Bayesian latent class analysis to estimate the optimal cut-off for the MilA ELISA for the detection of Mycoplasma bovis antibodies in sera, accounting for repeated measures.

The MilA ELISA has been identified as a highly effective diagnostic tool for the detection of Mycoplasma bovis specific antibodies and has been validated for serological use in previous studies. This study aimed to estimate the optimal cut-off and corresponding estimates of diagnostic sensitivity (DSe) and diagnostic specificity (DSp) of the MilA ELISA for testing bovine serum. Serum samples from 298 feedlot cattle from 14 feedlots across four Australian states were tested on entry into the feedlot and approximately 42 days later. The paired serum samples were tested with the MilA ELISA, BIO K302 (Bio-X Diagnostics, Belgium) and BIO K260 (Bio-X Diagnostics, Belgium). A cut-off of 135 AU was estimated to be optimal using Bayesian latent class analysis with three tests in multiple populations, accounting for conditional dependence between tests. At this cut-off, the DSe and DSp of the MilA ELISA were estimated to be 92.1 % (95 % highest probability density [HPD] interval: 87.4, 95.8) and 95.5 % (95 % HPD: 92.4, 97.8), respectively. The DSes of the BIO K260 and BIO K302 ELISAs were estimated to be 60.5 % (95 % HPD: 54.0, 66.9) and 44.6 % (95 % HPD: 38.7, 50.7), respectively. DSps were 95.6 % (95 % HPD: 92.9, 97.7) and 97.8 % (95 % HPD: 95.9, 99.0), respectively. Mycoplasma bovis seroprevalence was remarkably high at follow-up after 42 days on the feedlots. Overall, this study estimated a cut-off, DSe and DSp for the MilA ELISA with less dependence on prior information than previous analyses and demonstrated that the MilA ELISA has higher DSe than the BIO K260 and BIO K302 assays.

Evaluation of the MilA ELISA for the diagnosis of herd infection with Mycoplasma bovis using bulk tank milk and estimation of the prevalence of M. bovis in Australia.

Infection with Mycoplasma bovis has been identified as a growing threat in dairy industries worldwide and there is an urgent need for an inexpensive and accurate herd-level screening tool to identify herds that have been exposed to M. bovis. This study aimed to evaluate the use of the MilA ELISA for testing bulk tank milk (BTM) samples for antibodies against M. bovis and estimate a suitable cut-off and diagnostic sensitivity (DSe) and specificity (DSp) for this assay. An optimal cut-off was then applied for investigating the geographical and seasonal distribution of infection with M. bovis in Australia. A total of 5554 BTM samples from 2683 dairy herds were collected during March, August and December 2017. BTM samples were tested in the MilA ELISA and a cut-off of 29 antibody units (AU) was estimated to be optimal using Bayesian latent class analysis which makes no assumption about the true disease status of herds under investigation. At this cut-off, the DSe and DSp were estimated to be 96.6% (95% highest probability density [HPD] interval: 87.0, 99.8) and 94.2% (95% HPD: 89.9, 97.4), respectively. The diagnostic specifications were found to vary markedly with stage of the production cycle, suggesting that targeted sampling was needed to maximize accuracy. We also found distinct differences in the apparent prevalence of M. bovis in different dairying regions, as well as seasonal variation. The highest apparent prevalence of M. bovis was observed in samples collected in March and an overall drop in the proportion of positive herds was seen from March to December. Overall, this study provides insights into the dynamics of BTM antibodies against M. bovis in Australian dairy herds and how the MilA ELISA can be applied for bulk tank milk testing.