Evidence map›Paper›PMID 39091525›Full record

ArticleFrontiers in public health2024

Investigating seasonal changes in factors associated with COVID-19 concerns: Results from a serial cross-sectional survey study in Germany between 2020 and 2023.

Robin Janzik, Dan Borzekowski, Gaby-Fleur Böl

Abstract read
In one paragraph

Article in Frontiers in public health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–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

1 citing paper in PubMed.

  1. 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

3 authors.

Robin JanzikDepartment Risk Communication, German Federal Institute for Risk Assessment (BfR), Berlin, Germany.
Dan BorzekowskiDepartment Risk Communication, German Federal Institute for Risk Assessment (BfR), Berlin, Germany.
Gaby-Fleur BölDepartment Risk Communication, German Federal Institute for Risk Assessment (BfR), Berlin, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: COVID-19 risk perceptions are discussed to be volatile and have been shown to be connected to the adoption of preventive public health behaviors. This study aimed to investigate changes in COVID-19 concerns and influencing factors as a function of season among the German public. Methods: Sixty-three waves of cross-sectional telephone surveys with German participants aged 14 years and older conducted at least monthly between June 2020 and April 2023 provided the data basis ( Results: Results showed a higher between-seasons than within-seasons variability in concerns, with rises in physical and mental health and social concerns during fall. Multivariate regressions revealed being female, lower education, adopting protective measures, and higher perceived probability of infection in both public and private settings to be consistent predictors of higher COVID-19 concerns. Coefficients of these predictors remained comparatively stable over seasons and years. Conclusion: Results indicate re-occurring changes in concerns during a prolonged crisis, with distinct characteristics being consistently associated with higher reported concerns. To ensure the application of protective measures, communicators should consider that risk perceptions are subject to fluctuations, but that certain groups of individuals tend to develop them and therefore deserve particular focus.

Indexed as

COVID-19SeasonsAdolescentAdultAgedCross-Sectional StudiesFemaleGermanyHumansMaleMental HealthMiddle AgedSARS-CoV-2Surveys and QuestionnairesYoung AdultbehaviorconcernsCOVID-19perceptionsserial cross-sectional datasurvey study

Identifiers

PMID39091525
PMCPMC11291447

What OpenQuestion holds

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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.