A stochastic model to determine the economic value of changing diagnostic test characteristics for identification of cattle for treatment of bovine respiratory disease.

Abstract

Bovine respiratory disease is an economically important syndrome in the beef industry, and diagnostic accuracy is important for optimal disease management. The objective of this study was to determine whether improving diagnostic sensitivity or specificity was of greater economic value at varied levels of respiratory disease prevalence by using Monte Carlo simulation. Existing literature was used to populate model distributions of published sensitivity, specificity, and performance (ADG, carcass weight, yield grade, quality grade, and mortality risk) differences among calves based on clinical respiratory disease status. Data from multiple cattle feeding operations were used to generate true ranges of respiratory disease prevalence and associated mortality. Input variables were combined into a single model that calculated estimated net returns for animals by diagnostic category (true positive, false positive, false negative, and true negative) based on the prevalence, sensitivity, and specificity for each iteration. Net returns for each diagnostic category were multiplied by the proportion of animals in each diagnostic category to determine group profitability. Apparent prevalence was categorized into low (<15%) and high (≥15%) groups. For both apparent prevalence categories, increasing specificity created more rapid, positive change in net returns than increasing sensitivity. Improvement of diagnostic specificity, perhaps through a confirmatory test interpreted in series or pen-level diagnostics, can increase diagnostic value more than improving sensitivity. Mortality risk was the primary driver for net returns. The results from this study are important for determining future research priorities to analyze diagnostic techniques for bovine respiratory disease and provide a novel way for modeling diagnostic tests.