Estimating the size of "anti-vax" and vaccine hesitant populations in the US, UK, and Canada: comparative latent class modeling of vaccine attitudes.
Estimating the size of "anti-vax" and vaccine hesitant populations in the US, UK, and Canada: comparative latent class modeling of vaccine attitudes.
复制标题
DOI：
10.1080/21645515.2021.2008214
复制
发表时间：
2022-12-31
影响因子：
4.8
通讯作者：
Scotto, Thomas J.
中科院分区：
文献类型：
--
作者：
Gravelle, Timothy B.;Phillips, Joseph B.;Reifler, Jason;Scotto, Thomas J.
疫苗犹豫是全球努力在产生群体免疫的水平上接种SARS-CoV-2病毒的一个重大障碍。在这篇文章中，我们展示了一种归纳方法-潜在类别分析（LCA）-的实用性，它使我们能够描述不同疫苗态度群体的规模和性质;并比较这些群体在不同国家以及国家内的人口统计学亚组之间的差异。我们使用在美国、英国和加拿大收集的原始调查数据进行分析。我们还表明，这些类别与SARS-CoV-2疫苗接种意图以及对COVID-19疫苗有效性和安全性的看法密切相关，这表明对疫苗对抗新型冠状病毒大流行的态度可以很好地解释为大流行之前的潜在疫苗态度。更具体地说，我们发现四个实质性的疫苗态度：强烈的支持者，支持者的关注，疫苗犹豫不决，和“反VAX”，以及第五个测量误差类。强烈的“反vax”情绪阶层在这三个国家都很小，而强烈的支持者阶层在这三个国家都是最大的。我们观察到在不同的人口群体中，课堂作业的分布不同-最明显的是教育和政治倾向（党派和意识形态）。
Vaccine hesitancy is a significant impediment to global efforts to vaccinate against the SARS-CoV-2 virus at levels that generate herd immunity. In this article, we show the utility of an inductive approach – latent class analysis (LCA) – that allows us to characterize the size and nature of different vaccine attitude groups; and to compare how these groups differ across countries as well as across demographic subgroups within countries. We perform this analysis using original survey data collected in the US, UK, and Canada. We also show that these classes are strongly associated with SARS-CoV-2 vaccination intent and perceptions of the efficacy and safety of the COVID-19 vaccines, suggesting that attitudes about vaccines to fight the novel coronavirus pandemic are well explained by latent vaccine attitudes that precede the pandemic. More specifically, we find four substantive classes of vaccine attitudes: strong supporters, supporters with concerns, vaccine hesitant, and “anti-vax” as well as a fifth measurement error class. The strong “anti-vax” sentiment class is small in all three countries, while the strong supporter class is the largest across all three countries. We observe different distributions of class assignments in different demographic groups – most notably education and political leaning (partisanship and ideology).
登录
查看更多内容
影响因子：
5.4
作者：
Hainmueller, Jens;Mummolo, Jonathan;Xu, Yiqing
通讯作者：
Xu, Yiqing
影响因子：
29.9
作者：
Amin, Avnika B.;Bednarczyk, Robert A.;Omer, Saad B.
通讯作者：
Omer, Saad B.
影响因子：
5.5
作者：
Salmon, Daniel A.;Limaye, Rupali J.;Omer, Saad B.
通讯作者：
Omer, Saad B.
影响因子：
3
作者：
CLOGG, CC;GOODMAN, LA
通讯作者：
GOODMAN, LA
影响因子：
13.6
作者：
Clinton J;Cohen J;Lapinski J;Trussler M
通讯作者：
Trussler M