1398 INTRODUCTION Bovine respiratory disease (BRD) is the most common and economically signifcant disease af- fecting cattle in the United States (Galyean et al., 1999; Lechtenberg et al., 2011). Diagnosis of BRD is commonly performed by observing clinical signs including depression, anorexia, coughing, nasal dis- charge, and lack of rumen fll (Smith et al., 2001). Current methods used to diagnose BRD based on visual inspection have poor sensitivity and specifc- ity (Amrine et al., 2013; Leruste, 2012; White and Renter, 2009). Poor sensitivity and specifcity cause frequent erroneous diagnoses by not identifying and treating truly diseased cattle (low sensitivity) and un- necessarily treating disease-free animals (low speci- fcity). Treatment effcacy is evaluated and wellness assessments are commonly performed using these low sensitivity and specifcity measures demonstrat- ing economic losses and misrepresentation of the ef- fect BRD has on the industry. Performance differences between calves di- agnosed with BRD compared to clinically healthy calves vary in published literature. The objective of this study is to determine the economic value of A stochastic model to determine the economic value of changing diagnostic test characteristics for identi fcation of cattle for treatment of bovine respiratory disease 1 M. E. Theurer,* B. J. White,† 2 R. L. Larson,† and T. C. Schroeder‡ *Department of Diagnostic Medicine and Pathobiology, College of Veterinary Medicine, Kansas State University, Manhattan 66506; †Department of Clinical Sciences, College of Veterinary Medicine, Kansas State University, Manhattan 66506; and ‡Department of Agricultural Economics, College of Agriculture, Kansas State University, Manhattan 66506 ABSTRACT: Bovine respiratory disease is an eco- nomically important syndrome in the beef industry, and diagnostic accuracy is important for optimal dis- ease management. The objective of this study was to determine whether improving diagnostic sensitivity or specifcity was of greater economic value at var- ied levels of respiratory disease prevalence by using Monte Carlo simulation. Existing literature was used to populate model distributions of published sensi- tivity, specifcity, and performance (ADG, carcass weight, yield grade, quality grade, and mortality risk) differences among calves based on clinical respira- tory disease status. Data from multiple cattle feeding operations were used to generate true ranges of respi- ratory 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 preva- lence, sensitivity, and specifcity for each iteration. Net returns for each diagnostic category were multi- plied by the proportion of animals in each diagnostic category to determine group proftability. Apparent prevalence was categorized into low (<15%) and high (≥15%) groups. For both apparent prevalence categories, increasing specifcity created more rapid, positive change in net returns than increasing sensi- tivity. Improvement of diagnostic specifcity, perhaps through a confrmatory test interpreted in series or pen-level diagnostics, can increase diagnostic val- ue 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. Key words: bovine respiratory disease, diagnostic tools, economic modeling, Monte Carlo simulation © 2015 American Society of Animal Science. All rights reserved. J. Anim. Sci. 2015.93:1398–1410 doi:10.2527/jas2014-8487 1 There was no extrainstitutional funding or support for this project. 2 Corresponding author: bwhite@vet.k-state.edu Received September 5, 2014. Accepted December 10, 2014. Published March 31, 2015