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Case definition terminology for paratuberculosis (Johne's disease)
R J Whittington1, D J Begg2, K de Silva2
1Sydney School of Veterinary Science and School of Life and Environmental Sciences, Faculty of Science, The University of Sydney, 425 Werombi Road, Camden, NSW, 2570, Australia. richard.whittington@sydney.edu.au.
Abstract:
Paratuberculosis (Johne's disease) is an economically significant condition caused by Mycobacterium avium subsp. paratuberculosis. However, difficulties in diagnosis and classification of individual animals with the condition have hampered research and impeded efforts to halt its progressive spread in the global livestock industry. Descriptive terms applied to individual animals and herds such as exposed, infected, diseased, clinical, sub-clinical, infectious and resistant need to be defined so that they can be incorporated consistently into well-understood and reproducible case definitions. These allow for consistent classification of individuals in a population for the purposes of analysis based on accurate counts. The outputs might include the incidence of cases, frequency distributions of the number of cases by age class or more sophisticated analyses involving statistical comparisons of immune responses in vaccine development studies, or gene frequencies or expression data from cases and controls in genomic investigations. It is necessary to have agreed definitions in order to be able to make valid comparisons and meta-analyses of experiments conducted over time by a given researcher, in different laboratories, by different researchers, and in different countries. In this paper, terms are applied systematically in an hierarchical flow chart to enable classification of individual animals. We propose descriptive terms for different stages in the pathogenesis of paratuberculosis to enable their use in different types of studies and to enable an independent assessment of the extent to which accepted definitions for stages of disease have been applied consistently in any given study. This will assist in the general interpretation of data between studies, and will facilitate future meta-analyses.
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