Evidence map›Paper›PMID 41775812›Full record

ArticleScientific reports2026

Antimicrobial use and Escherichia coli resistance patterns in Hungarian pig farms: a data-driven farm-level analysis.

Krisztián Vribék, Máté Farkas, Szilveszter Csorba, Miklós Süth, László Gombos, Ádám Kerek, Zoltán Somogyi, Ákos Jerzsele, Zsuzsa Farkas

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2026. 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

9 authors.

Krisztián VribékDepartment of Digital Food Science, Institute of Food Chain Science, University of Veterinary Medicine, Budapest, H-1078, Hungary.
Máté FarkasDepartment of Digital Food Science, Institute of Food Chain Science, University of Veterinary Medicine, Budapest, H-1078, Hungary. farkas.mate@univet.hu.
Szilveszter CsorbaDepartment of Digital Food Science, Institute of Food Chain Science, University of Veterinary Medicine, Budapest, H-1078, Hungary.
Miklós SüthNational Laboratory of Infectious Animal Diseases, Antimicrobial Resistance, Veterinary Public Health and Food Chain Safety, University of Veterinary Medicine Budapest, Budapest, H-1078, Hungary.
László GombosWittmann Antal Multidisciplinary Doctoral School of Plant, Animal, and Food Sciences, Széchenyi István University, 2 Vár Square, Mosonmagyaróvár, H-9200, Hungary.
Ádám KerekNational Laboratory of Infectious Animal Diseases, Antimicrobial Resistance, Veterinary Public Health and Food Chain Safety, University of Veterinary Medicine Budapest, Budapest, H-1078, Hungary.
Zoltán SomogyiNational Laboratory of Infectious Animal Diseases, Antimicrobial Resistance, Veterinary Public Health and Food Chain Safety, University of Veterinary Medicine Budapest, Budapest, H-1078, Hungary.
Ákos JerzseleNational Laboratory of Infectious Animal Diseases, Antimicrobial Resistance, Veterinary Public Health and Food Chain Safety, University of Veterinary Medicine Budapest, Budapest, H-1078, Hungary.
Zsuzsa FarkasDepartment of Digital Food Science, Institute of Food Chain Science, University of Veterinary Medicine, Budapest, H-1078, Hungary.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Antimicrobial resistance (AMR) poses a critical challenge to both human and veterinary medicine, with pig production recognized as one of the major contributor due to intensive antimicrobial usage (AMU). This study aimed to explore the relationship between AMU and AMR patterns of Escherichia coli isolated from commercial pig farms, using data-driven analytical methods. Farm-level records were harmonized with microbiological data from 203 isolates collected in December 2023 across four Hungarian farms. AMU was summarized over 3-, 6-, 9-, and 12-month retrospective windows and expressed in modified population-corrected units, while AMR was quantified as mean minimum inhibitory concentration (MIC) and AMR rate under epidemiological and clinical breakpoints. The results revealed substantial variation in AMU among farms, with amoxicillin predominating across timeframes. Farm-specific comparisons indicated that higher AMU may not always coincide with elevated resistance levels, and data analysis did not consistently identify a direct association between use and resistance at the individual farm level, which warrants further investigation in larger datasets. Correlation analyses identified strong intra-class relationships among β-lactams and fluoroquinolones, as well as a cross-class linking, suggesting concurrent selection pressures. Overall, the integration of AMU and AMR data demonstrated the feasibility of farm-level surveillance for AMR modelling and provides a foundation for future predictive systems to support antimicrobial stewardship in livestock production.

Indexed as

Anti-Bacterial AgentsDrug Resistance, BacterialEscherichia coliEscherichia coli InfectionsSwine DiseasesAnimal HusbandryAnimalsFarmsFluoroquinolonesHungaryMicrobial Sensitivity TestsSwineAnti-Bacterial AgentsFluoroquinolonesAMRAntimicrobial useEscherichia coliMICReal-world dataSwine production

Identifiers

PMID41775812
PMCPMC13065741

What Socratic holds

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LicenceCC BY
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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 Socratic graph.