In the present study we assessed the
standardisation of the active surveillance of
scrapie throughout time across the EU and
identified countries with similar underlying
characteristics allowing comparisons between
The abattoir and the fallen stock surveys
constitute the active surveillance component
aimed at improving the detection of scrapie
across the EU. Previous studies have suggested
the occurrence of significant differences in the
operation of the surveys throughout Europe
[1]. [1] assessed the presence of heterogeneity
between the observed prevalence estimates of
18 EU countries by means of a meta-analysis
and showed a large residual variability
indicating an inconsistent approach to the
surveys across the EU. The study of these
differences merits attention as they inform
discrepancies in the performance of the
surveys between countries. In the absence of
sufficient covariate information to explain the
observed variability across countries, we can
model, still under the general context of the
meta-analysis, the unobserved heterogeneity in
our data. Countries could be grouped into
clusters representing the underlying
subpopulations relative to the risk of scrapie
between the two surveys in each country.

We start our analyses by defining the measure
of effect to be compared between the EU
countries under the meta-analysis approach.
Our EU data can be displayed in a 2×2 table
and risk ratios (RR) between the FS and the
AS computed for each country. We conducted
a random-effects meta-analysis to study the
presence of heterogeneity between the
countries. We did so for the three years of data
available (2003, 2004 and 2005) for classical
scrapie. We extended our analysis to atypical
scrapie (2005 only). To estimate the RR under
conditions of unobserved heterogeneity we
applied the profile likelihood (PL) [2] to
inform the parametric density, also called the
mixture kernel, of the non parametric mixture
distribution. This allows the identification of
components or clusters of countries. If the
number of clusters is 1 we have the
homogeneity situation. Furthermore, we
extended our analysis to incorporate country

specific covariates informing systematic
variability between countries. More
specifically, we modelled the proportion of the
adult sheep population sampled by the fallen
stock survey every year.
Our results show that the between-country
heterogeneity dropped in 2004 and 2005
relative to that of 2003 for classical scrapie. As
a consequence, the number of clusters in the
last year was also reduced indicating the
gradual standardisation of the surveillance
efforts across the EU. The crude analyses of
the atypical data grouped all the countries in
one cluster showing non-significant gain in the
detection of this type of scrapie by any of the
two sources. The proportion of the population
sampled by the fallen stock survey appeared
significantly associated with our risk ratio for
both types of scrapie, although in opposite
directions: negative for classical and positive
for atypical.
Two major conclusions can be extracted from
our study. The first is that there appears to be a
gradual standardisation of the active
surveillance of scrapie across the EU. Ideally
this would be as a result of homogeneous
practices in all countries. We argued that the
alternative, the gradual standardisation of the
targeted populations across the EU, would be
far from ideal. The second major conclusion is
the apparent loss of usefulness of the fallen
stock survey across the years.