Evaluation of an alternative method of herd classification for infection with paratuberculosis in cattle herds in the United States | Academic Article individual record
abstract

OBJECTIVE: To develop a better system for classification of herd infection status for paratuberculosis (Johne's disease [JD]) in US cattle herds on the basis of the risk of potential transmission of Mycobacterium avium subsp paratubeculosis. SAMPLE: Simulated data for herd size and within-herd prevalence; sensitivity and specificity for test methods obtained from consensus-based estimates. PROCEDURES: Interrelationships among variables influencing interpretation and classification of herd infection status for JD were evaluated by use of simulated data for various herd sizes, true within-herd prevalences, and sampling and testing methods. The probability of finding ≥ 1 infected animal in herds was estimated for various testing methods and sample sizes by use of hypergeometric random sampling. RESULTS: 2 main components were required for the new herd JD classification system: the probability of detection of infection determined on the basis of test results from a sample of animals and the maximum detected number of animals with positive test results. Tables were constructed of the estimated probability of detection of infection, and the maximum number of cattle with positive test results or fecal pools with positive culture results with 95% confidence for classification of herd JD infection status were plotted. Herd risk for JD was categorized on the basis of 95% confidence that the true within-herd prevalence was ≤ 15%, ≤ 10%, ≤ 5%, or ≤ 2%. CONCLUSIONS AND CLINICAL RELEVANCE: Analysis of the findings indicated that a scientifically rigorous and transparent herd classification system for JD in cattle is feasible.

author list (cited authors)
Tavornpanich, S., Wells, S. J., Fossler, C. P., Roussel, A. J., & Gardner, I. A.
publication date
2012
keywords
  • Paratuberculosis
  • Cattle Diseases
  • Models, Biological
  • United States
  • Computer Simulation
  • Cattle
  • Animals
citation count

3