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Am J Med Sci ; 366(5): 321-329, 2023 11.
Article in English | MEDLINE | ID: mdl-37619894

ABSTRACT

BACKGROUND: To prepare for rollout of a COVID-19 vaccine in fall 2020, there was an urgent need to understand barriers to ensuring equitable access and addressing vaccine skepticism and resistance. This study aimed to understand the association between trusted sources of COVID-19 information and likelihood of vaccination during that time, focusing on lessons learned to prepare for future public health crises. METHODS: From December 2020-March 2021, we surveyed a probability-based, cross-sectional sample of 955 patients across seven federally qualified health centers (FQHCs) serving predominantly low-income, Black and White populations in southeastern Louisiana. Vaccination likelihood was measured on a 7-point scale; "very likely to vaccinate" was defined as score=7. Trust in healthcare provider was measured with a single survey item. High trust in personal contacts, government, and media, respectively, were defined as the highest tertiles of summative scores of trust items. Weighted multivariable logistic regression estimated adjusted odds ratios (aOR) and 95% confidence intervals (CI) for being very likely to vaccinate. RESULTS: Participants were 56% Black, 64% women, mean age 44.6 years; 33% were very likely to vaccinate. High trust in healthcare provider (aOR=4.14, 95% CI 2.26-7.57) and government sources (aOR=3.23, 95% CI 1.98-5.28) were associated with being very likely to vaccinate. CONCLUSIONS: During initial COVID-19 vaccination rollout, trust in healthcare providers and government sources of COVID-19 information was associated with likelihood to vaccinate in FQHC patients. To inform public health planning for future crises, we highlight lessons learned for translating community-relevant insights into direct action to reach those most impacted.


Subject(s)
COVID-19 Vaccines , COVID-19 , United States , Humans , Female , Adult , Male , COVID-19 Vaccines/therapeutic use , Cross-Sectional Studies , COVID-19/epidemiology , COVID-19/prevention & control , Probability , Vaccination
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