Determinants of cluster distribution in the molecular epidemiology of tuberculosis

Abstract

Recently developed molecular techniques have revolutionized the epidemiology of tuberculosis. Multiple studies have used these tools to examine the population structure of Mycobacterium tuberculosis isolates in different communities. The distributions of clusters of M. tuberculosis isolates in these settings may variously reflect social mixing patterns or the differential fitness of specific clones of the organism. We developed an individual-based microsimulation of tuberculosis transmission to explore social and demographic determinants of cluster distribution and to observe the effect of transmission dynamics on the empiric data from molecular epidemiologic studies. Our results demonstrate that multiple hostrelated factors contribute to wide variation in cluster distributions even when all strains of the organism are assumed to be equally transmissible. These host characteristics include interventions such as chemotherapy, vaccination and chemoprophylaxis, HIV prevalence, the age structure of the population, and the prevalence of latent tuberculosis infection. We consider the implications of these results for the interpretation of cluster studies of M. tuberculosis as well as the more general application of microsimulation models to infectious disease epidemiology.O ver the past 10 years, molecular tools have become available that have changed the way that epidemiologists study the transmission of infectious disease (1). In addition to their role in detecting unsuspected transmission links (2-4), molecular markers are increasingly being used to study transmission patterns within populations and to evaluate host-and strain-specific risk factors for disease spread (5, 6). Nowhere has this approach been used more rigorously than in the pioneering work on the molecular epidemiology of tuberculosis (TB). Since the development of standardized methods for DNA fingerprinting of Mycobacterium tuberculosis, molecular techniques have been used to estimate the fraction of cases attributable to recent transmission of M. tuberculosis (7-14), identify host-specific risk factors for disease spread (15-18), document exogenous reinfection (19-21), and study patterns of drug resistance (22)(23)(24). Investigators also have begun to use these methods to explore potential strain-specific differences in bacterial phenotypes such as tissue tropism, virulence, and transmissibility (25,26).This research has shown that the genetic diversity of M. tuberculosis isolates from different human communities can vary considerably (27,28). Clusters of identical isolates are assumed to share DNA fingerprints as a result of the spread of the organism among the human hosts who harbor the isolates in the cluster. Patients with TB whose isolates cannot be grouped into clusters, i.e., those with unique DNA fingerprints, are assumed to have disease that results from the reactivation of latent infection acquired in the past. Variation in the distribution of clusters of M. tuberculosis isolates in different communities is thought to reflect different TB...

Methodological and Clinical Aspects of the Molecular Epidemiology of Mycobacterium tuberculosis and Other Mycobacteria

“…Patterns of TB transmission differ greatly between settings, as they reflect an interplay of various factors, including existing TB control strategies, endemicity, population density, subpopulations with lower immunity to TB, migration patterns, geography, demography, and transmissibility of locally relevant M. tuberculosis complex strains (433). Genotyping data have given important clues about risk factors for TB transmission.…”

Molecular Epidemiology of Tuberculosis: Current Insights

“…That is, if strains of M. tuberculosis that bear specific properties enhance their intrinsic transmissibility, the identification of such strains in populations may warrant additional control measures. Some studies have investigated fitness in terms of estimating the average number of secondary infections or secondary cases as the transmissibility of a particular strain (or genotype) in a susceptible population (44,185). In estimating epidemic potentials, parameter estimates are heavily dependent on empirical epidemiologic data from specific human populations, such as molecular and/or epidemiologic cluster investigations and model-based estimates of average fitness (33,34,57).…”

“…Although molecular clustering may approximate epidemiologic clustering or recent transmission in low-incidence populations, the approximations tend to be more divergent in high-incidence populations due to high rates of infection and multiple transmission pathways. The precise proportion of disease due to recent transmission or endogenous reactivation is variable and heavily dependent on a number of factors, including the annual rate of TB infection, the molecular method employed, effective TB control programs, the size of the infected pool of individuals, age cohort effects (275), immigration history (109,155), population susceptibility (e.g., genetic susceptibility, HIV prevalence, BCG vaccination), and the sampling strategies employed to derive estimates (114,185,186). Thus, when studies account for these independent factors and are used in conjunction with conventional epidemiologic methods, greater resolution and insight into transmission dynamics can be gleaned for the specific communities or populations studied.…”

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