Accuracy of PCR, mycobacterial culture and interferon-γ assays for detection of Mycobacterium bovis in blood and milk samples from Egyptian dairy cows using Bayesian modelling.

2020 
Abstract The number of bovine tuberculosis (bTB) infected dairy herds in Egypt is growing and this calls for accurate and reliable diagnostic methods at cow level for cost-effective bTB eradication as culling of the whole herd is not economically sustainable. The present study aimed to estimate the sensitivity (Se) and specificity (Sp) of PCR, mycobacterial culture and interferon-γ (IFN-γ) assays for Mycobacterium bovis (M. bovis) detection in blood and milk samples from dairy cows in Egyptian dairy herds within a Bayesian framework. As a secondary objective, the distribution of true within-herd prevalence of M. bovis infection was estimated. Blood and milk samples were collected from 245 Holstein dairy cows in 11 Egyptian dairy herds and subjected to PCR, mycobacterial culture and IFN-γ testing. With respect to the detection of M. bovis in blood, IFN-γ recorded higher Se [0.97 (95% Posterior Credible Interval (PCI): 0.87–1.00)] than PCR [0.68 (95% PCI: 0.53–0.95)] and culture [0.22 (95% PCI: 0.13–0.37)]. However, Sp estimates of PCR [0.98 (95% PCI: 0.95–1.00)], culture [0.99 (95% PCI: 0.98–1.00)] and IFN-γ [0.97 (95% PCI: 0.88–1.00)] were comparable. As for milk samples, Se estimate of PCR [0.29 (95% PCI: 0.01–0.60)] was higher than that of culture [0.08 (95% PCI: 0.001–0.23)]. However, the Sp estimates of both tests were statistically similar. The estimated true within-herd prevalences of M. bovis varied across the tested bovine subpopulations and ranged between 0.06 and 0.66. In conclusion, IFN-γ registered a similar overall performance to PCR but was superior to mycobacterial culture. With its good accuracy and wide applicability, IFN-γ lends itself to use in the Egyptian bTB eradication program.
    • Correction
    • Source
    • Cite
    • Save
    • Machine Reading By IdeaReader
    52
    References
    3
    Citations
    NaN
    KQI
    []