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  • 标题:Influence of a COVID-19 vaccine’s effectiveness and safety profile on vaccination acceptance
  • 本地全文:下载
  • 作者:Robert M. Kaplan ; Arnold Milstein
  • 期刊名称:Proceedings of the National Academy of Sciences
  • 印刷版ISSN:0027-8424
  • 电子版ISSN:1091-6490
  • 出版年度:2021
  • 卷号:118
  • 期号:10
  • 页码:1
  • DOI:10.1073/pnas.2021726118
  • 出版社:The National Academy of Sciences of the United States of America
  • 摘要:Although a safe and effective vaccine holds the greatest promise for resolving the COVID-19 pandemic, hesitancy to accept vaccines remains common. To explore vaccine acceptance decisions, we conducted a national survey of 1,000 people from all US states in August of 2020 and a replication in December of 2020. Using a 3 × 3 × 3 factorial experimental design, we estimated the impact of three factors: probability of 1) protection against COVID-19, 2) minor side effects, and 3) a serious adverse reactions. The outcome was respondents’ reported likelihood of receiving a vaccine for the coronavirus. Probability of vaccine efficacy (50%, 70%, or 90%) had the largest effect among the three factors. The probability of minor side effects (50%, 75%, 90%) including fever and sore arm, did not significantly influence likelihood of receiving the vaccine. The chances of a serious adverse reaction, such as temporary or permanent paralysis, had a small but significant effect. A serious adverse reaction rate of 1/100,000 was more likely to discourage vaccine use in comparison to rates of 1/million or 1/100 million. All interactions between the factors were nonsignificant. A replication following the announcement that vaccines were 95% effective showed small, but significant increases in the likelihood of taking a vaccine. The main effects and interactions in the model remained unchanged. Expected benefit was more influential in respondents’ decision making than expected side effects. The absence of interaction effects suggests that respondents consider the side effects and benefits independently.
  • 关键词:vaccine acceptance ; COVID-19 ; conjoint analysis ; decision analysis
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