A novel Bayesian Latent Class Model (BLCM) evaluates multiple continuous and binary tests: A case study for Brucella abortus in dairy cattle.

Simple item page
dc.citation.volume224
dc.contributor.authorWang Y
dc.contributor.authorVallée E
dc.contributor.authorCompton C
dc.contributor.authorHeuer C
dc.contributor.authorGuo A
dc.contributor.authorWang Y
dc.contributor.authorZhang Z
dc.contributor.authorVignes M
dc.coverage.spatialNetherlands
dc.date.accessioned2024-07-22T21:43:41Z
dc.date.available2024-07-22T21:43:41Z
dc.date.issued2024-03-01
dc.description.abstractBovine brucellosis, primarily caused by Brucella abortus, severely affects both animal health and human well-being. Accurate diagnosis is crucial for designing informed control and prevention measures. Lacking a gold standard test makes it challenging to determine optimal cut-off values and evaluate the diagnostic performance of tests. In this study, we developed a novel Bayesian Latent Class Model that integrates both binary and continuous testing outcomes, incorporating additional fixed (parity) and random (farm) effects, to calibrate optimal cut-off values by maximizing Youden Index. We tested 651 serum samples collected from six dairy farms in two regions of Henan Province, China with four serological tests: Rose Bengal Test, Serum Agglutination Test, Fluorescence Polarization Assay, and Competitive Enzyme-Linked Immunosorbent Assay. Our analysis revealed that the optimal cut-off values for FPA and C-ELISA were 94.2 mP and 0.403 PI, respectively. Sensitivity estimates for the four tests ranged from 69.7% to 89.9%, while specificity estimates varied between 97.1% and 99.6%. The true prevalences in the two study regions in Henan province were 4.7% and 30.3%. Parity-specific odds ratios for positive serological status ranged from 1.2 to 2.2 for different parity groups compared to primiparous cows. This approach provides a robust framework for validating diagnostic tests for both continuous and discrete tests in the absence of a gold standard test. Our findings can enhance our ability to design targeted disease detection strategies and implement effective control measures for brucellosis in Chinese dairy farms.
dc.description.confidentialfalse
dc.edition.editionMarch 2024
dc.format.pagination106115-
dc.identifier.author-urlhttps://www.ncbi.nlm.nih.gov/pubmed/38219433
dc.identifier.citationWang Y, Vallée E, Compton C, Heuer C, Guo A, Wang Y, Zhang Z, Vignes M. (2024). A novel Bayesian Latent Class Model (BLCM) evaluates multiple continuous and binary tests: A case study for Brucella abortus in dairy cattle.. Prev Vet Med. 224. (pp. 106115-).
dc.identifier.doi10.1016/j.prevetmed.2024.106115
dc.identifier.eissn1873-1716
dc.identifier.elements-typejournal-article
dc.identifier.issn0167-5877
dc.identifier.number106115
dc.identifier.piiS0167-5877(24)00001-1
dc.identifier.urihttps://mro.massey.ac.nz/handle/10179/70270
dc.languageeng
dc.publisherElsevier B.V.
dc.publisher.urihttps://www.sciencedirect.com/science/article/pii/S0167587724000011?
dc.relation.isPartOfPrev Vet Med
dc.rights(c) 2024 The Author/s
dc.rightsCC BY 4.0
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectBayesian Latent Class Model (BLCM)
dc.subjectBovine brucellosis
dc.subjectCut-off calibration
dc.subjectDiagnostic performance
dc.subjectReceiver Operating Characteristic (ROC)
dc.subjectSerological tests
dc.subjectFemale
dc.subjectHumans
dc.subjectCattle
dc.subjectAnimals
dc.subjectBrucella abortus
dc.subjectBayes Theorem
dc.subjectLatent Class Analysis
dc.subjectSensitivity and Specificity
dc.subjectAgglutination Tests
dc.subjectBrucellosis
dc.subjectEnzyme-Linked Immunosorbent Assay
dc.subjectBrucellosis, Bovine
dc.subjectAntibodies, Bacterial
dc.subjectSerologic Tests
dc.subjectCattle Diseases
dc.titleA novel Bayesian Latent Class Model (BLCM) evaluates multiple continuous and binary tests: A case study for Brucella abortus in dairy cattle.
dc.typeJournal article
pubs.elements-id485785
pubs.organisational-groupOther
Files
Original bundle
Now showing 1 - 2 of 2
Thumbnail Image
Name:
Published version.pdf
Size:
2.61 MB
Format:
Adobe Portable Document Format
Description:
485785 PDF.pdf
Download
Thumbnail Image
Name:
Evidence.docx
Size:
955.13 KB
Format:
Microsoft Word XML
Description:
1-s2.0-S0167587724000011-mmc1.docx
Download
License bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
license.txt
Size:
9.22 KB
Format:
Plain Text
Description:
Download
Collections
Journal Articles