Bayesian latent class models for identifying canine visceral leishmaniosis using diagnostic tests in the absence of a gold standard

Abstract

Background: Like many infectious diseases, there is no practical gold standard for diagnosing clinical visceral leishmaniasis (VL). Latent class modeling has been proposed to estimate a latent gold standard for identifying disease. These proposed models for VL have leveraged information from diagnostic tests with dichotomous serological and PCR assays, but have not employed continuous diagnostic test information.

Methods/principal findings: In this paper, we employ Bayesian latent class models to improve the identification of canine visceral leishmaniasis using the dichotomous PCR assay and the Dual Path Platform (DPP) serology test. The DPP test has historically been used as a dichotomous assay, but can also yield numerical information via the DPP reader. Using data collected from a cohort of hunting dogs across the United States, which were identified as having either negative or symptomatic disease, we evaluate the impact of including numerical DPP reader information as a proxy for immune response. We find that inclusion of DPP reader information allows us to illustrate changes in immune response as a function of age.

Conclusions/significance: Utilization of continuous DPP reader information can improve the correct discrimination between individuals that are negative for disease and those with clinical VL. These models provide a promising avenue for diagnostic testing in contexts with multiple, imperfect diagnostic tests. Specifically, they can easily be applied to human visceral leishmaniasis when diagnostic test results are available. Also, appropriate diagnosis of canine visceral leishmaniasis has important consequences for curtailing spread of disease to humans.

Fig 1. Median posterior predictive probability of disease as a function of age for all four combinations of PCR and DPP test results.

We observe a peak in probability of disease at 5, after which dogs often die. Based on this model, those that survive past age 5 (307 of 1309 observations) never attain the same probability of disease. Due to the complex nature of this disease, there is still considerable uncertainty in these estimates; credible intervals are omitted from the graph.

Fig 2. Median posterior predictive probability of disease as a function of age for six combinations of PCR and DPP reader (interval) test results.

We observe a peak in probability of disease at 5, after which dogs often die. Based on this model, those that survive past age 5 attain the same probability of disease around age 10. Due to the complex nature of this disease, there is still considerable uncertainty in these estimates; credible intervals are omitted from the graph.

Fig 3. Median posterior predictive probability (and variability, shaded) of disease as a function of age for six combinations of PCR and DPP reader (interval) test results for simulated data.

Prevalences (0.1–0.4; columns) and sample sizes (1000, 5000, 10000; rows) are varied. Posterior predictive variability decreases for larger sample sizes across all prevalence values.