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  • Title: Estimating herd prevalence of bovine brucellosis in 46 USA states using slaughter surveillance.
    Author: Ebel ED, Williams MS, Tomlinson SM.
    Journal: Prev Vet Med; 2008 Jul 15; 85(3-4):295-316. PubMed ID: 18359525.
    Abstract:
    Making valid inferences about herd prevalence from data collected at slaughter is difficult because the observed sample is dependent on the number of animals sampled from each herd, which varies with herd size and culling practices, and the probability of a positive test result, which depends on variable within-herd prevalence levels as well as test sensitivity and specificity. In this study, brucellosis herd prevalence among beef cow-calf operations is estimated from slaughter surveillance data using a method that combines process modeling with Bayesian inference. Inferences are made for two populations; the first population comprises cow-calf beef herds in a typical U.S. state. The second population represents all beef herds in a collection of 46 low-risk states. The Bayesian Monte Carlo method used in this study links process model inputs to observed surveillance results via Bayes Theorem. The surveillance evidence across multiple years is accumulated at a discounted rate based on the probability of introducing new infection into an area. The process model's inputs include herd size, culling rate per herd, within-herd prevalence, serologic test performance, and the probability of successfully investigating positive results. The surveillance results comprise the number of cows and bulls tested at slaughter and the number of affected herds detected each year. The results find at least 95% confidence that brucellosis herd prevalence among beef cow-calf herds is less than 0.014% (3 per 21,500 herds) and 0.00081% (5 per 6,15,770) after 5 years of slaughter surveillance (with no detections of affected herds) in a typical U.S. state and across 46 low-risk U.S. states, respectively. These results were based on conservative modeling assumptions, but sensitivity analysis suggests only slight changes in the results from changing the assumed process model input values. The most influential analytic input was the probability of introducing new infection into a putatively brucellosis-free state or group of states.
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