Evidence map›Paper›PMID 40794763›Full record

ArticleThe Veterinary record2025

Survey to determine the farm-level impact of Schmallenberg virus during the 2023-2024 UK lambing season.

Sol Elliott, Rachel Clifton, Fiona Lovatt, Rachael Tarlinton

Abstract read
In one paragraph

Article in The Veterinary record, 2025. 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

4 authors.

Sol ElliottSchool of Veterinary Medicine and Science, University of Nottingham, Loughborough, UK.
Rachel CliftonSchool of Veterinary Medicine and Science, University of Nottingham, Loughborough, UK.ORCID https://orcid.org/0000-0002-4474-1340
Fiona LovattSchool of Veterinary Medicine and Science, University of Nottingham, Loughborough, UK.ORCID https://orcid.org/0000-0003-2298-9174
Rachael TarlintonSchool of Veterinary Medicine and Science, University of Nottingham, Loughborough, UK.ORCID https://orcid.org/0000-0003-3325-2311

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThis study aimed to assess the impacts of Schmallenberg virus (SBV) on the 2023/2024 UK lambing season.

methodsAn online survey was distributed to UK sheep farmers between February and May 2024. Responses were compared across SBV-confirmed, suspected and not suspected farms.

resultsHigher impacts on flock welfare, financial performance and farmer emotional wellbeing were reported on SBV-confirmed (n = 44) and SBV-suspected (n = 84) farms compared to not suspected farms (n = 101), and higher dystocia and dystocia-associated ewe deaths were reported on SBV-confirmed farms. Lamb mortality was higher than in previous SBV outbreaks in the UK. An earlier mating season was identified as a risk factor for SBV. LIMITATIONS: Cross-sectional surveys are prone to recall bias, and the data accuracy depends on the accuracy of farm records. Farmers who experienced negative impacts of SBV may have been more likely to complete the survey. There is also a risk of selection bias due to the study population being an opportunistic rather than a random sample of UK sheep farmers.

conclusionThis study confirms the significant impacts of SBV on the UK sheep industry, which are likely to be ongoing due to the cyclical re-emergence pattern of the virus.

Indexed as

Bunyaviridae InfectionsSheep DiseasesAnimalsCross-Sectional StudiesDisease OutbreaksFarmersFarmsFemaleMaleOrthobunyavirusPregnancySeasonsSheepSurveys and QuestionnairesUnited Kingdombluetongue viruseweslambsSchmallenberg virussurveys

Identifiers

PMID40794763
PMCPMC12355902

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.