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Latent-class methods to evaluate diagnostics tests for Echinococcus infections in dogs.

Sonja Hartnack1, Christine M Budke, Philip S Craig

  • 1Section of Veterinary Epidemiology, Vetsuisse Faculty, University of Zurich, Zurich, Switzerland. Sonja.Hartnack@access.uzh.ch

Plos Neglected Tropical Diseases
|March 6, 2013
PubMed
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Diagnosing canine echinococcosis is challenging without a perfect gold standard. Latent-class analysis, incorporating diagnostic uncertainty and covariates like Taenia co-infection, offers a more robust epidemiological approach.

Area of Science:

  • Veterinary Parasitology
  • Epidemiology
  • Diagnostic Accuracy

Background:

  • Canine echinococcosis diagnosis is difficult in surveillance due to the lack of a routine gold standard test.
  • Latent-class analysis can address challenges posed by unknown test specificities and sensitivities.

Purpose of the Study:

  • To evaluate the diagnostic performance of copro-antigen ELISA, copro-PCR, and arecoline purgation for canine echinococcosis.
  • To explore the impact of covariates such as Taenia co-infection, age, and sex on diagnostic results.
  • To apply Bayesian latent-class modeling to overcome diagnostic uncertainty.

Main Methods:

  • Utilized fecal and purge samples from a previous canine echinococcosis study on the Tibetan plateau.
  • Performed additional diagnostic tests: copro-antigen ELISA and copro-PCR on fecal samples.

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  • Employed Bayesian latent-class analysis to assess diagnostic accuracy and explore covariate effects.

    • Main Results:

    • Co-infection with Taenia spp. was identified as a significant covariate for Echinococcus infection.
    • Copro-PCR showed high estimated sensitivities (84-89%) and specificities (83-93%) for E. multilocularis and E. granulosus.
    • Copro-antigen ELISA had lower sensitivities (55-57%) and specificities (69-71%), while arecoline purgation showed sensitivities of 76-85%.

    Conclusions:

    • Incorporating diagnostic uncertainty and covariates into epidemiological analysis provides more accurate results than deterministic approaches.
    • Latent-class analysis is recommended for canine echinococcosis surveillance when a perfect gold standard is unavailable.
    • This approach enhances the reliability of epidemiological findings in parasite control programs.