Abstract
High resolution tests for genetic variation reveal that individuals may simultaneously host more than one distinct strain of Mycobacterium tuberculosis. Previous studies find that this phenomenon, which we will refer to as “mixed infection”, may affect the outcomes of treatment for infected individuals and may influence the impact of population-level interventions against tuberculosis. In areas where the incidence of TB is high, mixed infections have been found in nearly 20% of patients; these studies may underestimate the actual prevalence of mixed infection given that tests may not be sufficiently sensitive for detecting minority strains. Specific reasons for failing to detect mixed infections would include low initial numbers of minority strain cells in sputum, stochastic growth in culture and the physical division of initial samples into parts (typically only one of which is genotyped). In this paper, we develop a mathematical framework that models the study designs aimed to detect mixed infections. Using both a deterministic and a stochastic approach, we obtain posterior estimates of the prevalence of mixed infection. We find that the posterior estimate of the prevalence of mixed infection may be substantially higher than the fraction of cases in which it is detected. We characterize this bias in terms of the sensitivity of the genotyping method and the relative growth rates and initial population sizes of the different strains collected in sputum.
Highlights
Tools for the genetic analysis of Mycobacterium tuberculosis, the causative agent of human tuberculosis (TB), have fundamentally altered our understanding of the natural history of this pathogen
Specimen handling: The mathematical framework developed is based on three assumptions regarding the handling protocol and the specimen – i.e. the sputum sample – from an individual: 1. Each specimen contains at least one strain of M. tuberculosis, and may contain more
When hosts with mixed infection consistently have a good representation of minority types in their sputum, there are fewer false negatives, m is higher, and the posterior estimate of ρ is closer to the fraction of cases in whom we detect mixed infection
Summary
Tools for the genetic analysis of Mycobacterium tuberculosis, the causative agent of human tuberculosis (TB), have fundamentally altered our understanding of the natural history of this pathogen. The advent of high resolution tests for genetic variation has revealed that individuals may simultaneously harbor infections with more than one distinct strain of M. tuberculosis (Warren et al, 1999; Sola et al, 2003; Kremer et al, 1999; van Embden et al, 1993; Imaeda, 1985) This phenomenon, which we will refer to as “mixed infection”, has been linked with poor treatment outcome when the co-infecting strains differ with respect to drug susceptibility (van Rie et al, 2005; Hingley-Wilson et al, 2013) and is predicted to influence the impact of population-level interventions against tuberculosis (Cohen et al, 2008; Rodrigues et al, 2007; Colijn et al, 2009; Sergeev et al, 2011; Mills et al, 2013). As discussed in detail in a recent review (Cohen et al, 2012), there are many opportunities to fail to detect a mixed infection that is present in a host, because a minority strain might not be harvested in the collected clinical specimen, might be lost during the process of specimen transport and handling, and might fail to be detected by the particular genotyping method employed
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