Unique Predictors of Intended Uptake of a COVID-19 Vaccine in Adults Living in a Rural College Town in the United States

  1. Robert P. Lennon
  2. Meg L. Small
  3. Rachel A. Smith
  4. Lauren J. Van Scoy
  5. Jessica G. Myrick
  6. Molly A. Martin
  7. Data4 Action Research Group

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Abstract

To explore public confidence in a COVID-19 vaccine.

Design:

Cross-sectional survey.

Setting:

A rural college town in central Pennsylvania.

Subjects:

Adult residents without minor children.

Measures:

The primary outcome was COVID-19 vaccination intention. Secondary measures included vaccination attitudes, norms, efficacy, past behavior, trust in the vaccination process, and sociodemographic variables of education, financial standing, political viewpoint, and religiosity.

Analysis:

Descriptive statistics were used to describe quantitative data. Multivariate ordinal regression was used to model predictors of vaccine intention.

Results:

Of 950 respondents, 55% were “very likely” and 20% “somewhat likely” to take a coronavirus vaccine, even though 70% had taken the flu vaccine since September 2019. The strongest predictors of vaccine acceptance were trust in the system evaluating vaccines and perceptions of local COVID-19 vaccination norms. The strongest predictors of negative vaccine intentions were worries about unknown side-effects and positive attitudes toward natural infection. Sociodemographic factors, political views, and religiosity did not predict vaccine intentions.

Conclusion:

Fewer adults intend to take a coronavirus vaccine than currently take the flu vaccine. Traditional sociodemographic factors may not be effective predictors of COVID-19 vaccine uptake. Although based on a small sample, the study adds to our limited understanding of COVID-19-specific vaccine confidence among some rural Americans and suggests that traditional public health vaccination campaigns based on sociodemographic characteristics may not be effective.

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  1. Version published to 10.1177/08901171211026132
  2. ScreenIT

    SciScore for 10.1101/2020.12.11.20235838: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Statistical analysis was completed using SPSS statistical software version 25.
    SPSS
    suggested: (SPSS, RRID:SCR_002865)

    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: We detected the following sentences addressing limitations in the study:
    Limitations: Limitations to our study include that it is a single point in a cohort study; results at this time may not be generalizable over time. It is also limited by its narrow population sample, which was primarily well educated, Caucasian females. Strengths of our study include a large sample size compared to prior US COVID-19 studies and timeliness. Capturing intent closer to the potential time of COVID-19 vaccine release is more likely to reflect actual uptake than older studies. While further research is needed to confirm the generalizability of our results, given their concordance with the national Pew sample,7 it is highly likely that fewer US adults intend to take a coronavirus vaccine than currently take the flu vaccine. Further, given changes in positive and negative predictors, to overcome coronavirus vaccine hesitancy, practices, campaigns and policies may need to focus on reinforcing vaccine safety and development integrity over more general historic concerns.

    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.

  3. Version published to 10.1101/2020.12.11.20235838v1 on medRxiv