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Evaluation using latent class models of the diagnostic performances of three ELISA tests commercialized for the serological diagnosis of Coxiella burnetii infection in domestic ruminants
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Edité par CCSD -
International audience. Q fever is a worldwide zoonotic disease mainly responsible for reproductive disorders such as abortion in domestic ruminants. ELISA methods are the diagnostic tools recommended for the serological diagnosis of Coxiella burnetii infection in ruminants but their respective diagnostic performances are difficult to assess because of the absence of a gold standard. ObjectivesThis study focused on three commercial ELISA tests with the following objectives (1) assess their sensitivity and specificity in sheep, goats and cattle, (2) assess the between- and within-herd seroprevalence distribution in these species, accounting for diagnostic errors, and (3) estimate optimal sample sizes considering sensitivity and specificity at herd level.Materials and methodsWe comparatively tested 1,413 cattle, 1,474 goat and 1,432 sheep serum samples collected in France. We analyzed the cross-classified test results with a hierarchical zero-inflated beta-binomial latent class model considering each herd as a population and conditional dependence as a fixed effect. Potential biases and coverage probabilities of the model were assessed by simulation.ResultsConditional dependence for truly seropositive animals was high in all species for two of the three ELISA methods. Specificity estimates were high, ranging from 94.8% [92.1;97.8] to 99.2% [98.5;99.7], whereas sensitivity estimates were generally low, ranging from 39.3 [30.7;47.0] to 90.5% [83.3;93.8]. Between- and within-herd seroprevalence estimates varied greatly among geographic areas and herds. Overall, goats showed higher within-herd seroprevalence levels than sheep and cattle. The optimal sample size maximizing both herd sensitivity and herd specificity varied from 3 to at least 20 animals depending on the test and ruminant species. ConclusionThis study provides better interpretation of three widely used commercial ELISA tests and will make it possible to optimize their implementation in future studies.
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