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

Marie V Ozanne; Grant D Brown; Breanna M Scorza; Kurayi Mahachi; Angela J Toepp; Christine A Petersen

doi:10.1371/journal.pntd.0010236

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

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

Marie V Ozanne, Grant D Brown, Breanna M Scorza, Kurayi Mahachi, Angela J Toepp and Christine A Petersen

PLoS neglected tropical diseases, Vol.16(3), pp.e0010236-e0010236

03/14/2022

DOI: 10.1371/journal.pntd.0010236

PMCID: PMC8947804

PMID: 35286301

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Abstract

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. 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. 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.

Details

Title: Subtitle: Bayesian latent class models for identifying canine visceral leishmaniosis using diagnostic tests in the absence of a gold standard
Creators: Marie V Ozanne - Mount Holyoke College
Grant D Brown - University of Iowa
Breanna M Scorza - University of Iowa
Kurayi Mahachi - University of Iowa
Angela J Toepp - Eastern Virginia Medical School
Christine A Petersen - University of Iowa
Resource Type: Journal article
Publication Details: PLoS neglected tropical diseases, Vol.16(3), pp.e0010236-e0010236
DOI: 10.1371/journal.pntd.0010236
PMID: 35286301
PMCID: PMC8947804
NLM abbreviation: PLoS Negl Trop Dis
ISSN: 1935-2727
eISSN: 1935-2735
Grant note: DOI: 10.13039/100000061, name: Fogarty International Center, award: R01TW010500; DOI: 10.13039/100000060, name: National Institute of Allergy and Infectious Diseases, award: T32AI007260
Language: English
Date published: 03/14/2022
Academic Unit: Epidemiology; Biostatistics; Internal Medicine
Record Identifier: 9984228794602771

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