Bayesian Estimation of Diagnostic Accuracy of Three Diagnostic Tests for Bovine Tuberculosis in Egyptian Dairy Cattle Using Latent Class Models

Abstract

The aim of the present study was to calculate the sensitivity (Se) and specificity (Sp) of the single cervical tuberculin test (SCT), rapid lateral flow test (RLFT), and real-time polymerase chain reaction (RT-PCR) for the diagnosis of Mycobacterium bovis (M. bovis) infection in Egyptian dairy cattle herds within a Bayesian framework. The true M. bovis infection within-herd prevalence was assessed as a secondary objective. Data on the test results of SCT, RLFT, and RT-PCR for the detection of M. bovis were available from 245 cows in eleven herds in six major governorates in Egypt. A Bayesian latent class model was built for the estimation of the characteristics of the three tests. Our findings showed that Se of SCT (0.93 (95% Posterior credible interval (PCI): 0.89–0.93)) was higher than that of RT-PCR (0.83 (95% PCI: 0.28–0.93)) but was similar to the Se of RLFT (0.93 (95% PCI: 0.31–0.99)). On the contrary, SCT showed the lowest Sp estimate (0.60 (95% PCI: 0.59–0.65)), whereas Sp estimates of RT-PCR (0.99 (95% PCI: 0.95–1.00)) and RLFT (0.99 (95% PCI: 0.95–1.00)) were comparable. The true prevalence of M. bovis ranged between 0.07 and 0.71. In conclusion, overall, RT-PCR and RLFT registered superior performance to SCT, making them good candidates for routine use in the Egyptian bovine tuberculosis control program.

Ibrahim Elsohaby

Assistant Professor of Public Health and Epidemiology

My research interests include One Health epidemiology of infectious and zoonotic diseases, including antimicrobial resistance.