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      Evaluation of the Performance of Five Diagnostic Tests for Fasciola hepatica Infection in Naturally Infected Cattle Using a Bayesian No Gold Standard Approach

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          Abstract

          The clinical and economic importance of fasciolosis has been recognised for centuries, yet diagnostic tests available for cattle are far from perfect. Test evaluation has mainly been carried out using gold standard approaches or under experimental settings, the limitations of which are well known. In this study, a Bayesian no gold standard approach was used to estimate the diagnostic sensitivity and specificity of five tests for fasciolosis in cattle. These included detailed liver necropsy including gall bladder egg count, faecal egg counting, a commercially available copro-antigen ELISA, an in-house serum excretory/secretory antibody ELISA and routine abattoir liver inspection. In total 619 cattle slaughtered at one of Scotland’s biggest abattoirs were sampled, during three sampling periods spanning summer 2013, winter 2014 and autumn 2014. Test sensitivities and specificities were estimated using an extension of the Hui Walter no gold standard model, where estimates were allowed to vary between seasons if tests were a priori believed to perform differently for any reason. The results of this analysis provide novel information on the performance of these tests in a naturally infected cattle population and at different times of the year where different levels of acute or chronic infection are expected. Accurate estimates of sensitivity and specificity will allow for routine abattoir liver inspection to be used as a tool for monitoring the epidemiology of F. hepatica as well as evaluating herd health planning. Furthermore, the results provide evidence to suggest that the copro-antigen ELISA does not cross-react with Calicophoron daubneyi rumen fluke parasites, while the serum antibody ELISA does.

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          Most cited references29

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          Understanding diagnostic tests 1: sensitivity, specificity and predictive values.

          The usefulness of diagnostic tests, that is their ability to detect a person with disease or exclude a person without disease, is usually described by terms such as sensitivity, specificity, positive predictive value and negative predictive value. In this article, the first of the series, a simple, practical explanation of these concepts is provided and their use and misuse discussed. It is explained that while sensitivity and specificity are important measures of the diagnostic accuracy of a test, they are of no practical use when it comes to helping the clinician estimate the probability of disease in individual patients. Predictive values may be used to estimate probability of disease but both positive predictive value and negative predictive value vary according to disease prevalence. It would therefore be wrong for predictive values determined for one population to be applied to another population with a different prevalence of disease. Sensitivity and specificity are important measures of the diagnostic accuracy of a test but cannot be used to estimate the probability of disease in an individual patient. Positive and negative predictive values provide estimates of probability of disease but both parameters vary according to disease prevalence.
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            Estimating the error rates of diagnostic tests.

            S Hui, S Walter (1980)
            It is often required to evaluate the accuracy of a new diagnostic test against a standard test with unknown error rates. If the two tests are applied simultaneously to the same individuals from two populations with different disease prevalences, then assuming conditional independence of the errors of the two tests, the error rates of both tests and the true prevalences in both populations can be estimated by a maximum likelihood procedure. Generalizations to several tests applied in several populations are also possible.
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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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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                2016
                26 August 2016
                : 11
                : 8
                : e0161621
                Affiliations
                [1 ]The Roslin Institute, University of Edinburgh, Edinburgh, United Kingdom
                [2 ]Royal (Dick) School of Veterinary Studies, University of Edinburgh, Edinburgh, United Kingdom
                [3 ]Farm Animal Clinical Sciences, School of Veterinary Medicine, University of Glasgow, Glasgow, United Kingdom
                University of Texas Medical Branch, UNITED STATES
                Author notes

                Competing Interests: The authors have the following interests: This study was funded by Scotbeef Limited and used samples from Scotbeef. There are no patents, products in development or marketed products based on the results presented in this paper to declare. This does not alter the authors’ adherence to all the PLOS ONE policies on sharing data and materials.

                • Conceived and designed the experiments: SM NS RK BB IH.

                • Performed the experiments: SM NS RK BB.

                • Analyzed the data: SM IH.

                • Contributed reagents/materials/analysis tools: SM NS BB IH.

                • Wrote the paper: SM NS BB IH.

                [¤]

                Current address: The Roslin Institute, The University of Edinburgh, Easter Bush, Midlothian, EH25 9RG, Scotland, United Kingdom

                Author information
                http://orcid.org/0000-0002-4591-8267
                Article
                PONE-D-15-43337
                10.1371/journal.pone.0161621
                5001639
                27564546
                a6b7f4de-6147-46e6-81df-e4f4fe36b445
                © 2016 Mazeri et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 1 October 2015
                : 9 August 2016
                Page count
                Figures: 6, Tables: 2, Pages: 22
                Funding
                Funded by: Scotbeef Limited
                Scotbeef Limited, http://www.scotbeef.com/. BB thanks the BBSRC for their support through the Institute Strategic Programme (BB/J004235/1). The funders have read and agreed to publish this material, but had no role in study design, data collection and analysis, or preparation of the manuscript.
                Categories
                Research Article
                Biology and Life Sciences
                Organisms
                Animals
                Vertebrates
                Amniotes
                Mammals
                Bovines
                Cattle
                Biology and Life Sciences
                Agriculture
                Livestock
                Cattle
                Biology and Life Sciences
                Organisms
                Animals
                Vertebrates
                Amniotes
                Mammals
                Ruminants
                Cattle
                Biology and Life Sciences
                Developmental Biology
                Fibrosis
                Physical Sciences
                Mathematics
                Probability Theory
                Random Variables
                Covariance
                Medicine and Health Sciences
                Parasitic Diseases
                Research and Analysis Methods
                Immunologic Techniques
                Immunoassays
                Enzyme-Linked Immunoassays
                Biology and Life Sciences
                Veterinary Science
                Veterinary Medicine
                Veterinary Diagnostics
                Biology and Life Sciences
                Agriculture
                Animal Products
                Meat
                Biology and Life Sciences
                Nutrition
                Diet
                Food
                Meat
                Medicine and Health Sciences
                Nutrition
                Diet
                Food
                Meat
                Biology and Life Sciences
                Anatomy
                Liver
                Biliary System
                Gallbladder
                Medicine and Health Sciences
                Anatomy
                Liver
                Biliary System
                Gallbladder
                Custom metadata
                All relevant data for the main model shown in the paper are within the paper and its Supporting Information files. Data on location or identification of the farms the animals originated from are confidential and therefore cannot be shared. Nevertheless they are not critical for the main analysis.

                Uncategorized
                Uncategorized

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