Abstract
Mastitis, a worldwide endemic disease of dairy cows, is an important cause of decreased efficiency in milk production. Early medical treatment can reduce the nonreversible losses in milk production caused by this infection. Various diagnostic tests for mastitis are available, including a test measuring the electrical conductivity of milk (MEC test), the industry standard of somatic cell counting (SCC test), a bacteriological test, and a recently developed test measuring mammary associated amyloid A (MAA test). None of these tests is considered a gold standard, however. The aim of the present study was to determine which of these tests provides the best results, and at what cost, to improve the efficiency of milk production. For this study, 25 cows were tested at all four quarters of the udder with each of the aforementioned mastitis diagnostic tests. Based on the data, the disease prevalence as well as the sensitivity and the specificity of the four tests were estimated with a Bayesian approach by extending the Hui and Walter model with two independent tests and two populations to a model with four partially dependent tests and one population. This model was further combined with a receiver operating characteristics analysis to estimate the overall test accuracy.
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References
Black, M. A., and Craig, B. A. (2002), “Estimating Disease Prevalence in the Absence of a Gold Standard,” Statistics in Medicine, 21, 2653–2669.
Enøe, C., Georgiadis, M. P., and Johnson, W. O. (2000), “Estimation of Sensitivity and Specificity of Diagnostic Tests and Disease Prevalence When the True Disease State Is Unknown,” Preventive Veterinary Medicine, 45, 61–81.
Gardner, I. A., Stryhn, H., Lind, P., and Collins, M. T. (2000), “Conditional Dependence Between Tests Affects the Diagnosis and Surveillance of Animal Diseases,” Preventive Veterinary Medicine, 45, 107–122.
Gilks, W. R., Richardson, S., and Spiegelhalter, D. J. (eds.) (1995), Markov Chain Monte Carlo in Practice, London: Chapman & Hall.
Hoeting, J., Madigan, D., Raftery, A., and Volinsky, C. (1999), “Bayesian Model Averaging: A Tutorial,” Statistical Science, 14, 382–401.
Hogeveen, H. (ed.) (2005), Mastitis in Dairy Production, The Netherlands: Wageningen Academic Publishers.
Hui, S. L., and Walter, S. D. (1980), “Estimating the Error Rates of Diagnostic Tests,” Biometrics, 36, 167–171.
Toft, N., Innocent, G. T., Gettinby, G., and Reid, S. W.J. (2007), “Assessing the Convergence of Markov Chain Monte Carlo Methods: An Example From Evaluation of Diagnostic Tests in Absence of a Gold Standard,” Preventive Veterinary Medicine, 79, 244–256.
Toft, N., Jørgensen, E., and Højsgaard, S. (2005), “Diagnosing Diagnostic Tests: Evaluating the Assumptions Underlying the Estimation of Sensitivity and Specificity in the Absence of a Gold Standard,” Preventive Veterinary Medicine, 68, 19–33.
Walkenhorst, M. (2004), “Eine gute Prävention erhält die Eutergesundheit,” Die Grüne, 4, 38–39.
Whyte, D. S., Johnstone, P. T., Claycomb, R. W., and Mein, G. A. (2004), “On-Line Sensors for Earlier, More Reliable Mastitis Detection,” in British Mastitis Conference 2004.
Zhou, X., Obuchowski, N. A., and McClish, D. K. (2002), Statistical Methods in Diagnostic Medicine, New York: Wiley.
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Uhler, C. Mastitis in dairy production: Estimation of sensitivity, specificity and disease prevalence in the absence of a gold standard. JABES 14, 79–98 (2009). https://doi.org/10.1198/jabes.2009.0005
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DOI: https://doi.org/10.1198/jabes.2009.0005