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This paper describes a Bayesian approach to prevalence estimation based on pooled samples that accommodates variation in pool size and adjusts for test imperfection. A logistic model was developed for pooled fecal culture (PFC) sensitivity as a function of pool size and a logistic mixed model for ovine Johne’s disease (OJD) prevalence as a function of covariates that were found significant in a recent OJD risk factor study conducted in Australia. Available data on these factors and prior information about prevalence and sensitivity were incorporated into a Bayesian model to estimate OJD prevalence from PFC data. Overall, posterior cohort OJD prevalence was estimated to be 0.16 (range of prevalences across cohorts 0.002 to 0.72). The average prevalence was higher in wethers than ewes. PFC sensitivities for pool sizes 10, 30 and 50 were estimated to be 0.91 (95% probability intervals 0.80, 0.96), 0.85 (0.80, 0.90) and 0.77 (0.65, 0.88), respectively. Posterior specificity of PFC was almost perfect though based primarily on the prior. Results suggest the Bayesian model successfully estimated the animal-level prevalence after accounting for variable pool size and imperfect test parameters. The method can be easily adapted for other conditions and diseases where pooled samples are collected. WinBugs code for the article is available online.

A Bayesian Approach to Estimate OJD Prevalence From Pooled Fecal Samples of Variable Pool Size