Evidence map›Paper›PMID 39347952›Full record

ArticleBundesgesundheitsblatt, Gesundheitsforschung, Gesundheitsschutz2024

[Infection epidemiological effects of school closures during the second COVID-19 pandemic wave-an exemplary analysis within a lower Bavarian region].

Christine Deckart, Heribert Stich

Abstract readEnglish Abstract
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In one paragraph

Article in Bundesgesundheitsblatt, Gesundheitsforschung, Gesundheitsschutz, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

2 authors.

Christine DeckartInstitut für Medizinische Informationsverarbeitung, Biometrie und Epidemiologie - IBE, Ludwig-Maximilians-Universität München, München (Bayern), Deutschland.
Heribert StichInstitut für Medizinische Informationsverarbeitung, Biometrie und Epidemiologie - IBE, Ludwig-Maximilians-Universität München, München (Bayern), Deutschland. heribert.stich@landkreis-landshut.de.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe rapid global spread of the SARS-CoV‑2 virus during the COVID-19 pandemic led to widespread non-pharmaceutical interventions, such as school closures, to curb infections. This study investigates the influence of school closures on the number of new infections to reduce the existing knowledge gap in this area.

methodsThe effect of school closures starting on 16 December 2020, was quantified using COVID-19 infection data from the district and city of Landshut in Bavaria between November 2020 and January 2021. An interrupted time series (ITS) analysis was performed to analyze daily new infections among 6-18 year olds and the entire population in the region before and after the school closures using a negative binomial regression model.

resultsThe analysis showed that the trend of daily new COVID-19 infections among 6-18 year olds significantly decreased after the school closures (incidence rate ratio (IRR) 0.93; 95% confidence interval (CI): 0.89-0.96; p < 0.001). This was also true for the entire study population (IRR 0.95; 95% CI: 0.93-0.97; p < 0.001). DISCUSSION: In the context of various concurrent measures to contain the COVID-19 pandemic, the study provides evidence for a significant association between school closures and reduced COVID-19 infections. By optimizing the existing incomplete evidence base on the role of school closures in pandemic control, this finding can support involved stakeholders and contribute to strengthening future approaches to pandemic prevention.

Indexed as

COVID-19PandemicsSchoolsAdolescentChildFemaleGermanyHumansIncidenceInterrupted Time Series AnalysisMaleSARS-CoV-2IncidenceInterrupted times series analysisPublic health measuresSARS-CoV‑2School

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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.