In Journal of medical Internet research ; h5-index 88.0
BACKGROUND : While the COVID-19 pandemic has left an unprecedented impact globally, countries such as the United States of America have reported the most significant incidence of COVID-19 cases worldwide. Within the US, various sociodemographic factors have played an essential role in the creation of regional disparities. Regional disparities have resulted in the unequal spread of disease between US counties, underscoring the need for efficient and accurate predictive modelling strategies to inform public health officials and reduce the burden on healthcare systems. Furthermore, despite the widespread accessibility of COVID-19 vaccines across the US, vaccination rates have become stagnant, necessitating predictive modelling to identify important factors impacting vaccination uptake.
OBJECTIVE : To determine the association between sociodemographic factors and vaccine uptake across counties in the US.
METHODS : Sociodemographic data on fully vaccinated and unvaccinated individuals were sourced from several online databases, such as the US Centre for Disease Control and US Census Bureau COVID-19 Site. Machine learning analysis was performed using XGBoost and sociodemographic data.
RESULTS : Our model predicted COVID-19 vaccination uptake across US countries with 62% accuracy. In addition, it identified location, education, ethnicity, income, and household access to the internet as the most critical sociodemographic features in predicting vaccination uptake in US counties. Lastly, the model produced a choropleth demonstrating areas of low and high vaccination rates, which can be used by healthcare authorities in future pandemics to visualize and prioritize areas of low vaccination and design targeted vaccination campaigns.
CONCLUSIONS : Our study reveals that sociodemographic characteristics are predictors of vaccine uptake rate across counties in the US and if leveraged appropriately can assist policy makers and public health officials to understand vaccine uptake rates and craft policies to improve them.
Cheong Queena, Au-Yeung Martin, Quon Stephanie, Concepcion Katsy, Kong Jude Dzevela