Mastitis in dairy production: Estimation of sensitivity, specificity and disease prevalence in the absence of a gold standard

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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Correspondence to Caroline Uhler.

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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 (2009). https://doi.org/10.1198/jabes.2009.0005

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Key Words

  • Bayesian approach
  • Bayesian model averaging
  • Hui and Walter model
  • Mastitis diagnostic tests
  • MCMC
  • RJMCMC
  • ROC curve