Evaluation of surveillance and sample collection methods to document freedom from infectious bovine rhinotracheitis in cattle populations

Evaluation of animal and public health surveillance systems: a systematic review

Disease surveillance programmes ought to be evaluated regularly to ensure they provide valuable information in an efficient manner. Evaluation of human and animal health surveillance programmes around the world is currently not standardized and therefore inconsistent. The aim of this systematic review was to review surveillance system attributes and the methods used for their assessment, together with the strengths and weaknesses of existing frameworks for evaluating surveillance in animal health, public health and allied disciplines. Information from 99 articles describing the evaluation of 101 surveillance systems was examined. A wide range of approaches for assessing 23 different system attributes was identified although most evaluations addressed only one or two attributes and comprehensive evaluations were uncommon. Surveillance objectives were often not stated in the articles reviewed and so the reasons for choosing certain attributes for assessment were not always apparent. This has the potential to introduce misleading results in surveillance evaluation. Due to the wide range of system attributes that may be assessed, methods should be explored which collapse these down into a small number of grouped characteristics by focusing on the relationships between attributes and their links to the objectives of the surveillance system and the evaluation. A generic and comprehensive evaluation framework could then be developed consisting of a limited number of common attributes together with several sets of secondary attributes which could be selected depending on the disease or range of diseases under surveillance and the purpose of the surveillance. Economic evaluation should be an integral part of the surveillance evaluation process. This would provide a significant benefit to decision-makers who often need to make choices based on limited or diminishing resources.

Estimating herd prevalence of bovine brucellosis in 46 USA states using slaughter surveillance

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.