Bayesian modelling of tuberculosis clustering from DNA fingerprint data

Abstract

A combination of continuous and categorical tests, none of which is a gold standard, is often available for classification of subject status in epidemiologic studies. For example, tuberculosis (TB) molecular epidemiology uses select mycobacterial DNA sequences to provide clues about which cases of active TB are likely clustered, implying recent transmission between these cases, versus reactivation of previously acquired infection. The proportion of recently transmitted cases is important to public health, as different control methods are implemented as transmission rates increase. Standard typing methods include IS6110 restriction fragment length polymorphism (IS6110 RFLP), but recently developed polymerase chain reaction based genotyping modalities, including mycobacterial interspersed repetitive unit-variable-number tandem repeat and spoligotyping provide quicker results. In addition, it has recently been suggested that results from IS6110 RFLP can be used to create a continuous measure of genetic relatedness, called the nearest genetic distance. Whichever method is used, estimation of cluster rates is rendered difficult by the lack of a gold standard method for classifying cases as clustered or not. Since many of these methods are relatively new, their properties have not been extensively investigated. Misclassification errors subsequently lead to sub-optimal estimation of risk factors for clustering. Here we show how Bayesian latent class models can be used in such situations, for example to simultaneously analyse Mycobacterium tuberculosis DNA data from all three of the above methods. Using the data collected at the Public Health Unit in Montreal, we estimate the proportion of clustered cases and the operating characteristics of each method using information from all three methods combined, including both continuous and dichotomous measures from IS6110 RFLP. A misclassification-adjusted regression model provides estimates of the effects of risk factors on the clustering probabilities. We also discuss how one must carefully interpret any inferences that arise from a combination of continuous and dichotomous tests.

Proteomics Mapping of Cord Blood Identifies Haptoglobin “Switch-On” Pattern as Biomarker of Early-Onset Neonatal Sepsis in Preterm Newborns

“…antenatal antibiotics, uncultivable bacteria as etiological agents, specimen contamination ex-vivo ) increase the odds for EONS misclassification [37]. Our approach of using LCA was aimed to circumvent this obstacle [33], [77]. We established a diagnostic algorithm that adds two CB biochemical markers, (Hp&HpRP switch pattern and IL-6) to the hematological indices used for presumed EONS.…”

Proteomics/diagnosis of chorioamnionitis and of relationships with the fetal exposome

“…8,66 We knew that with respect to funisitis, which is a more precise pathologic entity, our Hp switch-on algorithm had better accuracy (83%) than with the clinical diagnosis of early onset sepsis (73%). Therefore, we used latent class analysis (LCA), 67 a statistical method that has gained increased acceptance for situations when a perfect gold standard diagnostic test does not exist. 68 This analysis assumes that a hidden (latent) variable (which in our case we named: antenatal exposure to intra-amniotic inflammation) is responsible for heterogeneity among observed variables.…”

Towards Eliminating Bias in Cluster Analysis of TB Genotyped Data

“…Therefore measures of recent transmission that account for genetic heterogeneity and fingerprint pattern change rate need to be developed to ensure that the sample cluster distribution accurately represents the reality in the population [9]. Scott et al [10] investigated and compared three measures – IS 6110 RFLP, both dichotomous and continuous (nearest genetic distance) and PCR-based. They concluded that the poor sensitivity of the standard IS6110 RFLP test leads to estimates of clustering that are likely too low yet IS6110 typing remains the best method, at least in a low-incidence setting where the population of M. tuberculosis isolates shows a high degree of genetic diversity.…”