Factors associated with cattle necropsy submissions in Switzerland, and their importance for surveillance.


Journal

Preventive veterinary medicine
ISSN: 1873-1716
Titre abrégé: Prev Vet Med
Pays: Netherlands
ID NLM: 8217463

Informations de publication

Date de publication:
Feb 2021
Historique:
received: 03 04 2020
revised: 24 11 2020
accepted: 09 12 2020
pubmed: 17 1 2021
medline: 20 7 2021
entrez: 16 1 2021
Statut: ppublish

Résumé

Pathology data have been reported to be important for surveillance, as they are crucial for correctly recognizing and identifying new or re-emerging diseases in animal populations. However, there are no reports in the literature of necropsy data being compared or complemented with other data. In our study, we compared cattle necropsy reports extracted from 3 laboratories with the Swiss fallen stock data and clinical data collected by the association of Swiss Cattle Breeders. The objective was to assess the completeness, validity and representativeness of the necropsy data, as well as evaluate potential factors for necropsy submission and how they can benefit animal health surveillance. Our results showed that, on average, 1% of Swiss cattle that die are submitted for post-mortem examinations. However, different factors influence cattle necropsy submissions, such as the age of the animal, the geographical location and the number of sick and/or dead animals on the farm. There was a median of five animals reported sick and two animals reported dead within 30 days prior to a necropsy submission, providing quantitative evidence of a correlation between on farm morbidity/mortality and post-mortem examination. Our results also showed that necropsy data can help improve the accuracy and completeness of health data for surveillance systems. In this study, we were able to demonstrate the importance of veterinary pathology data for AHS by providing quantitative evidence that necropsied animals are indicative of farms with important disease problems and are therefore critically important for surveillance. Furthermore, thanks to the amount of information provided by combined data sources, the epidemiology (e.g. season, geographic region, risk factors) of potential diseases can be analysed more precisely and help supporting animal health surveillance systems.

Identifiants

pubmed: 33453476
pii: S0167-5877(20)30919-3
doi: 10.1016/j.prevetmed.2020.105235
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

105235

Informations de copyright

Copyright © 2020 The Authors. Published by Elsevier B.V. All rights reserved.

Auteurs

Irene Zühlke (I)

Institute of Animal Pathology, Department of Infectious Diseases and Pathobiology, Vetsuisse Faculty, University of Bern, Bern, Switzerland. Electronic address: irene.zuehlke@vetsuisse.unibe.ch.

John Berezowski (J)

Veterinary Public Health Institute, University of Bern, Bern, Switzerland.

Michèle Bodmer (M)

Clinic for Ruminants, Herd Health Division, Department of Clinical Veterinary Medicine, Vetsuisse Faculty, University of Bern, Bern, Switzerland.

Susanne Küker (S)

Veterinary Public Health Institute, University of Bern, Bern, Switzerland.

Anne Göhring (A)

Department of Computational Linguistics, University of Zurich, Zurich, Switzerland.

Fabio Rinaldi (F)

Department of Computational Linguistics, University of Zurich, Zurich, Switzerland.

Céline Faverjon (C)

Veterinary Public Health Institute, University of Bern, Bern, Switzerland; Ausvet, 69001 Lyon, France.

Corinne Gurtner (C)

Institute of Animal Pathology, Department of Infectious Diseases and Pathobiology, Vetsuisse Faculty, University of Bern, Bern, Switzerland.

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