Abstract
BackgroundOne of the key steps in the management of chronic diseases in animals including Johne’s disease (JD), caused by Mycobacterium avium subsp. paratuberculosis (MAP), is the ability to track disease incidence over space and time. JD surveillance in the U.S. dairy cattle is challenging due to lack of regulatory requirements, imperfect diagnostic tests, and associated expenses, including time and labor. An alternative approach is to use voluntary testing programs. Here, data from a voluntary JD testing program, conducted by the Minnesota Dairy Herd Improvement Association, were used to: a) explore whether such a program provides representative information on JD-prevalence in Minnesota dairy herds, b) estimate JD distribution, and, c) identify herd and environmental factors associated with finding JD-positive cows. Milk samples (n = 70,809) collected from 54,652 unique cows from 600 Minnesota dairy herds between November 2014 and April 2017 were tested using a MAP antibody ELISA. Participant representativeness was assessed by comparing the number of JD-tested herds with the number of herds required to estimate the true disease prevalence per county based on official statistics from the National Agricultural Statistical Services. Multivariable logistic regression models, with and without spatial dependence between observations, were then used to investigate the association between herd status to JD (positive/negative), as indicated by milk ELISA results, and available covariates at the herd level.ResultsWithin the study population, at least one test-positive cow was found in 414 of 600 (69%) herds. Results indicated that large herds that test frequently and herds located in loamy or silt soils are more likely to have at least one MAP test-positive cow. After adjusting for herd size, testing frequency, and soil type, there was no spatial dependence in JD risk between neighboring dairies within 5 to 20 km. Furthermore, the importance of collecting data on herd management, feed, and biosecurity for insightful interpretations was recognized. The study suggested that, although limited, the voluntary testing database may support monitoring JD status.ConclusionsResults presented here help elucidate the spatial characteristics of JD in Minnesota and the study may ultimately contribute to the design and implementation of surveillance programs for the disease.
Highlights
One of the key steps in the management of chronic diseases in animals including Johne’s disease (JD), caused by Mycobacterium avium subsp. paratuberculosis (MAP), is the ability to track disease incidence over space and time
Spatial representativeness During the 2.5-year study period, 600/4746 (13%) dairy herds in Minnesota tested at least once for JD at Minnesota Dairy Herd Improvement Association (MNDHIA) laboratories, representing 18.7% (600/3210) of the licensed dairy herds in Minnesota with permits to ship milk for human consumption [17]
We observed that the distribution of the MNDHIA participants included in this study mirrors the pattern of the milk cow herds included in the United States Department of Agriculture (USDA) National Agricultural Statistics Services (NASS) 2012 report (Fig. 1)
Summary
One of the key steps in the management of chronic diseases in animals including Johne’s disease (JD), caused by Mycobacterium avium subsp. paratuberculosis (MAP), is the ability to track disease incidence over space and time. One of the key steps in the management of chronic diseases in animals including Johne’s disease (JD), caused by Mycobacterium avium subsp. JD surveillance in the U.S dairy cattle is challenging due to lack of regulatory requirements, imperfect diagnostic tests, and associated expenses, including time and labor. The management and control of a chronic disease such as JD in a proactive and organized manner is challenging in the U.S due to lack of regulatory requirements for testing [9], imperfect diagnostic tests [10], long-term survival of the pathogen outside the host [11], multiple routes of transmission, and the cost and labor necessary for long-term disease tracking [12]
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