Evidence map›Paper›PMID 35349385›Full record

ArticleHuman vaccines & immunotherapeutics2022

Estimating the size of "anti-vax" and vaccine hesitant populations in the US, UK, and Canada: comparative latent class modeling of vaccine attitudes.

Timothy B Gravelle, Joseph B Phillips, Jason Reifler, Thomas J Scotto

Abstract read
In one paragraph

Article in Human vaccines & immunotherapeutics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

15 citing papers in PubMed, 1 synthesis or guideline pooled it.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Timothy B GravelleMomentive, Aurora, Canada.ORCID 0000-0002-7091-206X
Joseph B PhillipsSchool of Psychology, University of Kent, canterbury, UK.ORCID 0000-0003-0084-4601
Jason ReiflerDepartment of Politics, University of Exeter, Exeter, UK.ORCID 0000-0002-1116-7346
Thomas J ScottoSchool of Political and Social Sciences, University of Glasgow, Glasgow, UK.ORCID 0000-0003-4801-6821

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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).

Indexed as

COVID-19VaccinesAttitudeCanadaCOVID-19 VaccinesHumansSARS-CoV-2United KingdomVaccinationCOVID-19 VaccinesVaccinesanti-vaxCOVIDlatent class modelingvaccine hesitancyvaccines

Identifiers

PMID35349385
PMCPMC9009899

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.