Modelling variation in test sensitivity for monitoring leptospirosis in beef cattle.

Bayesian Diagnostic sensitivity and specificity Leptospirosis Mixture models Prevalence distribution

Journal

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

Informations de publication

Date de publication:
Dec 2023
Historique:
received: 01 08 2023
revised: 01 11 2023
accepted: 06 11 2023
pubmed: 18 11 2023
medline: 18 11 2023
entrez: 17 11 2023
Statut: ppublish

Résumé

When Bayesian latent class analysis is used for diagnostic test data in the absence of a gold standard test, it is common to assume that any unknown test sensitivities and specificities are constant across different populations. Indeed this assumption is often necessary for model identifiability. However there are a number of practical situations, depending on the type of test and the nature of the disease, where this assumption may not be true. We present a case study of using a microscopic agglutination test to diagnose leptospiroris infection in beef cattle, which strongly suggests that sensitivity in particular varies among herds. We develop and fit an alternative model in which sensitivity is related to within-herd prevalence, and discuss the statistical and epidemiological implications.

Identifiants

pubmed: 37976969
pii: S0167-5877(23)00238-6
doi: 10.1016/j.prevetmed.2023.106074
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

106074

Informations de copyright

Copyright © 2023 The Authors. Published by Elsevier B.V. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of Competing Interest No conflicts of interest.

Auteurs

Geoff Jones (G)

School of Mathematical and Computational Sciences, Massey University, Palmerston North 4442, New Zealand. Electronic address: g.jones@massey.ac.nz.

Wesley O Johnson (WO)

Department of Statistics, University of California, Irvine, USA.

Cord Heuer (C)

EpiCentre, Massey University, Palmerston North 4442, New Zealand.

Classifications MeSH