Estimation of the sensitivity and specificity of four serum ELISA and one fecal PCR for diagnosis of paratuberculosis in adult dairy cattle in New Zealand using Bayesian latent class analysis.

In New Zealand, a new diagnostic approach for the control of paratuberculosis in mixed aged milking cows has been developed using a combination of ELISA and quantitative fecal PCR (f-qPCR). Our analysis was designed to evaluate performance of these individual tests in infected or infectious mixed aged cows across the prevalence of infection typically encountered on NZ dairy farms and calculate test accuracy when used as a screening test of serological ELISAs for four separate antigens read in parallel followed by a confirmatory quantitative f-qPCR test. Data from a cross-sectional study of 20 moderate prevalence herds was combined with existing data from 2 low and 20 high prevalence herds forming a dataset of 3845 paired serum and fecal samples. Incidence of clinical Johne's disease (JD) was used to classify herds into three prevalence categories. High (≥ 3% annual clinical JD for the last three years), moderate (<3 - 1%) and low (<1% incidence for at least the last five years). Positive tests were declared if> 50 ELISA units and f-qPCR at two cut-points (≥1 × 10 genomes/mL or >1 × 10 genomes/mL). Fixed Bayesian latent class models at both f-qPCR cut-points, accounted for conditional independence and paired conditional dependence. Mixed models at both f-qPCR cut-points, using a different mechanism to account for conditional dependencies between tests were also implemented. Models (24 in number) were constructed using OpenBUGS. The aim was to identify Mycobacterium avium subsp. paratuberculosis (MAP) infected cows that met at least one of two criteria: shedding sufficient MAP in feces to be detected by f-qPCR or mounting a detectable MAP antibody response. The best fit to the data was obtained by modelling pairwise dependencies between tests in a fixed model or by accounting for dependencies in a mixed model at a fecal cut-off of ≥1 × 10 genomes/mL. Test performance differed with prevalence, but models were robust to prior assumptions. For the fixed model, at a prevalence of 0.29 (95 % probability interval (PI) = 0.25-0.33), as a screening plus confirmatory f-qPCR, post-test probability for disease in a positive animal was 0.84 (95 %PI = 0.80-0.88) and 0.16 (95 %PI = 0.15-0.18) for disease in a test negative animal. In low prevalence herds (0.01(95 %PI = 0.00-0.04)) the equivalent figures were 0.84 (95 %PI = 0.08-0.92) and 0.00 (95 %PI = 0.00-0.02). These results suggest this is a useful tool to control JD on dairy farms, particularly in herds with higher levels of infection, where the sampling and testing cost per animal is defrayed across more detected animals.