Inadequate intention to receive Covid-19 vaccination: indicators for public health messaging needed to improve uptake in UK

  1. Patrik Bachtiger
  2. Alexander Adamson
  3. William A Maclean
  4. Jennifer K Quint
  5. Nicholas S Peters

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Abstract

Data promising effective Covid-19 vaccines have accelerated the UK’s mass vaccination programme. The UK public’s attitudes to the government’s prioritisation list are unknown, and achieving critical population immunity will require the remaining majority to accept both vaccination and the delay in access of up to a year or more. This cross-sectional observational study sent an online questionnaire to registrants of the UK National Health Service’s largest personal health record. Question items covered willingness for Covid-19 vaccine uptake and attitudes to prioritisation. Among 9,122 responses, 71.5% indicated wanting a vaccine, below what previous modelling indicated as critical levels for progressing towards herd immunity. 22.7% disagreed with the prioritisation list, though 70.3% were against being able to expedite vaccination through payment. Age and female gender were, respectively, strongly positively and negatively associated with wanting a vaccine. Teachers and Black, Asian and Minority Ethnic (BAME) groups were most cited by respondents for prioritisation. This study identifies factors to inform the public health messaging critical to improving uptake.

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  1. ScreenIT

    SciScore for 10.1101/2020.12.07.20243881: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Institutional Review Board StatementIRB: This study was approved by the Institutional Review Board of Imperial College Healthcare National Health Service (NHS) Trust (ICHNT).
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Free-text analysis and quantification of next-priority groups was performed using natural language processing packages (SpaCy and RegEx) in Python (version 3.7).
    Python
    suggested: (IPython, RRID:SCR_001658)

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  2. Version published to 10.1101/2020.12.07.20243881v1 on medRxiv