A Bayesian hierarchical analysis to compare classical and atypical scrapie surveillance data; Wales 2002–2006

Abstract

We describe the application of Bayesian hierarchical models (BHM) to the analysis of risk of sheep scrapie using data from multiple surveillance sources. More specifically, we analysed data from the test results of three surveillance sources on classical and atypical scrapie in Wales for the period 2002–2006. For each form of scrapie, a BHM was fitted to assess the occurrence of spatial patterns of risk shared by the multiple surveillance sources and the association between covariates and disease. We defined a shared-component model whereby the two types of data sources: exhaustive lists (e.g. reports of clinical cases) and sample-based data sources (e.g. abattoir survey) shared a common spatial pattern of risks at parish level. This shared component was adjusted by a risk-gradient parameter that moderated the individual contribution of the datasets. For both forms of scrapie, the risk-gradient was not significantly different indicating that the sensitivity of the two types of dataset was similar for the two diseases. The spatial patterns of the combinations of data sources appeared similar within disease. However, our results suggest that classical and atypical scrapie differ in their spatial patterns and disease determinants. The joint approach permitted inference from all the available evidence and resulted in robust and less biased estimates of risk, particularly for atypical scrapie where the number of observations was very limited.