Latent Class Models for Echinococcus Multilocularis Diagnosis in Foxes in Switzerland in the Absence of a Gold Standard

Background: In 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.

Results: The 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.

Conclusions: Adoption 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.