Hesitant or not? A global survey of potential acceptance of a COVID-19 vaccine
Open accessAbstract
A number of COVID-19 vaccines are under development, with one or more possibly becoming available in 2021. We conducted a global survey in June 2020 of 13,426 people in 19 countries to determine potential acceptance rates of a COVID-19 vaccine and factors influencing acceptance. We ran univariate logistic regressions to examine the associations with demographic variables. 71.5% reported they would be very or somewhat likely to take a COVID-19 vaccine; 61.4% reported they would accept their employer’s recommendation to take a COVID-19 vaccine. Differences in acceptance across countries ranged from almost 9 in 10 (China) to fewer than 6 in 10 (Russia). Respondents reporting higher levels of trust in information from government sources were more likely to accept a vaccine, and take their employer’s advice to do so. Targeted interventions addressing age, sex, income, and education level are required to increase and sustain public acceptance of a COVID-19 vaccine.
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The relatively high rates of COVID-19 vaccine acceptance in East and South East Asia were attributed to strong trust in governments.
“The relatively high rates of vaccine acceptance in the region were attributed to strong trust in governments [46].”
From COVID-19 Vaccine Hesitancy Worldwide: A Concise Systematic Review of Vaccine Acceptance Rates (2021) · cites this paper as [46]
Only two surveys among the general public in African countries reported COVID-19 vaccine acceptance rates of 81.6% in South Africa and 65.2% in Nigeria.
“Only two surveys among the general public in African countries reported an acceptance rate of 81.6% in South Africa and 65.2% in Nigeria [46].”
From COVID-19 Vaccine Hesitancy Worldwide: A Concise Systematic Review of Vaccine Acceptance Rates (2021) · cites this paper as [46]
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Vaccine trials during a pandemic: potential approaches to ethical dilemmas
AlQahtani, M., Mallah, S. I., Stevenson, N. J., & Doherty, S. (2021) · Trials · doi:10.1186/s13063-021-05597-8 · Cited by 6 · see citing works
Guzmán, C. A. F., Montero‐Zamora, P., Bolaños-Palmieri, C., Araya-Amador, J., Benavides-Rawson, J., & Avila-Aguero, M. L. (2021) · Cureus · doi:10.7759/cureus.18798 · Cited by 6 · see citing works
COVID-19 vaccine perceptions: An observational study on RedditPreprint
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Vaccination willingness for COVID-19 among health care workers in SwitzerlandPreprint
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Awareness of COVID 19 vaccine in a Rural Area near Bangalore, Karnataka
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- Authors
- Lazarus, J. V., Ratzan, S., Palayew, A., Gostin, L. O., Larson, H. J., Rabin, K., Kimball, S., & El-Mohandes, A.
- Affiliations
- Barcelona Institute for Global Health (ES)
- City University of New York (US)
- Georgetown University (US)
- London School of Hygiene & Tropical Medicine (GB)
- Emerson College (US)
- Corresponding author
- Lazarus, Jeffrey V
- ORCID
- Venue
- medRxiv
- Publisher
- openRxiv
- Published
- 2020-08-25
- OpenAlex
- W3080809458
- Cited by
- 128
- References
- 15
- Citations by year
- 2026: 1 · 2025: 3 · 2024: 2 · 2023: 12 · 2022: 18 · 2021: 87 · 2020: 5
- Type
- posted content
- Language
- en
- Open access
- green
- doi.org
- Locations
- medRxiv (acceptedVersion)
- Funders
- City University of New York
- Ministerio de Ciencia, Innovación y Universidades (CEX 2018-000806-S)
- Centres de Recerca de Catalunya (CEX2018-000806-S)
- Generalitat de Catalunya (CEX2018-000806-S)
- Università Bocconi
- Topics
- Social Sciences › Health › Vaccine Coverage and Hesitancy
- Health Sciences › Medicine › Infectious Diseases › SARS-CoV-2 and COVID-19 Research
- Social Sciences › Sociology and Political Science › Risk Perception and Management
- Keywords
- COVID-19 vaccines, vaccine acceptance, hesitancy, demographic factors, cross-national comparison
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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