Statistical design for health monitoring in laboratory animal facilities using sentinel animals.

Animal facilities quality assurance/control sample size statistics

Journal

Laboratory animals
ISSN: 1758-1117
Titre abrégé: Lab Anim
Pays: England
ID NLM: 0112725

Informations de publication

Date de publication:
05 Aug 2024
Historique:
medline: 5 8 2024
pubmed: 5 8 2024
entrez: 5 8 2024
Statut: aheadofprint

Résumé

Regular health monitoring is crucial in laboratory animal facilities to determine the presence or absence of specific pathogens. One common approach to monitoring involves the use of sentinel animals, which are periodically exposed to biological material from the cages being monitored. At a certain point, some of these sentinel animals are tested for pathogens. This article discusses designing an effective sampling scheme to meet desired quality standards. It addresses questions such as the number of sentinel animals required, the frequency of sampling biological material, the selection of cages based on facility set-up, and the optimal frequency and quantity of sentinel animal tests. While existing design formulas are available for simple random sampling, no quantitative recommendation exists for using sentinel animals to the best of our knowledge. We propose a Monte Carlo simulation-based approach in this article to address this. Our algorithm has been implemented in a publicly accessible web page at http://nolan.cnb.csic.es/sentinelcagesmanager.

Identifiants

pubmed: 39102525
doi: 10.1177/00236772231219292
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

236772231219292

Déclaration de conflit d'intérêts

Declaration of conflicting interestsThe authors have no conflicts of interest to declare.

Auteurs

Carlos Oscar S Sorzano (COS)

National Centre of Biotechnology, CSIC, Madrid, Spain.

Irene Sánchez (I)

National Centre of Biotechnology, CSIC, Madrid, Spain.

Angel Naranjo (A)

National Centre of Biotechnology, CSIC, Madrid, Spain.

Classifications MeSH