Please note that the copy function is not enabled for this field.
If you wish to
modify
existing outcomes, please copy and paste the current outcome text into the Update field.
LOGIN
CREATE ACCOUNT
MY TRIALS
LOGIN
CREATE ACCOUNT
MY TRIALS
REGISTER TRIAL
FAQs
HINTS AND TIPS
DEFINITIONS
Register a trial
The ANZCTR website will be unavailable from 1pm until 3pm (AEDT) on Wednesday the 30th of October for website maintenance. Please be sure to log out of the system in order to avoid any loss of data.
The safety and scientific validity of this study is the responsibility of the study sponsor and investigators. Listing a study does not mean it has been endorsed by the ANZCTR. Before participating in a study, talk to your health care provider and refer to this
information for consumers
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
Titles & IDs
Public title
Does Repeat Influenza Vaccination Constrain Influenza Immune Responses and Protection
Query!
Scientific title
Does Repeat Influenza Vaccination Constrain Influenza Immune Responses and Protection
Query!
Secondary ID [1]
0
0
1R01AI41534
Query!
Universal Trial Number (UTN)
Query!
Trial acronym
Query!
Linked study record
Query!
Health condition
Health condition(s) or problem(s) studied:
Influenza, Human
0
0
Query!
SARS-CoV-2 Infection
0
0
Query!
Condition category
Condition code
Infection
0
0
0
0
Query!
Other infectious diseases
Query!
Respiratory
0
0
0
0
Query!
Other respiratory disorders / diseases
Query!
Intervention/exposure
Study type
Observational
Query!
Patient registry
Query!
Target follow-up duration
Query!
Target follow-up type
Query!
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.
Query!
Intervention code [1]
0
0
Treatment: Other
Query!
Comparator / control treatment
Query!
Control group
Query!
Outcomes
Primary outcome [1]
0
0
Seropositivity post-vaccination (influenza vaccine)
Query!
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.
Query!
Timepoint [1]
0
0
Post-vaccination blood draws are at 14-21 days post vaccination. Collected each year 2020-2023 post annual influenza vaccination.
Query!
Primary outcome [2]
0
0
Seropositivity post-season (influenza vaccine)
Query!
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.
Query!
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.
Query!
Primary outcome [3]
0
0
Fold-rise in geometric mean antibody titre (GMT) pre- to post-vaccination
Query!
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.
Query!
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.
Query!
Primary outcome [4]
0
0
Fold-change in geometric mean antibody titre (GMT) post-vaccination to post-season
Query!
Assessment method [4]
0
0
The changes in GMT from post-vaccination to post-season.
Query!
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.
Query!
Primary outcome [5]
0
0
Seroconversion fraction post-vaccination
Query!
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.
Query!
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.
Query!
Secondary outcome [1]
0
0
Healthcare workers (HCWs) PCR-positive for influenza at the end of each season
Query!
Assessment method [1]
0
0
Proportion of HCWs that are PCR-positive for influenza at the end of each season.
Query!
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.
Query!
Secondary outcome [2]
0
0
Influenza attack rate at the end of each season
Query!
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.
Query!
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.
Query!
Secondary outcome [3]
0
0
Vaccine efficacy (VE)
Query!
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.
Query!
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.
Query!
Secondary outcome [4]
0
0
Duration of illness (influenza)
Query!
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.
Query!
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.
Query!
Secondary outcome [5]
0
0
Haemagglutinin (HA) antibody landscapes for vaccine-naïve and highly-vaccinated healthcare workers (HCWs)
Query!
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.
Query!
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.
Query!
Secondary outcome [6]
0
0
Haemagglutinin (HA) antibody landscapes for infected versus uninfected healthcare workers (HCWs)
Query!
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.
Query!
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.
Query!
Secondary outcome [7]
0
0
Enumeration of cells
Query!
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).
Query!
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.
Query!
Secondary outcome [8]
0
0
B cells
Query!
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.
Query!
Timepoint [8]
0
0
Blood draws on day 7 post influenza vaccination and post infection. Collected each year 2020-2023.
Query!
Secondary outcome [9]
0
0
Quantify biological mechanisms that shape the antibody response
Query!
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.
Query!
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.
Query!
Secondary outcome [10]
0
0
Estimate protective titres
Query!
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.
Query!
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.
Query!
Secondary outcome [11]
0
0
Optimal influenza vaccination strategy for healthcare workers (HCWs) under different vaccine availability
Query!
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.
Query!
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.
Query!
Secondary outcome [12]
0
0
Estimated SARS-CoV-2 attack rates among symptomatic and asymptomatic healthcare workers (HCWs)
Query!
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.
Query!
Timepoint [12]
0
0
Follow-up period 2020-2023.
Query!
Secondary outcome [13]
0
0
Case-hospitalization risk
Query!
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.
Query!
Timepoint [13]
0
0
Follow-up period 2020-2023.
Query!
Secondary outcome [14]
0
0
Risk factors for asymptomatic, mild and severe SARS-CoV-2 infection
Query!
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.
Query!
Timepoint [14]
0
0
Follow-up period 2020-2023.
Query!
Secondary outcome [15]
0
0
Estimated SARS-CoV-2 antibody titre associated with protection
Query!
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.
Query!
Timepoint [15]
0
0
Follow-up period 2020-2023.
Query!
Secondary outcome [16]
0
0
Estimated SARS-CoV-2 antibody kinetics over time
Query!
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.
Query!
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.
Query!
Secondary outcome [17]
0
0
Identification of key behavioural drivers of transmission
Query!
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.
Query!
Timepoint [17]
0
0
Follow-up period 2020-2023.
Query!
Secondary outcome [18]
0
0
Estimated duration of viral shedding and viral load in SARS-CoV-2 infection over time
Query!
Assessment method [18]
0
0
We will estimate the average duration of viral shedding and viral load over time and correlation with severity.
Query!
Timepoint [18]
0
0
During symptomatic infection to two days post resolution of symptoms. Follow-up period 2020-2023.
Query!
Secondary outcome [19]
0
0
Enumeration of SARS-CoV-2-reactive B and T cells and identification of dominant epitopes
Query!
Assessment method [19]
0
0
Mean antibody concentration will be calculated in innate immune responses.
Query!
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.
Query!
Secondary outcome [20]
0
0
Gene expression
Query!
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.
Query!
Timepoint [20]
0
0
Changes from day 0 to day 7 post vaccination. Follow-up period 2020-2023.
Query!
Secondary outcome [21]
0
0
Enumeration of SARS-CoV-2-reactive B and T cells induced by each vaccine formulation
Query!
Assessment method [21]
0
0
Mean antibody concentration will be calculated and compared for vaccine groups (Comirnaty vs Vaxzevria vaccine).
Query!
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.
Query!
Secondary outcome [22]
0
0
Seroconversion of SARS-CoV-2 serum antibody titres induced by each vaccine formulation
Query!
Assessment method [22]
0
0
Seroconversion post-vaccination will be calculated and compared between vaccine groups by logistic regression (Comirnaty vs Vaxzevria vaccine).
Query!
Timepoint [22]
0
0
At day 14-21 post vaccine schedule completion. Follow-up period 2020-2023.
Query!
Secondary outcome [23]
0
0
Fold changes in innate immune cells and in vaccine specific B and T cells
Query!
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).
Query!
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.
Query!
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.
Query!
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.
Query!
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.
Query!
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.
Query!
Minimum age
18
Years
Query!
Query!
Maximum age
60
Years
Query!
Query!
Sex
Both males and females
Query!
Can healthy volunteers participate?
Yes
Query!
Key exclusion criteria
* Immunosuppressive treatment (including systemic corticosteroids) within the past 6 months;
* Personnel for whom vaccination is contraindicated at the time of recruitment.
Query!
Study design
Purpose
Query!
Duration
Query!
Selection
Query!
Timing
Prospective
Query!
Statistical methods / analysis
Query!
Recruitment
Recruitment status
Recruiting
Query!
Data analysis
Query!
Reason for early stopping/withdrawal
Query!
Other reasons
Query!
Date of first participant enrolment
Anticipated
Query!
Actual
2/04/2020
Query!
Date of last participant enrolment
Anticipated
Query!
Actual
Query!
Date of last data collection
Anticipated
1/11/2023
Query!
Actual
Query!
Sample size
Target
1500
Query!
Accrual to date
Query!
Final
Query!
Recruitment in Australia
Recruitment state(s)
NSW,QLD,SA,VIC,WA
Query!
Recruitment hospital [1]
0
0
John Hunter Hospital - New Lambton Heights
Query!
Recruitment hospital [2]
0
0
The Children's Hospital at Westmead - Westmead
Query!
Recruitment hospital [3]
0
0
Queensland Children's Hospital - Brisbane
Query!
Recruitment hospital [4]
0
0
Women's and Children's Hospital - Adelaide
Query!
Recruitment hospital [5]
0
0
The Alfred - Melbourne
Query!
Recruitment hospital [6]
0
0
Perth Children's Hospital - Nedlands
Query!
Recruitment postcode(s) [1]
0
0
2305 - New Lambton Heights
Query!
Recruitment postcode(s) [2]
0
0
2145 - Westmead
Query!
Recruitment postcode(s) [3]
0
0
4101 - Brisbane
Query!
Recruitment postcode(s) [4]
0
0
5006 - Adelaide
Query!
Recruitment postcode(s) [5]
0
0
3004 - Melbourne
Query!
Recruitment postcode(s) [6]
0
0
6009 - Nedlands
Query!
Funding & Sponsors
Primary sponsor type
Other
Query!
Name
University of Melbourne
Query!
Address
Query!
Country
Query!
Other collaborator category [1]
0
0
Other
Query!
Name [1]
0
0
The University of Queensland
Query!
Address [1]
0
0
Query!
Country [1]
0
0
Query!
Other collaborator category [2]
0
0
Other
Query!
Name [2]
0
0
Sydney Children's Hospitals Network
Query!
Address [2]
0
0
Query!
Country [2]
0
0
Query!
Other collaborator category [3]
0
0
Other
Query!
Name [3]
0
0
The Alfred
Query!
Address [3]
0
0
Query!
Country [3]
0
0
Query!
Other collaborator category [4]
0
0
Other
Query!
Name [4]
0
0
University of Adelaide
Query!
Address [4]
0
0
Query!
Country [4]
0
0
Query!
Other collaborator category [5]
0
0
Other
Query!
Name [5]
0
0
The University of Western Australia
Query!
Address [5]
0
0
Query!
Country [5]
0
0
Query!
Other collaborator category [6]
0
0
Other
Query!
Name [6]
0
0
London School of Hygiene and Tropical Medicine
Query!
Address [6]
0
0
Query!
Country [6]
0
0
Query!
Other collaborator category [7]
0
0
Other
Query!
Name [7]
0
0
University of Newcastle, Australia
Query!
Address [7]
0
0
Query!
Country [7]
0
0
Query!
Ethics approval
Ethics application status
Query!
Summary
Brief summary
The objectives of this study are to understand the long-term consequences of repeated annual influenza vaccination among healthcare workers (HCWs) and to use statistical and mathematical modelling to elucidate the immunological processes that underlie vaccination responses and their implications for vaccination effectiveness. These objectives will be achieved by pursuing three specific aims: 1. To study the immunogenicity and effectiveness of influenza vaccination by prior vaccination experience 2. To characterize immunological profiles associated with vaccination and infection 3. To evaluate the impact of immunity on vaccination effectiveness. Under Aim 1, a cohort of hospital workers will be recruited and followed for up to 4 years to assess their pre- and post-vaccination and post-season antibody responses, and their risk of influenza infection. These outcomes will be compared by vaccination experience, classified as frequently vaccinated (received =3 vaccines in the past 5 years), infrequently vaccinated (\<3 vaccinations in past 5 years), vaccinated once, vaccine naïve and unvaccinated. In Aim 2, intensive cellular and serological assessments will be conducted to dissect the influenza HA-reactive B cell and antibody response, and build antibody landscapes that typify the different vaccination groups. In Aim 3, the data generated in Aims 1 and 2 will be used to develop a mathematical model that considers prior infection, vaccination history, antibody kinetics, and antigenic distance to understand the effects of repeated vaccination on vaccine effectiveness. Completion of the proposed research will provide evidence to inform decisions about continued support for influenza vaccination programs among HCWs and general policies for annual influenza vaccination, as well as much needed clarity about the effects of repeated vaccination. In March-April 2020 pursuant to the SARS-CoV-2 global pandemic an administrative supplement added a SARS-CoV-2 protocol addendum for follow-up of COVID-19 infections amongst our HCW participant cohort. The following objectives were added: 1. To estimate risk factors and correlates of protection for SARS-CoV-2 infection amongst HCW 2. To characterize viral kinetics and within-host viral dynamics of SARS-CoV-2 infecting HCW 3. To characterize immunological profiles following infection by SARS-CoV-2 4. To characterize immunological profiles following vaccination for SARS-CoV-2.
Query!
Trial website
https://clinicaltrials.gov/study/NCT05110911
Query!
Trial related presentations / publications
Query!
Public notes
Query!
Contacts
Principal investigator
Name
0
0
Sheena Sullivan, MPH, PhD
Query!
Address
0
0
University of Melbourne
Query!
Country
0
0
Query!
Phone
0
0
Query!
Fax
0
0
Query!
Email
0
0
Query!
Contact person for public queries
Name
0
0
Sheena Sullivan, MPH, PhD
Query!
Address
0
0
Query!
Country
0
0
Query!
Phone
0
0
+61 3 9342 9317
Query!
Fax
0
0
Query!
Email
0
0
[email protected]
Query!
Contact person for scientific queries
Data sharing statement
Will individual participant data (IPD) for this trial be available (including data dictionaries)?
Yes
Query!
What data in particular will be shared?
Sharing original data: The proposed study will collect demographic and clinical information, as well as blood and respiratory specimens from participants. Because we will be conducting longitudinal follow-up, we will be collecting identifiable information. Any data shared will be stripped of identifiers prior to release for sharing. However, there remains the possibility of deductive disclosure of participants with unusual characteristics. Thus, data will only be shared with new collaborators under a data-sharing agreement that provides for: (1) a commitment to using the data only for research purposes and not to identify any individual participant; (2) a commitment to securing the data using appropriate computer technology; and (3) a commitment to destroying or returning the data after analyses are completed.
Supporting document/s available: Study protocol, Statistical analysis plan (SAP), Informed consent form (ICF), Analytic code
Query!
When will data be available (start and end dates)?
Data will be available after publication of results, likely in late-2024.
Query!
Available to whom?
Data will only be shared with new collaborators under a data-sharing agreement that provides for: (1) a commitment to using the data only for research purposes and not to identify any individual participant; (2) a commitment to securing the data using appropriate computer technology; and (3) a commitment to destroying or returning the data after analyses are completed.
Query!
Available for what types of analyses?
Query!
How or where can data be obtained?
IPD available at link: https://hcwflustudy.com/home
Query!
What supporting documents are/will be available?
No Supporting Document Provided
Type
Other Details
Attachment
Study protocol
Study Protocol and Statistical Analysis Plan: Main...
[
More Details
]
https://cdn.clinicaltrials.gov/large-docs/11/NCT05110911/Prot_SAP_000.pdf
Statistical analysis plan
Study Protocol and Statistical Analysis Plan: Main...
[
More Details
]
https://cdn.clinicaltrials.gov/large-docs/11/NCT05110911/Prot_SAP_000.pdf
Study protocol
Study Protocol and Statistical Analysis Plan: Adde...
[
More Details
]
https://cdn.clinicaltrials.gov/large-docs/11/NCT05110911/Prot_SAP_001.pdf
Statistical analysis plan
Study Protocol and Statistical Analysis Plan: Adde...
[
More Details
]
https://cdn.clinicaltrials.gov/large-docs/11/NCT05110911/Prot_SAP_001.pdf
Informed consent form
Informed Consent Form: Influenza Vaccinated Partic...
[
More Details
]
https://cdn.clinicaltrials.gov/large-docs/11/NCT05110911/ICF_002.pdf
Informed consent form
Informed Consent Form: Unvaccinated Participants
https://cdn.clinicaltrials.gov/large-docs/11/NCT05110911/ICF_003.pdf
Informed consent form
Informed Consent Form: COVID-19 Vaccinated Partici...
[
More Details
]
https://cdn.clinicaltrials.gov/large-docs/11/NCT05110911/ICF_004.pdf
Results publications and other study-related documents
No documents have been uploaded by study researchers.
Results not provided in
https://clinicaltrials.gov/study/NCT05110911