Abstract
BackgroundIn Europe the principal definitive host for Echinococcus multilocularis, causing alveolar echinococcosis in humans, is the red fox (Vulpes vulpes). Obtaining reliable estimates of the prevalence of E. multilocularis and relevant risk factors for infection in foxes can be difficult if diagnostic tests with unknown test accuracies are used. Latent-class analysis can be used to obtain estimates of diagnostic test sensitivities and specificities in the absence of a perfect gold standard. Samples from 300 foxes in Switzerland were assessed by four different diagnostic tests including necropsy followed by sedimentation and counting technique (SCT), an egg-PCR, a monoclonal and a polyclonal copro-antigen ELISA. Information on sex, age and presence of other cestode species was assessed as potential covariates in the Bayesian latent class models. Different Bayesian latent-class models were run, considering dichotomized test results and, additionally, continuous readings resulting in empirical ROC curves.ResultsThe model without covariates estimated a true parasite prevalence of 59.5% (95% CI: 43.1–66.4%). SCT, assuming a specificity of 100%, performed best among the four tests with a sensitivity of 88.5% (95% CI: 82.7–93.4%). The egg-PCR showed a specificity of 93.4% (95% CI: 87.3–99.1%), although its sensitivity of 54.8% was found moderately low (95% CI: 48.5–61.0%). Relatively higher sensitivity (63.2%, 95% CI: 55.3–70.8%) and specificity (70.0%, 95% CI: 60.1–79.4%) were estimated for the monoclonal ELISA compared to the polyclonal ELISA with a sensitivity and specificity of 56.0% (95% CI: 48.0–63.9%) and 65.9% (95% CI: 55.8–75.6%), respectively. In the Bayesian models, adult foxes were found to be less likely infected than juveniles. Foxes with a concomitant cestode infection had double the odds of an E. multilocularis infection. ROC curves following a Bayesian approach enabled the empirical determination of the best cut-off point. While varying the cut-offs of both ELISAs, sensitivity and specificity of the egg-PCR and SCT remained constant in the Bayesian latent class models.ConclusionsAdoption of a Bayesian latent class approach helps to overcome the absence of a perfectly accurate diagnostic test and gives a more reliable indication of the test performance and the impact of covariates on the prevalence adjusted for diagnostic uncertainty.
Highlights
In Europe the principal definitive host for Echinococcus multilocularis, causing alveolar echinococcosis in humans, is the red fox (Vulpes vulpes)
The addition of the covariate “cestodes” brought the largest improvement in deviance information criterion (DIC) and suggested that foxes with a concomitant cestode infection had double the odds of presenting E. multilocularis compared to foxes without it
The model including the covariate “age” experienced a less remarkable improvement in DIC and implied that adult foxes were less likely to be infected with E. multilocularis compared to younger animals
Summary
In Europe the principal definitive host for Echinococcus multilocularis, causing alveolar echinococcosis in humans, is the red fox (Vulpes vulpes). Three of the diagnostic techniques frequently used for E. multilocularis detection in the definitive host include the visual identification of adult worms in the small intestine at necropsy through the sedimentation and counting technique (SCT), the parasite coproantigen detection and the amplification of DNA from parasitic eggs present in the fox faeces [6] The performance of these tests, for a given population, are commonly measured based on their diagnostic sensitivity and specificity. Since the first publication of this technique for E. multilocularis diagnosis [22] different approaches have been developed to improve its performance on faeces [23,24,25,26,27,28,29,30,31] This method is highly specific, but low worm burdens and the presence of inhibitory components may compromise its sensitivity [29, 32]. Prevalence studies in foxes rely on imperfect diagnostic methods and these limitations in tests’ accuracies should be taken into account when reporting and interpreting their results [6]
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
