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Bayesian estimation of the performance of using clinical observations and harvest lung lesions for diagnosing bovine respiratory disease in post-weaned beef calves

Correspondence: ¹Corresponding Author: Brad J. White, Department of Clinical Sciences, Kansas State University, Manhattan, KS 66506. bwhite{at}vet.ksu.edu

Bovine respiratory disease (BRD) diagnosis during the postweaning phase of beef production is an important component of effective preventive health and treatment programs. Although identification of diseased animals based on signs of clinical illness (CI) is a common method in the beef industry for identifying BRD, very little information is available on the accuracy of this method. Previous investigators hypothesized that monitoring pulmonary lesions at harvest (LU) could be a more reliable indicator of disease status during the postweaning phase. A structured literature review was conducted to identify research that compared CI and LU. Because there is no true gold standard for diagnosing BRD, Bayesian methods were used to estimate the sensitivity and specificity of each diagnostic method relative to a BRD diagnosis at any time during the postweaning phase. Results from the current study indicate that the estimated diagnostic sensitivity and specificity of CI were 61.8% (97.5% probability interval [PI]: 55.7, 68.4) and 62.8% (97.5% PI: 60.0, 65.7), respectively. Use of LU for a BRD diagnosis was estimated to have a sensitivity of 77.4% (97.5% PI: 66.2, 87.3) and a specificity of 89.7% (97.5% PI: 86.0, 93.8). Further analysis revealed that the probabilities of LU having higher sensitivity and specificity than CI were 99.4% and 100%, respectively. The present research indicates that neither method was perfect, and both methods were relatively poor at correctly classifying truly diseased animals (sensitivity) but that LU was more accurate than CI for BRD diagnosis. Results from the present study should be considered when these diagnostic methods are used to evaluate BRD outcomes in clinical and research settings.

Key Words: Bayesian analysis • bovine respiratory disease • diagnostic methods