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Drivers and Predictors of COVID-19 Vaccine Hesitancy in Virginia
Annual conference of the Computational Social Science Society of the Americas, CSSSA 2021 ; : 98-111, 2022.
Article in English | Scopus | ID: covidwho-1826200
ABSTRACT
This research uses the COVID-19 Trends and Impact Survey provided by Carnegie Mellon University in partnership with Facebook to study predictors and drivers of COVID-19 vaccine hesitancy in Virginia’s adult population. It estimates vaccine hesitancy rates at a zip code level in Virginia by applying multilevel statistical models. Our analysis identifies the demographic features of zip codes that are associated with vaccine hesitancy. It also examines the drivers of COVID-19 vaccine hesitancy across Virginia. Results show the presence of a larger percentage of Black and White population and a lower percentage of Hispanic population are predictors of higher vaccine hesitancy within a zip code in Virginia. Among these drivers, the biggest is system distrust, where individuals either do not trust the government or believe that the vaccine is not efficacious. Finally, it provides policy insights and tailored outreach programs for improving COVID-19 vaccination acceptability in different regions in Virginia. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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Full text: Available Collection: Databases of international organizations Database: Scopus Type of study: Prognostic study Topics: Vaccines Language: English Journal: Annual conference of the Computational Social Science Society of the Americas, CSSSA 2021 Year: 2022 Document Type: Article

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Full text: Available Collection: Databases of international organizations Database: Scopus Type of study: Prognostic study Topics: Vaccines Language: English Journal: Annual conference of the Computational Social Science Society of the Americas, CSSSA 2021 Year: 2022 Document Type: Article