Johne's disease
Johne's disease is caused by Mycobacterium avium subspecies paratuberculosis (MAP), which typically infects calves that remain latently infected during a long period, making early detection of infection challenging. Cow to calf transmission can occur in-utero, via milk/colostrum or faecal–orally. Understanding of the different transmission routes to calves is important in informing control recommendations. The aim of a longitudinal study by Nunney et al (2023) (10.1016/j.prevetmed.2023.106022) was to measure the association between the transmission routes via the dam and the environment on a calf subsequently testing serologically positive for MAP. The study population comprised of 439 UK dairy calves from six herds enrolled between 2012 and 2013 and followed up until 2023. At birth individual calf data were captured. During follow-up, individuals entering the milking herd were quarterly tested for the presence of MAP antibodies using milk enzyme-linked immunosorbent assay (ELISA). Cox regression models were used to measure the association between exposure from the dam (in-utero and/or colostrum) or from the environment and time to first detection of MAP infection. An association between calves born to positive dams and probability of having a MAP positive test result remained after excluding potential MAP transmission via colostrum. Calves unlikely to be infected with MAP via the in-utero or colostrum route had a 3.68 (95% CI: 3.68 1.45–9.33) higher hazard of a positive test result when they stayed longer in a dirty calving area. The effect of the dam infection status on transmission to calves precedes the dam's seroconversion and persists after excluding the potential role of transmission via colostrum. The association between time spent in a dirty calving area and probability of a MAP positive test result highlights the role of environmental contamination as a source of infection in addition to the dam.
Tuberculosis
Bovine tuberculosis (bTB) continues to be the costliest, most complex animal health problem in England. The effectiveness of the test-and-slaughter policy is hampered by the imperfect sensitivity of the surveillance tests. Up to half of recurrent incidents within 24 months of a previous one could have been due to undetected infected cattle not being removed. Improving diagnostic testing with more sensitive tests, like the interferon (IFN)-gamma test, is one of the government's top priorities. However, blanket deployment of such tests could result in more false positive results (due to imperfect specificity), together with logistical and costefficiency challenges. A targeted application of such tests in higher prevalence scenarios, such as a subpopulation of high-risk herds, could mitigate against these challenges. Pilar Romero et al (2023) (10.1016/j.prevetmed.2023.106004) developed classification machine learning algorithms to evaluate the deployment of IFN-gamma testing in high-risk herds. The resulting model, classification tree analysis, with an area under a receiver operating characteristic curve >95, showed a 73% sensitivity and a 97% specificity in one test dataset. In another, it predicted 8% of eligible active herds as at-risk of a bovine tuberculosis incident, most of them (66% or 2328 herds) experiencing at least one. While all predicted at-risk herds could have preventive measures applied, the additional application of the IFN-gamma test in parallel interpretation to the statutory skin test, if the risk materialises, would have resulted in 8585 additional IFN-gamma reactors detected (a 217% increase over the 2710 IFN-gamma reactors already detected by tests carried out). The authors therefore conclude that this methodology provides a better way of directing the application of the IFN-gamma test towards the high-risk subgroup of herds. Classification tree analysis ensured the systematic identification of high-risk herds to consistently apply additional measures in a targeted way. This could increase the detection of infected cattle more efficiently, preventing recurrence and accelerating efforts to achieve eradication.
Digital dermatitis
Digital dermatitis is a painful inflammation at the coronary band of the claws, a major cause of lameness in cattle and associated with infections with several Treponema spp. Clinical inspection of the feet is the best way to diagnose digital dermatitis, but this is laborious and stressful for cattle. A simple diagnostic tool was developed by Holzhauer et al (2023) (10.3390/vetsci10090571) to monitor digital dermatitis prevalence at the herd level. An antibody ELISA based on antigens from four different Treponema spp. has been developed and validated in two field studies.
In one study, bulk milk and individual milk samples of seven dairy herds, of which clinical claw scores were obtained, were tested.
In the second study, bulk milk was tested from 110 herds of which clinical scores were obtained. A weak correlation between clinical scores of cows and the ELISA results in individual milk samples was observed. The ELISA response in bulk milk was higher in herds with higher mean clinical scores. Using the ELISA results in bulk milk, herds with a low or high proportion of cattle with digital dermatitis lesions could be distinguished.