Evidence map›Paper›PMID 41204200›Full record

ArticleBMC microbiology2025

Prevalence, antimicrobial resistance, and public health risk assessment of zoonotic bacterial pathogens in raw cattle meat from three zonal cities in Tigray, Ethiopia.

Merhawit Reda, Teka Mebrahtu, Muuz Gebru, Mebrahtu Weldegebriel, Mebrahtu Berhe, Haftay Abraha, Kedir Seid, Gidey Ambaye, Abrha Bsrat

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Article in BMC microbiology, 2025. 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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1 · What the graph read from it

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2 · The registry

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4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Merhawit RedaCollege of Veterinary Sciences, Mekelle University, Mekelle, Ethiopia. merhawit.reda@mu.edu.et.
Teka MebrahtuBureau of Agriculture and Natural Resource Tigray Region, Mekelle, Ethiopia.
Muuz GebruCollege of Veterinary Sciences, Mekelle University, Mekelle, Ethiopia.
Mebrahtu WeldegebrielCollege of Veterinary Sciences, Mekelle University, Mekelle, Ethiopia.
Mebrahtu BerheEthiopian Agricultural Authority, Mekelle, Ethiopia.
Haftay AbrahaCollege of Veterinary Sciences, Mekelle University, Mekelle, Ethiopia.
Kedir SeidCollege of Veterinary Sciences, Mekelle University, Mekelle, Ethiopia.
Gidey AmbayeCollege of Veterinary Sciences, Mekelle University, Mekelle, Ethiopia.
Abrha BsratCollege of Veterinary Sciences, Mekelle University, Mekelle, Ethiopia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Ethiopia harbors a large cattle population that significantly contributes to the national economy through meat and dairy production. However, bacterial contamination of raw meat and antimicrobial resistance present serious public health challenges. This study aimed to isolate zoonotic bacterial pathogens from raw cattle meat and assess their antibiotic resistance profiles in the urban centers of Mekelle, Maichew, and Humera in Tigray, Ethiopia. A cross-sectional survey was conducted from October 2019 to June 2020, collecting one hundred thirty-two (132) raw meat samples from three abattoirs. Bacterial isolation and identification were performed using conventional microbiological methods, and antimicrobial susceptibility testing followed Clinical and Laboratory Standards Institute guidelines. Additionally, a questionnaire survey assessed knowledge, attitudes, and practices related to rational drug use among livestock owners and veterinary professionals. Result showed bacterial contamination in 95.45% of samples, with frequent presence of Escherichia coli (38.64%), Salmonella spp. (63.64%), Staphylococcus aureus (65.15%), and Campylobacter spp. (43.94%). Resistance was notably high to some antibiotics such as Vancomycin (100%) and Erythromycin (78.26%), which are not commonly used in veterinary practice in the region, while resistance to commonly used antibiotics like Amoxicillin was moderate (47.83%). The survey revealed relatively good knowledge of drug use among professionals (79.17%) but poor practices among livestock owners (56.16%). These findings highlight significant bacterial contamination likely due to poor hygiene in abattoirs and handling, along with concerns about antimicrobial resistance potentially linked to unregulated veterinary antibiotic use. This study underscores the need for improved meat hygiene measures, strengthened veterinary drug regulation, and targeted awareness campaigns to promote rational antimicrobial use and enhance food safety, thereby mitigating public health risks.

Indexed as

BacteriaBacterial ZoonosesDrug Resistance, BacterialMeatAbattoirsAnimalsAnti-Bacterial AgentsCattleCitiesCross-Sectional StudiesEthiopiaFood MicrobiologyHumansMicrobial Sensitivity TestsPrevalencePublic HealthAnti-Bacterial AgentsAbattoirAntimicrobial susceptibility testing bacterial isolatesFood borne diseasesFood safety

Identifiers

PMID41204200
PMCPMC12595695

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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.