Evidence map›Paper›PMID 39261675›Full record

ArticleCommunications medicine2024

Evaluating the direct effect of vaccination and non-pharmaceutical interventions during the COVID-19 pandemic in Europe.

Maxime Fajgenblat, Geert Molenberghs, Johan Verbeeck, Lander Willem, Jonas Crèvecoeur, Christel Faes, Niel Hens, Patrick Deboosere, Geert Verbeke, Thomas Neyens

Abstract read
In one paragraph

Article in Communications medicine, 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
  2. Article
  3. Article
  4. Article
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.

Maxime FajgenblatInteruniversity Institute for Biostatistics and Statistical Bioinformatics (I-BioStat), Data Science Institute (DSI), UHasselt, Hasselt, Belgium. maxime.fajgenblat@gmail.com.ORCID http://orcid.org/0000-0002-2233-1527
Geert MolenberghsInteruniversity Institute for Biostatistics and Statistical Bioinformatics (I-BioStat), Data Science Institute (DSI), UHasselt, Hasselt, Belgium.
Johan VerbeeckInteruniversity Institute for Biostatistics and Statistical Bioinformatics (I-BioStat), Data Science Institute (DSI), UHasselt, Hasselt, Belgium.ORCID http://orcid.org/0000-0002-4923-1032
Lander WillemCentre for Health Economics Research and Modelling of Infectious Diseases (CHERMID), Vaccine & Infectious Disease Institute (VAXINFECTIO), University of Antwerp, Antwerp, Belgium.ORCID http://orcid.org/0000-0002-9210-1196
Jonas CrèvecoeurInteruniversity Institute for Biostatistics and Statistical Bioinformatics (I-BioStat), Data Science Institute (DSI), UHasselt, Hasselt, Belgium.ORCID http://orcid.org/0000-0002-2983-8857
Christel FaesInteruniversity Institute for Biostatistics and Statistical Bioinformatics (I-BioStat), Data Science Institute (DSI), UHasselt, Hasselt, Belgium.
Niel HensInteruniversity Institute for Biostatistics and Statistical Bioinformatics (I-BioStat), Data Science Institute (DSI), UHasselt, Hasselt, Belgium.ORCID http://orcid.org/0000-0003-1881-0637
Patrick DeboosereInterface Demography (ID), Department of Sociology, Vrije Universiteit Brussel, Brussels, Belgium.
Geert VerbekeInteruniversity Institute for Biostatistics and Statistical Bioinformatics (I-BioStat), Data Science Institute (DSI), UHasselt, Hasselt, Belgium.
Thomas NeyensInteruniversity Institute for Biostatistics and Statistical Bioinformatics (I-BioStat), Data Science Institute (DSI), UHasselt, Hasselt, Belgium.

Funding

EC | Horizon 2020 Framework Programme (EU Framework Programme for Research and Innovation H2020) 101003688Fonds Wetenschappelijk Onderzoek (Research Foundation Flanders) 11E3222NFonds Wetenschappelijk Onderzoek (Research Foundation Flanders) G0A4121N
6 · The paper itself

Abstract

backgroundAcross Europe, countries have responded to the COVID-19 pandemic with a combination of non-pharmaceutical interventions and vaccination. Evaluating the effectiveness of such interventions is of particular relevance to policy-makers.

methodsWe leverage almost three years of available data across 38 European countries to evaluate the effectiveness of governmental responses in controlling the pandemic. We developed a Bayesian hierarchical model that flexibly relates daily COVID-19 incidence to past levels of vaccination and non-pharmaceutical interventions as summarised in the Stringency Index. Specifically, we use a distributed lag approach to temporally weight past intervention values, a tensor-product smooth to capture non-linearities and interactions between both types of interventions, and a hierarchical approach to parsimoniously address heterogeneity across countries.

resultsWe identify a pronounced negative association between daily incidence and the strength of non-pharmaceutical interventions, along with substantial heterogeneity in effectiveness among European countries. Similarly, we observe a strong but more consistent negative association with vaccination levels. Our results show that non-linear interactions shape the effectiveness of interventions, with non-pharmaceutical interventions becoming less effective under high vaccination levels. Finally, our results indicate that the effects of interventions on daily incidence are most pronounced at a lag of 14 days after being in place.

conclusionsOur Bayesian hierarchical modelling approach reveals clear negative and lagged effects of non-pharmaceutical interventions and vaccination on confirmed COVID-19 cases across European countries.

Identifiers

PMID39261675
PMCPMC11391057

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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.