Bayesian latent class modelling of true prevalence in animal subgroups with application to bovine paratuberculosis infection.

The prevalence of an infectious disease of animals living in separate groups (e.g. herds) is naturally analyzed using a Bayesian hierarchical latent class model. We propose an extension to this methodology by including subgroup level prevalence measures within the groups of animals. As an application illustrating the merits of our methodology, we reassessed the prevalence of bovine paratuberculosis (PTBC) infection in Hungarian commercial dairy farms. Our aim was to consolidate previous findings using a large amount of recent data and priors based on historical data. To model the subgroup level infection prevalence within animal groups, we considered correlated prevalences following beta distributions derived from independent normally distributed random herd effects. In the application, infection status of herds was handled as latent classes, multiparous and primiparous cows as within-herd subgroups. The novel methodology allows us to estimate both the mean and median conditional within-herd true prevalence (CWHP) related to each animal subgroup as well as other measures characterizing the interrelation of subgroups. The results of the application aligned with the findings of the former PTBC study, while the more recent and considerably larger dataset and the use of historical priors increased the reliability of the results. The STAN and JAGS codes of the application are available in Supplementary material.

The Impact of Paratuberculosis on Milk Production, Fertility, and Culling in Large Commercial Hungarian Dairy Herds.

Paratuberculosis (PTBC) is a chronic disease caused by Mycobacterium avium subsp. paratuberculosis (MAP), which is common in dairy herds worldwide, although the scale of its impact on herd productivity is unclear. The aim of our study was to determine the differences between MAP ELISA positive vs. negative cows in terms of milk production and quality, reproductive parameters, and culling. The data of five large dairy herds that participated in the voluntary PTBC testing program in Hungary were analyzed. Cows were tested by ELISA (IDEXX Paratuberculosis Screening Ab Test, IDEXX Laboratories, Inc., Westbrook, ME, USA) using milk samples collected during official performance testing. The outcome of the initial screening test involving all milking cows in the herds was used for the classification of the cows. The 305-day milk production, reproduction and culling data of 4,341 dairy cows, and their monthly performance testing results (n = 87,818) were analyzed. Multivariate linear and logistic models, and right censored tobit model were used for the statistical analysis. Test-day and 305-day milk production of ELISA positive cows decreased by 4.6 kg [95% CI: 3.5-5.6 kg, P < 0.0001 (-13.2%)] and 1,030 kg [95% CI: 708-1,352 kg, P < 0.0001 (-9.4%)], compared to their ELISA negative herdmates, respectively. Milk ELISA positive cows had 35.8% higher [95% CI: 17.9-56.4%, P < 0.0001] somatic cell count, on average. Test positive cows conceived 23.2 days later [95% CI: 9.2-37.3 days, P = 0.0012 (+16.5%)] and their calving interval was 33.8 days longer [95% CI: 13.2-54.4 days, P = 0.0013, (+9.7%)], compared to the negative cows, on average. Milk ELISA positive cows were less likely to conceive to first insemination (odds ratio: 0.49, 95% CI: 0.31-0.75, P = 0.0013), and required 0.42 more inseminations to conceive [95% CI: 0.07-0.77, P = 0.0192 (+13.7%)], on average. Milk ELISA positive cows were culled 160.5 days earlier after testing compared to their ELISA negative herdmates (95% CI: 117.5-203.5 days, P < 0.0001). Our results suggest that MAP ELISA positive cows experience decreased milk production, milk quality, fertility, and longevity, which supports the need to control the prevalence of PTBC in dairy herds.