Evidence map›Paper›PMID 39370683›Full record

ArticleEpidemiology and infection2024

Spatio-temporal distributions of COVID-19 vaccine doses uptake in the Netherlands: a Bayesian ecological modelling analysis.

Haoyi Wang, Tugce Varol, Thomas Gültzow, Hanne M L Zimmermann, Robert A C Ruiter, Kai J Jonas

Abstract read
In one paragraph

Article in Epidemiology and infection, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

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

  1. 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

6 authors.

Haoyi WangDepartment of Work and Social Psychology, Maastricht University, Maastricht, The Netherlands.ORCID 0000-0002-1844-7156
Tugce VarolDepartment of Work and Social Psychology, Maastricht University, Maastricht, The Netherlands.ORCID 0000-0003-0737-9802
Thomas GültzowDepartment of Work and Social Psychology, Maastricht University, Maastricht, The Netherlands.ORCID 0000-0002-9268-1880
Hanne M L ZimmermannDepartment of Work and Social Psychology, Maastricht University, Maastricht, The Netherlands.ORCID 0000-0002-1647-8485
Robert A C RuiterDepartment of Work and Social Psychology, Maastricht University, Maastricht, The Netherlands.ORCID 0000-0001-5017-3258
Kai J JonasDepartment of Work and Social Psychology, Maastricht University, Maastricht, The Netherlands.ORCID 0000-0001-6607-1993

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In the transitioning era towards the COVID-19 endemic, there is still a sizable population that has never been vaccinated against COVID-19 in the Netherlands. This study employs Bayesian spatio-temporal modelling to assess the relative chances of COVID-19 vaccination uptake - first, second, and booster doses - both at the municipal and regional (public health services) levels. Incorporating ecological regression modelling to consider socio-demographic factors, our study unveils a diverse spatio-temporal distribution of vaccination uptake. Notably, the areas located in or around the Dutch main urban area (Randstad) and regions that are more religiously conservative exhibit a below-average likelihood of vaccination. Analysis at the municipal level within public health service regions indicates internal heterogeneity. Additionally, areas with a higher proportion of non-Western migrants consistently show lower chances of vaccination across vaccination dose scenarios. These findings highlight the need for tailored national and local vaccination strategies. Particularly, more regional efforts are essential to address vaccination disparities, especially in regions with elevated proportions of marginalized populations. This insight informs ongoing COVID-19 campaigns, emphasizing the importance of targeted interventions for optimizing health outcomes during the second booster phase, especially in regions with a relatively higher proportion of marginalized populations.

Indexed as

Bayes TheoremCOVID-19COVID-19 VaccinesSpatio-Temporal AnalysisAdolescentAdultAgedFemaleHumansMaleMiddle AgedNetherlandsSARS-CoV-2VaccinationYoung AdultCOVID-19 VaccinesBayesian spatio-temporal analysisCOVID-19preventionsmall area estimationvaccination uptake

Identifiers

PMID39370683
PMCPMC11474892

What OpenQuestion holds

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LicenceCC BY
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.