Proceedings of the 10th International Colloquium on Paratuberculosis

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Use <strong>of</strong> multiple tests to determine <strong>the</strong> status <strong>of</strong> UK dairy herds with respect to Mycobacterium
avium subsp. paratuberculosis
Alasdair Cook, Alberto Vidal-Diez, Mark Arnold, Robin Sayers, George Caldow, Adrian McGoldrick,
John Donaghy, Samuel Strain, Ian Gardner
Veterinary Laboratory Agency, DEFRA, United Kingdom; Scottish Agricultural College, United Kingdom; Agri-Food
and Biosciences Institute, United Kingdom; University <strong>of</strong> California, USA
A random sample <strong>of</strong> 136 UK dairy herds was recruited to a survey to estimate <strong>the</strong> proportion <strong>of</strong> <strong>the</strong>se that were
infected with Mycobacterium avium subsp. paratuberculosis (MAP). Three tests were employed: firstly, heparinised
blood samples were collected from all female cattle over three years old and sera were tested using a
commercial ELISA test (Pourquier); secondly, individual faecal samples were collected from <strong>the</strong>se cows and
combined into pools <strong>of</strong> five which were inoculated into a liquid media system (Trek ESP II) for culture for MAP
and finally, bulk milk samples were collected and tested for presence <strong>of</strong> MAP DNA by PCR targeting <strong>the</strong> IS900
insertion sequence. These tests are all imperfect and <strong>the</strong>re is no “Gold Standard”. They were applied at different
levels – to individual animals, to groups <strong>of</strong> 5 animals or to <strong>the</strong> entire herd. Each herd was categorised as
positive (at least one positive sample) or negative for each test. Bayesian approaches have been proposed
(eg Branscum et al 2004; 2005) for data from two or more imperfect tests and we applied an integrated multilevel
Bayesian model to <strong>the</strong> data to estimate <strong>the</strong> herd-level sensitivity and specificity <strong>of</strong> each test. The median
herd level sensitivity (HSe) and specificity (HSp) and 95% credibility intervals were estimated for each test as
follows: ELISA HSe 95%-100% and HSp 47%-53%; pooled faecal liquid culture HSe 54%-69% and HSp 98%-
100%; bulk milk PCR HSe 13%-51% and HSp 57%-82%. Bayesian analysis incorporates prior estimates <strong>of</strong> parameter
values based on existing knowledge and results can be sensitive to <strong>the</strong>se assumptions. We undertook
a sensitivity analysis by varying <strong>the</strong> prior values that were used in our model. This showed that prior assumptions
<strong>of</strong> <strong>the</strong> individual level specificity <strong>of</strong> <strong>the</strong> ELISA test had a limited impact on <strong>the</strong> results described above.
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