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      Does a major change to a COVID-19 vaccine program alter vaccine intention? A qualitative investigation

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          Abstract

          Background

          On 8 th April 2021, the Australian Technical Advisory Group on Immunisation (ATAGI) made the Pfizer-BioNtech (Comirnaty) vaccine the “preferred” vaccine for adults in Australia aged <50 years due to a risk of thrombosis with thrombocytopenia syndrome (TTS) following AstraZeneca vaccination. We sought to understand whether this impacted COVID-19 vaccine intentions.

          Method

          we undertook qualitative interviews from February – April 2021 before and after the program change with 28 adults in Perth, Western Australia. Using our COVID-19 vaccine intentions model, we assessed changes in participants’ COVID-19 vaccine intention before and after the program change. Participants were classified as 1) ‘acceptors’: no concerns about COVID-19 vaccine safety, efficacy, access and would accept whatever vaccine is offered, 2) ‘cautious acceptors’: some concerns and would prefer a particular vaccine brand but would accept whatever is offered, 3) ‘Wait awhile’: for more data, easier access, for another vaccine brand, or a greater perceived COVID-19 threat or 4) ‘refuser’: no intention to vaccinate due to concerns about safety and/or efficacy.

          Results

          before the change, 7/18 of those aged <50 years were ‘acceptors,’ 10/18 were ‘cautious acceptors’ and 1/18 was ‘wait awhile.’ Overall, 14/18 participants had the same COVID-19 vaccine intention after the change; 4/18 became more concerned. For those aged ≥50 years and before the change, 5/10 were ‘acceptors’ and 5/10 were ‘cautious acceptors.’ After the change, 8/10 still had the same COVID-19 vaccine intention; 2/10 became more cautious. The major concern before the program change was COVID-19 vaccines having different vaccine efficacy; the concern pivoted to safety.

          Conclusion

          the majority of participants were ‘cautious acceptors’ who intended on being vaccinated; many had this intention before and after the program change. The Australian government, health care providers and media need to better address COVID-19 vaccine concerns to assist those with COVID-19 vaccine intentions receive a vaccine.

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          Most cited references13

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          Research electronic data capture (REDCap)--a metadata-driven methodology and workflow process for providing translational research informatics support.

          Research electronic data capture (REDCap) is a novel workflow methodology and software solution designed for rapid development and deployment of electronic data capture tools to support clinical and translational research. We present: (1) a brief description of the REDCap metadata-driven software toolset; (2) detail concerning the capture and use of study-related metadata from scientific research teams; (3) measures of impact for REDCap; (4) details concerning a consortium network of domestic and international institutions collaborating on the project; and (5) strengths and limitations of the REDCap system. REDCap is currently supporting 286 translational research projects in a growing collaborative network including 27 active partner institutions.
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            The REDCap consortium: Building an international community of software platform partners

            The Research Electronic Data Capture (REDCap) data management platform was developed in 2004 to address an institutional need at Vanderbilt University, then shared with a limited number of adopting sites beginning in 2006. Given bi-directional benefit in early sharing experiments, we created a broader consortium sharing and support model for any academic, non-profit, or government partner wishing to adopt the software. Our sharing framework and consortium-based support model have evolved over time along with the size of the consortium (currently more than 3200 REDCap partners across 128 countries). While the "REDCap Consortium" model represents only one example of how to build and disseminate a software platform, lessons learned from our approach may assist other research institutions seeking to build and disseminate innovative technologies.
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              Is Open Access

              Reducing and meta-analysing estimates from distributed lag non-linear models

              Background The two-stage time series design represents a powerful analytical tool in environmental epidemiology. Recently, models for both stages have been extended with the development of distributed lag non-linear models (DLNMs), a methodology for investigating simultaneously non-linear and lagged relationships, and multivariate meta-analysis, a methodology to pool estimates of multi-parameter associations. However, the application of both methods in two-stage analyses is prevented by the high-dimensional definition of DLNMs. Methods In this contribution we propose a method to synthesize DLNMs to simpler summaries, expressed by a reduced set of parameters of one-dimensional functions, which are compatible with current multivariate meta-analytical techniques. The methodology and modelling framework are implemented in R through the packages dlnm and mvmeta. Results As an illustrative application, the method is adopted for the two-stage time series analysis of temperature-mortality associations using data from 10 regions in England and Wales. R code and data are available as supplementary online material. Discussion and Conclusions The methodology proposed here extends the use of DLNMs in two-stage analyses, obtaining meta-analytical estimates of easily interpretable summaries from complex non-linear and delayed associations. The approach relaxes the assumptions and avoids simplifications required by simpler modelling approaches.
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                Author and article information

                Journal
                Vaccine
                Vaccine
                Vaccine
                Published by Elsevier Ltd.
                0264-410X
                1873-2518
                16 December 2021
                16 December 2021
                Affiliations
                [a ]Wesfarmers Centre of Vaccines and Infectious Diseases, Telethon Kids Institute, Perth, Western Australia, Australia
                [b ]VaxPolLab, School of Social Sciences, University of Western Australia, Perth, Western Australia, Australia
                [c ]School of Medicine, The University of Western Australia, Perth, Western Australia, Australia
                [d ]Department of Infectious Diseases, Perth Children’s Hospital, Perth, Western Australia, Australia
                [e ]Department of Microbiology, PathWest Laboratory Medicine WA, Perth, Western Australia, Australia
                Author notes
                [* ]Corresponding author.
                Article
                S0264-410X(21)01629-7
                10.1016/j.vaccine.2021.12.021
                8674511
                34952758
                adb52ae4-810f-4af1-8c19-d6d00bfa1009
                © 2021 Published by Elsevier Ltd.

                Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.

                History
                : 15 June 2021
                : 8 October 2021
                : 10 December 2021
                Categories
                Article

                Infectious disease & Microbiology
                Infectious disease & Microbiology

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