Vaccine hesitancy for COVID19: what is the role of statistical literacy?

Introduction: Vaccination is an important measure used to control the spread of COVID19. The estimation of risk versus benefit of vaccination is based on the understanding of information about the vaccine. Statistics are frequently part of communications about COVID19. Individuals that do not have an adequate foundation of statistical knowledge may not be able to properly assess associated risks and benefits. This study aims to assess the association between statistical literacy and hesitation to receive the COVID19 vaccine.
Methods: A nationally representative sample of 2,138 adults, recruited through CINT United States, Inc., (Lawrenceville, NJ; http://www.cint.com), completed an internet survey in the summer of 2021. This survey collected demographic measures and information about COVID19 vaccination status. The competency of respondents on various basic statistical concepts was assessed along with the corresponding confidence of respondents in their answers. A multivariable logistic regression model was constructed to assess the relationship between vaccine hesitancy and statistical literacy while controlling for covariates of interest.
Results: Statistical literacy was found to have a negligible association with COVID19 vaccine hesitancy (OR 1.01; 95% CI 1.00-1.02). In addition, differences in the proportion receiving the COVID19 vaccine between political affiliations, income levels, race groups, and ethnicities were observed.
Discussion: The statistical knowledge of the general American public is not commensurate with the need to be literate in basic statistical concepts in the data-driven world in which we live. An effective way to stem vaccine hesitancy may rely on increased statistical knowledge to not be biased by preconceived beliefs shaped by misinformation.
Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
(Copyright © 2023 Kahlenberg, Williams, van Tilburg and Jiroutek.)