Factors associated with genotype clustering of Mycobacterium tuberculosis isolates in an ethnically diverse region of southern California, United States

Abstract

Mycobacterium tuberculosis (Mtb) isolates with identical genotypes, found in different patients, are most likely the result of recent transmission. Mtb strains with closely related genotypes, called clonal complexes, are most likely derived from one another. We examined Mtb genotypes from southern California TB patients from 2005 through 2008 to complete the first comprehensive molecular epidemiology analysis of this complicated and ethnically diverse region. Mtb genotypes were characterized with spoligo-type and MIRU-12 typing. MIRU-VNTRplus was utilized to assign genotypes to global lineages and complete cluster analyses. Associations between patient characteristics and genotype clustering and clonal complexes were evaluated using logistic regression and frequency analysis. Of 832 Mtb isolates analyzed, 480 (58%) fell into 94 strain clusters. The majority of isolates were identified as being in the EA1 (31%), LAM (17%) and Haarlem (15%) lineages, but 13 different lineages were found in this region. TB patients with clustered isolates were more likely to be homeless (AOR 3.44, 95% CI 1.65, 7.18) and male (AOR 1.57, 95% CI 1.17, 2.10). Of the 480 clustered strains, 388 aggregated into six clonal complexes. Over 45% of reported TB cases were clustered and likely resulted from recent transmission events. Patients with clustered Mtb isolates that were grouped into clonal complexes had unique socio-demographic characteristics. These data suggest that TB is being transmitted in relatively insular community networks defined by race/ethnicity and country of origin. The addition of clonal complex analysis to simple cluster analysis provides important public health insights into the local transmission of TB in ethnically diverse regions with diverse Mtb genotypes.

Neighborhood socioeconomic position and tuberculosis transmission: a retrospective cohort study

“…Both clustered and non-clustered case incidence rates were seen to increase with lower SES quartile, with those patients living in the lowest SEP quartile at measurably higher risk for clustering. The analysis confirmed previous molecular epidemiologic investigations identifying patients of US birth, Hispanic ethnicity, homelessness and higher frequencies of substance use as at greater odds for clustering [3,7,20]. As in previous work, there was less evidence of genotypic clustering among foreign-born persons, and genotyping clusters indicated almost no transmission between US and foreign-born groups [20-22].…”

A Field-Validated Approach Using Surveillance and Genotyping Data to Estimate Tuberculosis Attributable to Recent Transmission in the United States

“…It evaluates the same time frame for a plausible-source case for all cases, regardless of when during the study period the case was diagnosed. Unlike the n – 1 method (8), the commonly used approach to account for source cases (15, 16, 18, 19, 21), our plausible-source case approach does not assume that the first case in the cluster is the source case for every other case in the cluster. Rather, it considers plausible-source cases for each case individually, requiring that a plausible-source case have characteristics associated with infectiousness.…”

“…Three of the approaches, referred to here as geographic approaches, were based on geography and genotyping alone. The geographic units in these approaches have been previously used as the basis for published estimates of RT (14, 19–21). The fourth approach, the plausible-source case approach, is a novel approach we propose that is based on identifying a plausible-source case of transmission based on clinical and demographic factors, the time interval between cases, geography, and genotyping data.…”

“…TB genotyping is frequently used to estimate the proportion of TB cases in a population that are attributable to RT (5, 8, 13–21), as it provides a tool with which to infer transmission connections between cases. Genotype-based methods for estimating RT rely on the fundamental assumption that cases related by RT will have identical genotypes (typically referred to as clustered cases), while cases resulting from the reactivation of remotely acquired infection will have unique genotypes in the population (nonclustered cases).…”

“…As a result, estimates have varied dramatically, even across populations that would be expected to have similar rates of RT (15–19, 21, 26). Multiple factors affect genotype-based RT estimates, including the geographic unit over which clustering is defined (20), the time window of the study (26), and whether or not source cases are taken into account (27).…”