Combating COVID-19 Vaccine Hesitancy: A Synthetic Public Segmentation Approach for Predicting Vaccine Acceptance.

Objective: Vaccine hesitancy impacts the ability to cope with coronavirus disease 2019 (COVID-19) effectively in the United States. It is important for health organizations to increase vaccine acceptance. Addressing this issue, this study aimed to predict citizens' acceptance of the COVID-19 vaccine through a synthetic approach of public segmentation including cross-situational and situational variables. Controlling for demographics, we examined institutional trust, negative attitudes toward, and low levels of knowledge about vaccines (ie, lacuna public characteristics), and fear of COVID-19 during the pandemic. Our study provides a useful framework for public segmentation and contributes to risk and health campaigns by identifying significant predictors of COVID-19 vaccine acceptance.

Method: We conducted an online survey on October 10, 2020 ( = 499), and performed hierarchical regression analyses to predict citizens' COVID-19 vaccine acceptance.

Results: This study demonstrated that trust in the Centers for Disease Control and Prevention (CDC) and federal government, vaccine attitude, problem recognition, constraint recognition, involvement recognition, and fear positively predicted COVID-19 vaccine acceptance.

Conclusions: This study outlines a useful synthetic public segmentation framework and extends the concept of lacuna public to the pandemic context, helping to predict vaccine acceptance. Importantly, the findings could be useful in designing health campaign messages.