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      Assessment of the diagnostic sensitivity and specificity of an indirect ELISA kit for the diagnosis of Brucella ovis infection in rams

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          Brucella ovis causes an infectious disease responsible for infertility and subsequent economic losses in sheep production. The standard serological test to detect B. ovis infection in rams is the complement fixation test (CFT), which has imperfect sensitivity and specificity in addition to technical drawbacks. Other available tests include the indirect enzyme-linked immunosorbent assays (I-ELISA) but no I-ELISA kit has been fully evaluated.

          The study aimed to compare an I-ELISA kit and the standard CFT. Our study was carried out on serum samples from 4599 rams from the South of France where the disease is enzootic. A Bayesian approach was used to estimate tests characteristics (diagnostic sensitivity, Se and diagnostic specificity, Sp). The tests were then studied together in order to optimise testing strategies to detect B. ovis.


          After optimising the cut-off values in order to avoid doubtful results without deteriorating the concordance between the results of the two tests, the I-ELISA appeared to be slightly more sensitive than CFT (Se I-ELISA = 0.917 [0.822; 0.992], 95% Credibility Interval (CrI) compared to Se CFT = 0.860 [0.740; 0.967], 95% CrI). However, CFT was slightly more specific than I-ELISA (Sp CFT = 0.988 [0.947; 1.0], 95% CrI) compared to Sp I-ELISA =0.952 [0.901; 1.0], 95% CrI).

          The tests were then associated with two different interpretation schemes. The series association increased the specificity of screening and could be used for pre-movement testing in rams from uninfected flocks. The parallel association increased sequence sensitivity, thus appearing more suitable for eradicating the disease in infected flocks.


          The high sensitivity and acceptable specificity of this I-ELISA kit support its potential interest to avoid the limitations of CFT. The two tests could also be used together or combined with other diagnostic methods such as semen culture to improve the testing strategy. The choice of test sequence and interpretation criteria depends on the epidemiological context, screening objectives and the financial and practical constraints.

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          Most cited references 23

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          Estimation of diagnostic-test sensitivity and specificity through Bayesian modeling.

          We review recent Bayesian approaches to estimation (based on cross-sectional sampling designs) of the sensitivity and specificity of one or more diagnostic tests. Our primary goal is to provide veterinary researchers with a concise presentation of the computational aspects involved in using the Bayesian framework for test evaluation. We consider estimation of diagnostic-test sensitivity and specificity in the following settings: (i) one test in one population, (ii) two conditionally independent tests in two or more populations, (iii) two correlated tests in two or more populations, and (iv) three tests in two or more populations, where two tests are correlated but jointly independent of the third test. For each scenario, we describe a Bayesian model that incorporates parameters of interest. The WinBUGS code used to fit each model, which is available at http://www.epi.ucdavis.edu/diagnos-tictests/, can be altered readily to conform to different data.
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            Statistics and Computing, 10(4), 325-337
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              Bayesian approaches to modeling the conditional dependence between multiple diagnostic tests.

              Many analyses of results from multiple diagnostic tests assume the tests are statistically independent conditional on the true disease status of the subject. This assumption may be violated in practice, especially in situations where none of the tests is a perfectly accurate gold standard. Classical inference for models accounting for the conditional dependence between tests requires that results from at least four different tests be used in order to obtain an identifiable solution, but it is not always feasible to have results from this many tests. We use a Bayesian approach to draw inferences about the disease prevalence and test properties while adjusting for the possibility of conditional dependence between tests, particularly when we have only two tests. We propose both fixed and random effects models. Since with fewer than four tests the problem is nonidentifiable, the posterior distributions are strongly dependent on the prior information about the test properties and the disease prevalence, even with large sample sizes. If the degree of correlation between the tests is known a priori with high precision, then our methods adjust for the dependence between the tests. Otherwise, our methods provide adjusted inferences that incorporate all of the uncertainty inherent in the problem, typically resulting in wider interval estimates. We illustrate our methods using data from a study on the prevalence of Strongyloides infection among Cambodian refugees to Canada.

                Author and article information

                BMC Vet Res
                BMC Vet. Res
                BMC Veterinary Research
                BioMed Central
                9 July 2012
                : 8
                : 68
                [1 ]National Veterinary School of Alfort (ENVA) / French Agency for Food, Environmental and Occupational Health Safety (ANSES), USC Epidemiology of Animal Infectious Diseases Unit (Epi-MAI), 94700, Maisons-Alfort, France
                [2 ]Groupement de Défense Sanitaire des Alpes de Haute Provence, 04000, Digne les Bains, France
                [3 ]ANSES, Animal Health Laboratory, EU/OIE/FAO Brucellosis Reference Laboratory, 94706, Maisons-Alfort, France
                [4 ]INSERM, Centre for research in Epidemiology and Population Health (CESP), U1018, Faculté de Médecine Paris-Sud, Le Kremlin-Bicêtre; AP-HP, Hopital Bicêtre, Epidemiology and Public Health Service, Université Paris-Sud, 94276, Le Kremlin-Bicêtre, France
                Copyright ©2012 Praud et al.; licensee BioMed Central Ltd.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                Research Article

                Veterinary medicine

                specificity, i-elisa, bayesian approach, cft, brucella ovis, sensitivity, diagnostic tests


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