Sociodemographic predictors of COVID-19 vaccine acceptance: a nationwide US-based survey study. (September 2021)

• Record Type: Journal Article
• Authors: Mondal, Pritish; Sinharoy, Ankita; Su, Lilly
• Is Part Of: Public health. Volume 198(2021)
• Page Start: 252
• Page End: 259
• Publication Date: 2021-09
• Subjects: COVID-19 -- COVID-19 vaccine -- Prediction model -- Sociodemographic predictors -- Machine learning -- Vaccine hesitancy; Public health -- Periodicals; Public health -- Periodicals; Electronic journals; 362.1
• DOI: 10.1016/j.puhe.2021.07.028
• Languages: English
• ISSNs: 0033-3506

Abstract

Abstract: Objectives: Acceptance of COVID-19 vaccination is attributable to sociodemographic factors and their complex interactions. Attitudes towards COVID-19 vaccines in the United States are changing frequently, especially since the launch of the vaccines and as the United States faces a third wave of the pandemic. Our primary objective was to determine the relative influence of sociodemographic predictors on COVID-19 vaccine acceptance. The secondary objectives were to understand the reasons behind vaccine refusal and compare COVID-19 vaccine acceptance with influenza vaccine uptake. Study design: This was a nationwide US-based survey study. Methods: A REDCap survey link was distributed using various online platforms. The primary study outcome was COVID-19 vaccine acceptance (yes/no). Sociodemographic factors, such as age, ethnicity, gender, education, family income, healthcare worker profession, residence regions, local healthcare facility and 'vaccine launch' period (pre vs post), were included as potential predictors. The differences in vaccine acceptance rates among sociodemographic subgroups were estimated by Chi-squared tests, whereas logistic regression and neural networks computed the prediction models and determined the predictors of relative significance. Results: Among 2978 eligible respondents, 81.1% of participants were likely to receive the vaccine. All the predictors demonstrated significant associations with vaccine acceptance, except vaccine launch period. Regression analyses eliminated gender and vaccine launch period from the model, and the machine learning model reproduced the regression result. Both models precisely predicted individual vaccine acceptance and recognised education, ethnicity and age as the most important predictors. Fear of adverse effects and concern with efficacy were the principal reasons for vaccine refusal. Conclusions: Sociodemographic predictors, such as education, ethnicity and age, significantly influenced COVID-19 vaccine acceptance, and concerns of side-effects and efficacy led to increased vaccine hesitancy. Graphical abstract: Image 1