentration will be calculated and compared for vaccine groups (Comirnaty vs Vaxzevria vaccine).
Timepoint [21] 0 0
Specific B and T cells detected at day 14-21 post vaccine schedule completion versus day 0. Follow-up period 2020-2023.
Secondary outcome [22] 0 0
Seroconversion of SARS-CoV-2 serum antibody titres induced by each vaccine formulation
Assessment method [22] 0 0
Seroconversion post-vaccination will be calculated and compared between vaccine groups by logistic regression (Comirnaty vs Vaxzevria vaccine).
Timepoint [22] 0 0
At day 14-21 post vaccine schedule completion. Follow-up period 2020-2023.
Secondary outcome [23] 0 0
Fold changes in innate immune cells and in vaccine specific B and T cells
Assessment method [23] 0 0
Antibody levels will be correlated with fold changes in innate immune cells and in vaccine specific B and T cells in each vaccine formulation (Comirnaty vs Vaxzevria vaccine).
Timepoint [23] 0 0
Vaccine specific B and T cells detected at day 14-21 post vaccine schedule completion versus day 0. Follow-up period 2020-2023.
Secondary outcome [24] 0 0
Comparison of antibody (and B and T cell) responses induced against COVID-19 and influenza vaccines among participants who received COVID-19 versus influenza vaccine first or who were co-administered both vaccines.
Assessment method [24] 0 0
Mean antibody concentration will be calculated and compared for vaccine groups (CoVax vs influenza vaccine). Seroconversion post-vaccination will be calculated and compared between vaccine groups by logistic regression.
Timepoint [24] 0 0
Antibody levels will be correlated with fold changes in innate immune cells and in vaccine specific B and T cells detected at day 14-21 post vaccine schedule completion versus day 0. Follow-up period 2020-2023.

Eligibility
Key inclusion criteria
Eligible participants will be recruited from 1 of 6 participating hospitals and will meet the following criteria:

* Personnel (including staff, honorary staff, students and volunteers) located at a participating hospital or healthcare service at the time of recruitment who would be eligible for the hospital's free vaccination programme
* Be aged =18 years old and =60 years old;
* Have a mobile phone that can receive and send SMS messages;
* Willing and able to provide blood samples;
* Available for follow-up over the next 7 months;
* Able and willing to complete the informed consent process.

There are no restrictions on the type of healthcare worker (HCW) that can be recruited into the study in terms of their job role. HCWs can be any hospital staff, including clinical, research, administrative and support staff.
Minimum age
18 Years
Maximum age
60 Years
Sex
Both males and females
Can healthy volunteers participate?
Yes
Key exclusion criteria
* Immunosuppressive treatment (including systemic corticosteroids) within the past 6 months;
* Personnel for whom vaccination is contraindicated at the time of recruitment.

Study design
Purpose
Duration
Selection
Timing
Prospective
Statistical methods / analysis

Recruitment
Recruitment status
Active, not recruiting
Data analysis
Reason for early stopping/withdrawal
Other reasons
Date of first participant enrolment
Anticipated
Actual
Date of last participant enrolment
Anticipated
Actual
Date of last data collection
Anticipated
Actual
Sample size
Target
Accrual to date
Final
Recruitment in Australia
Recruitment state(s)
NSW,QLD,SA,VIC,WA
Recruitment hospital [1] 0 0
John Hunter Hospital - New Lambton Heights
Recruitment hospital [2] 0 0
The Children's Hospital at Westmead - Westmead
Recruitment hospital [3] 0 0
Queensland Children's Hospital - Brisbane
Recruitment hospital [4] 0 0
Women's and Children's Hospital - Adelaide
Recruitment hospital [5] 0 0
The Alfred - Melbourne
Recruitment hospital [6] 0 0
Perth Children's Hospital - Nedlands
Recruitment postcode(s) [1] 0 0
2305 - New Lambton Heights
Recruitment postcode(s) [2] 0 0
2145 - Westmead
Recruitment postcode(s) [3] 0 0
4101 - Brisbane
Recruitment postcode(s) [4] 0 0
5006 - Adelaide
Recruitment postcode(s) [5] 0 0
3004 - Melbourne
Recruitment postcode(s) [6] 0 0
6009 - Nedlands

Funding & Sponsors
Primary sponsor type
Other
Name
University of Melbourne
Country
Other collaborator category [1] 0 0
Other
Name [1] 0 0
The University of Queensland
Country [1] 0 0
Other collaborator category [2] 0 0
Other
Name [2] 0 0
Sydney Children's Hospitals Network
Country [2] 0 0
Other collaborator category [3] 0 0
Other
Name [3] 0 0
The Alfred
Country [3] 0 0
Other collaborator category [4] 0 0
Other
Name [4] 0 0
University of Adelaide
Country [4] 0 0
Other collaborator category [5] 0 0
Other
Name [5] 0 0
The University of Western Australia
Country [5] 0 0
Other collaborator category [6] 0 0
Other
Name [6] 0 0
London School of Hygiene and Tropical Medicine
Country [6] 0 0
Other collaborator category [7] 0 0
Other
Name [7] 0 0
University of Newcastle, Australia
Country [7] 0 0

Ethics approval
Ethics application status

Summary
Brief summary
Trial website
Public notes

Contacts
Principal investigator
Name 0 0
Sheena Sullivan, MPH, PhD
Address 0 0
University of Melbourne
Country 0 0
Phone 0 0
Email 0 0
Contact person for public queries
Name 0 0
Sheena Sullivan, MPH, PhD
Address 0 0
Country 0 0
Phone 0 0
+61 3 9342 9317
Email 0 0
sheena.sullivan@influenzacentre.org
Contact person for scientific queries

Data sharing statement