Strategies for two-stage sampling designs for estimating herd-level prevalence.

Abstract

We propose a herd-level sample-size formula based on a common adjustment for prevalence estimates when diagnostic tests are imperfect. The formula depends on estimates of herd-level sensitivity and specificity. With Monte Carlo simulations, we explored the effects of different intracluster correlations on herd-level sensitivity and specificity. At low prevalence (e.g. 1% of animals infected), herd-level sensitivity increased with increasing intracluster correlation and many herds were classified as positive based only on false-positive test results. Herd-level sensitivity was less affected at higher prevalence (e.g. 20% of animals infected). A real-life example was developed for estimating ovine progressive pneumonia prevalence in sheep. The approach allows researchers to balance the number of herds and the total number of animals sampled by manipulating herd-level test characteristics (such as the number of animals sampled within a herd).