A novel Bayesian Latent Class Model (BLCM) evaluates multiple continuous and binary tests : A case study for Brucella abortus in dairy cattle
Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved..
Bovine 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.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:224 |
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| Enthalten in: |
Preventive veterinary medicine - 224(2024) vom: 15. Feb., Seite 106115 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Wang, Yu [VerfasserIn] |
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| Links: |
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| Themen: |
Antibodies, Bacterial |
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| Anmerkungen: |
Date Completed 19.02.2024 Date Revised 19.02.2024 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1016/j.prevetmed.2024.106115 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| 520 | |a Bovine 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 | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Bayesian Latent Class Model (BLCM) | |
| 650 | 4 | |a Bovine brucellosis | |
| 650 | 4 | |a Cut-off calibration | |
| 650 | 4 | |a Diagnostic performance | |
| 650 | 4 | |a Receiver Operating Characteristic (ROC) | |
| 650 | 4 | |a Serological tests | |
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| 700 | 1 | |a Vallée, Emilie |e verfasserin |4 aut | |
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| 700 | 1 | |a Heuer, Cord |e verfasserin |4 aut | |
| 700 | 1 | |a Guo, Aizhen |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Youming |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Zhen |e verfasserin |4 aut | |
| 700 | 1 | |a Vignes, Matthieu |e verfasserin |4 aut | |
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