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Modeling the Accuracy of Two in-vitro Bovine Tuberculosis Tests Using a Bayesian Approach

Frontiers in veterinary science publish this investigation article

August 13rd, 2019

Accuracy of new or alternative diagnostic tests is typically estimated in relation to a well-standardized reference test referred to as a gold standard. However, for bovine tuberculosis (bTB), a chronic disease of cattle, affecting animal and public health, no reliable gold standard is available. In this context, latent-class models implemented using a Bayesian approach can help to assess the accuracy of diagnostic tests incorporating previous knowledge on test performance and disease prevalence. In Uruguay, bTB-prevalence has increased in the past decades partially because of the limited accuracy of the diagnostic strategy in place, based on intradermal testing (caudal fold test, CFT, for screening and comparative cervical test, CCT, for confirmation) and slaughter of reactors. Here, we evaluated the performance of two alternative bTB-diagnostic tools, the interferon-gamma assay, IGRA, and the enzyme-linked immunosorbent assay (ELISA), which had never been used in Uruguay in the absence of a gold standard. In order to do so animals from two heavily infected dairy herds and tested with CFT-CCT were also analyzed with the IGRA using two antigens (study 1) and the ELISA (study 2). The accuracy of the IGRA and ELISA was assessed fitting two latent-class models: a two test-one population model (LCA-a) based on the analysis of CFT/CFT-CCT test results and one in-vitro test (IGRA/ELISA), and a one test-one population model (LCA-b) using the IGRA or ELISA information in which the prevalence was modeled using information from the skin tests. Posterior estimates for model LCA-a suggested that IGRA was as sensitive (75-78%) as the CFT and more sensitive than the serial use of CFT-CCT. Its specificity (90-96%) was superior to the one for the CFT and equivalent to the use of CFT-CCT. Estimates from LCA-b models consistently yielded lower posterior Se estimates for the IGRA but similar results for its Sp. Estimates for the Se (52% 95%PPI:44.41-71.28) and the Sp (92% 95%PPI:78.63-98.76) of the ELISA were however similar regardless of the model used. These results suggest that the incorporation of IGRA for detection of bTB in highly infected herds could be a useful tool to improve the sensitivity of the bTB-control in Uruguay




Picasso C., Perez AM., Gil A., Nunez A., Salaberry X., Suanses A. and Alvarez J..




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Modeling the Accuracy of Two in-vitro Bovine Tuberculosis Tests Using a Bayesian Approach

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Modeling the Accuracy of Two in-vitro Bovine Tuberculosis Tests Using a Bayesian Approach



Participants:

University of MinnesotaDepartment of Veterinary Population Medicine. College of Veterinary Medicine. University of Minnesota (UMM).

Universidad de la RepúblicaFacultad de Veterinaria. Universidad de la República.

Ministerio de Ganadería, Agricultura y PescaDivisión Laboratorios Veterinarios Miguel C. Rubino. Ministerio de Ganadería, Agricultura y Pesca.

Universidad ComplutenseCentro de Vigilancia Sanitaria Veterinaria (VISAVET). Universidad Complutense (UCM).

Universidad ComplutenseDepartamento de Sanidad Animal. Facultad de Veterinaria. Universidad Complutense (UCM).







Frontiers in veterinary science
FACTOR YEAR Q
2.245 2019

NLMID: 101666658

PMID: 31457019

ISSN: 2297-1769



TITLE: Modeling the Accuracy of Two in-vitro Bovine Tuberculosis Tests Using a Bayesian Approach


JOURNAL: Front Vet Sci


NUMERACIÓN: 6:261


AÑO: 2019


PUBLISHER: Lausanne : Frontiers Media S.A


AUTHORS: Picasso C., Perez AM., Gil A., Nunez A., Salaberry X., Suanses A. and Alvarez J..


2nd
Andrés Maximiliano Pérez
Last
Julio Álvarez Sánchez

DOI: https://doi.org/10.3389/fvets.2019.00261


CITE THIS PUBLICATION:

Picasso C., Perez AM., Gil A., Nunez A., Salaberry X., Suanses A. and Alvarez J. Modeling the Accuracy of Two in-vitro Bovine Tuberculosis Tests Using a Bayesian Approach. Frontiers in veterinary science. 6:261. 2019. (A). ISSN: 2297-1769. DOI: 10.3389/fvets.2019.00261