Evidence map›Paper›PMID 34863621›Full record

ArticleVaccine2022

Individual factors influencing COVID-19 vaccine acceptance in between and during pandemic waves (July-December 2020).

Sara Valckx, Jonas Crèvecoeur, Frederik Verelst, Maren Vranckx, Greet Hendrickx, Niel Hens, Pierre Van Damme, Koen Pepermans, Philippe Beutels, Thomas Neyens

Open access · greenAbstract read
In one paragraph

Article in Vaccine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.

0numbers the graph read from it
0cells of the map it votes in
28citing papers in PubMed
5.7field-weighted citation impact, top 4% of its field
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

28 citing papers in PubMed, 55 citations in OpenAlex.

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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

10 authors at 3 institutions in 1 country.

Sara ValckxCentre for the Evaluation of Vaccination, VAXINFECTIO, Faculty of Medicine and Health Sciences, University of Antwerp, 2000 Antwerpen, Belgium. Electronic address: sara.valckx@uantwerpen.be.
Jonas CrèvecoeurLeuven Biostatistics and statistical Bioinformatics Centre (L-BioStat), Faculty of Medicine, KU Leuven, Kapucijnenvoer 35, building D, box 7001, 3000 Leuven, Belgium; Interuniversity Institute for Biostatistics and statistical Bioinformatics (I-BioStat), Data Science Institute, Hasselt University, Martelarenlaan 42, 3500 Hasselt, Belgium. Electronic address: jonas.crevecoeur@kuleuven.be.
Frederik VerelstCentre for Health Economics Research and Modelling Infectious Diseases, VAXINFECTIO, Faculty of Medicine and Health Sciences, University of Antwerp, 2000 Antwerpen, Belgium. Electronic address: frederik.Verelst@uantwerpen.be.
Maren VranckxInteruniversity Institute for Biostatistics and statistical Bioinformatics (I-BioStat), Data Science Institute, Hasselt University, Martelarenlaan 42, 3500 Hasselt, Belgium. Electronic address: maren.vranckx@uhasselt.be.
Greet HendrickxCentre for the Evaluation of Vaccination, VAXINFECTIO, Faculty of Medicine and Health Sciences, University of Antwerp, 2000 Antwerpen, Belgium. Electronic address: greet.hendrickx@uantwerpen.be.
Niel HensCentre for Health Economics Research and Modelling Infectious Diseases, VAXINFECTIO, Faculty of Medicine and Health Sciences, University of Antwerp, 2000 Antwerpen, Belgium; Interuniversity Institute for Biostatistics and statistical Bioinformatics (I-BioStat), Data Science Institute, Hasselt University, Martelarenlaan 42, 3500 Hasselt, Belgium. Electronic address: niel.hens@uantwerpen.be.
Pierre Van DammeCentre for the Evaluation of Vaccination, VAXINFECTIO, Faculty of Medicine and Health Sciences, University of Antwerp, 2000 Antwerpen, Belgium. Electronic address: pierre.vandamme@uantwerpen.be.
Koen PepermansFaculty of Social Sciences, University of Antwerp, Sint-Jacobstraat 2, 2000 Antwerpen, Belgium. Electronic address: koen.pepermans@uantwerpen.be.
Philippe BeutelsCentre for Health Economics Research and Modelling Infectious Diseases, VAXINFECTIO, Faculty of Medicine and Health Sciences, University of Antwerp, 2000 Antwerpen, Belgium. Electronic address: philippe.beutels@uantwerpen.be.
Thomas NeyensLeuven Biostatistics and statistical Bioinformatics Centre (L-BioStat), Faculty of Medicine, KU Leuven, Kapucijnenvoer 35, building D, box 7001, 3000 Leuven, Belgium; Interuniversity Institute for Biostatistics and statistical Bioinformatics (I-BioStat), Data Science Institute, Hasselt University, Martelarenlaan 42, 3500 Hasselt, Belgium. Electronic address: thomas.neyens@uhasselt.be.
University of Antwerp · BEHasselt University · BEKU Leuven · BE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundA year after the start of the COVID-19 outbreak, the global rollout of vaccines gives us hope of ending the pandemic. Lack of vaccine confidence, however, poses a threat to vaccination campaigns. This study aims at identifying individuals' characteristics that explain vaccine willingness in Flanders (Belgium), while also describing trends over time (July-December 2020).

methodsThe analysis included data of 10 survey waves of the Great Corona Survey, a large-scale online survey that was open to the general public and had 17,722-32,219 respondents per wave. Uni- and multivariable general additive models were fitted to associate vaccine willingness with socio-demographic and behavioral variables, while correcting for temporal and geographical variability.

resultsWe found 84.2% of the respondents willing to be vaccinated, i.e., respondents answering that they were definitely (61.2%) or probably (23.0%) willing to get a COVID-19 vaccine, while 9.8% indicated maybe, 3.9% probably not and 2.2% definitely not. In Flanders, vaccine willingness was highest in July 2020 (90.0%), decreased over the summer period to 80.2% and started to increase again from late September, reaching 85.9% at the end of December 2020. Vaccine willingness was significantly associated with respondents' characteristics: previous survey participation, age, gender, province, educational attainment, household size, financial situation, employment sector, underlying medical conditions, mental well-being, government trust, knowing someone with severe COVID-19 symptoms and compliance with restrictive measures. These variables could explain much, but not all, variation in vaccine willingness.

conclusionsBoth the timing and location of data collection influence vaccine willingness results, emphasizing that comparing data from different regions, countries and/or timepoints should be done with caution. To maximize COVID-19 vaccination coverage, vaccination campaigns should focus on (a combination of) subpopulations: aged 31-50, females, low educational attainment, large households, difficult financial situation, low mental well-being and labourers, unemployed and self-employed citizens.

Indexed as

COVID-19VaccinesCOVID-19 VaccinesFemaleHumansPandemicsSARS-CoV-2COVID-19 VaccinesVaccinesCOVID-19Online surveySocio-demographicsTrustVaccine hesitancyVaccine willingness

Identifiers

PMID34863621
PMCPMC8634074
OpenAlexW3215732813

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.