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A Bayesian approach for the evaluation of six diagnostic assays and the estimation of Cryptosporidium prevalence in dairy calves

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dc.contributor.author Geurden, T. en_US
dc.contributor.author Berkvens, D. en_US
dc.contributor.author Geldhof, P. en_US
dc.contributor.author Vercruysse, J. en_US
dc.contributor.author Claerebout, E. en_US
dc.date.accessioned 2007-12-06T14:36:13Z
dc.date.available 2007-12-06T14:36:13Z
dc.date.issued 2006 en_US
dc.identifier.issn 0928-4249 en_US
dc.identifier.doi http://dx.doi.org/10.1051/vetres:2006029
dc.identifier.other ITG-A2A en_US
dc.identifier.other ANIMAL en_US
dc.identifier.other U-ANIMAL en_US
dc.identifier.other JIF en_US
dc.identifier.other DOI en_US
dc.identifier.other ABSTRACT en_US
dc.identifier.other FTA
dc.identifier.uri http://hdl.handle.net/10390/517
dc.description.abstract The prevalence of Cryptosporidium in calves and the test properties of six diagnostic assays (microscopy (ME), an immunofluorescence assay (IFA), two ELISA and two PCR assays) were estimated using Bayesian analysis. In a first Bayesian approach, the test results of the four conventional techniques were used: ME, IFA and two ELISA. This four-test approach estimated that the calf prevalence was 17% (95% Probability Interval (PI): 0.1-0.28) and that the specificity estimates of the IFA and ELISA were high compared to ME. A six-test Bayesian model was developed using the test results of the 4 conventional assays and 2 PCR assays, resulting in a higher calf prevalence estimate (58% with a 95% PI: 0.5-0.66) and in a different test evaluation: the sensitivity estimates of the conventional techniques decreased in the six-test approach, due to the inclusion of two PCR assays with a higher sensitivity compared to the conventional techniques. The specificity estimates of these conventional assays were comparable in the four-test and six-test approach. These results both illustrate the potential and the pitfalls of a Bayesian analysis in estimating prevalence and test characteristics, since posterior estimates are variables depending both on the data at hand and prior information included in the analysis. The need for sensitive diagnostic assays in epidemiological studies is demonstrated, especially for the identification of subclinically infected animals since the PCR assays identify these animals with reduced oocyst excretion, which the conventional techniques fail to identify. en_US
dc.language English en_US
dc.publisher EDP Sciences
dc.subject Animal diseases en_US
dc.subject Calves en_US
dc.subject Protozoal diseases en_US
dc.subject Cryptosporidium en_US
dc.subject Prevalence en_US
dc.subject Laboratory diagnosis en_US
dc.subject Microscopy en_US
dc.subject Immunofluorescence en_US
dc.subject ELISA en_US
dc.subject Molecular diagnostic tests en_US
dc.subject PCR en_US
dc.subject Evaluation en_US
dc.subject Sensitivity en_US
dc.subject Subclinical en_US
dc.subject Specificity en_US
dc.subject Modeling en_US
dc.subject Bayes theorem en_US
dc.title A Bayesian approach for the evaluation of six diagnostic assays and the estimation of Cryptosporidium prevalence in dairy calves en_US
dc.type Article en_US
dc.citation.issue 5 en_US
dc.citation.jtitle Veterinary Research en_US
dc.citation.volume 37 en_US
dc.citation.pages 671-682 en_US
dc.publisher.place Les Ulis fr
dc.identifier.pmid http://www.ncbi.nlm.nih.gov/pubmed/16777038
dc.identifier.url http://www.vetres.org/
dc.citation.jabbreviation Vet Res en_US


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