Digital dermatitis: Natural lesion progression and regression in Holstein dairy cattle over 3 years

Bovine digital dermatitis (DD) is a leading cause of lameness in dairy cattle in the United States, with prevalence estimates as high as 30%. Whereas clinical lesions have been well described, little is known about the morphologic changes that are associated with the early stages of lesion development from normal skin to clinical lesions. This study used the Iowa DD scoring system to evaluate the epidemiology of natural lesion development by digitally photographing the rear legs of a cohort of dairy cows over a 3-yr period. Sixty-one adult Holstein dairy cows were monitored for 1,032 cow foot-months. The incidence rate of lesion development was 4 lesions per 100 cow foot-months, with the average time for a lesion to develop being 133 d. Whereas 20% of the 1,678 foot observations exhibited clinical DD lesions, an additional 55% of all observations exhibited preclinical stage 1 and 2 lesions that were indicative of DD lesion development. Utilizing the dichotomous categorization of preclinical lesions in the Iowa DD scoring system, it was found that first-lactation heifers had a higher rate of the thickened and crusted "B" type lesions, whereas the ulcerative "A" type lesions were more likely to be identified in multiparous animals. For clinical DD lesions that received topical treatment, scoring of the post-treatment lesions using the Iowa DD scoring system was found to be useful in prognosticating both the risk of recrudescence and the time until recrudescence. Systemic disease, systemic antibiotic therapy, and periparturient stress were not associated with an increase or decrease in DD lesion scores. Treatment with a single topical tetracycline wrap was associated with a significant decrease (-1.17) in DD lesion score. The results of this study demonstrate that the complex morphologic changes associated with digital dermatitis can be readily classified using the Iowa DD scoring system and the scores can be used to predict and monitor the effects of treatment and prevention measures.