Using conditional inference to quantify interaction effects of socio-demographic covariates of US COVID-19 vaccine hesitancy

COVID-19 vaccine hesitancy has become a major issue in the U.S. as vaccine supply has outstripped demand and vaccination rates slow down. At least one recent global survey has sought to study the covariates of vaccine acceptance, but an inferential model that makes simultaneous use of several socio-demographic variables has been lacking. In this article, we present such a model using US-based survey data collected by Gallup. Our study agrees with the global survey results in some respects, but is also found to exhibit significant differences. For example, women and people aged between 25-54 were found to be more vaccine hesitant. Our conditional inference tree model suggests that trust in government, age and ethnicity are the most important covariates for predicting vaccine hesitancy, and can interact in ways that make them useful for communication-based outreach, especially if conjoined with census data. In particular, we found that the most vaccine hesitant individuals were those who identified as Black Republicans with a high school (or lower) education and lower income levels, who were involuntarily unemployed and trusted in the Trump administration.