By Bennett, T. and Dopfer, D. and Tremblay, M., Prev Vet Med, 2016
Research Paper Web Link / URL:
Digital dermatitis (DD) is the most important infectious claw disease in the cattle industry causing outbreaks of lameness. The clinical course of disease can be classified using 5 clinical stages. M-stages represent not only different disease severities but also unique clinical characteristics and outcomes. Monitoring the proportions of cows per M-stage is needed to better understand and address DD and factors influencing risks of DD in a herd. Changes in the proportion of cows per M-stage over time or between groups may be attributed to differences in management, environment, or treatment and can have impact on the future claw health of the herd. Yet trends in claw health regarding DD are not intuitively noticed without statistical analysis of detailed records. Our specific aim was to develop a mobile application (app) for persons with less statistical training, experience or supporting programs that would standardize M-stage records, automate data analysis including trends of M-stages over time, the calculation of predictions and assignments of Cow Types (i.e., Cow Types I-III are assigned to cows without active lesions, single and repeated cases of active DD lesions, respectively). The predictions were the stationary distributions of transitions between DD states (i.e., M-stages or signs of chronicity) in a class-structured multi-state Markov chain population model commonly used to model endemic diseases. We hypothesized that the app can be used at different levels of record detail to discover significant trends in the prevalence of M-stages that help to make informed decisions to prevent and control DD on-farm. Four data sets were used to test the flexibility and value of the DD Check App. The app allows easy recording of M-stages in different environments and is flexible in terms of the users' goals and the level of detail used. Results show that this tool discovers trends in M-stage proportions, predicts potential outbreaks of DD, and makes comparisons among Cow Types, signs of chronicity, scorers or pens. The DD Check App also provides a list of cows that should be treated augmented by individual Cow Types to help guide treatment and determine prognoses. Producers can be proactive instead of reactive in controlling DD in a herd by using this app. The DD Check App serves as an example of how technology makes knowledge and advice of veterinary epidemiology widely available to monitor, control and prevent this complex disease.
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