Networks and tuberculosis: an undetected community outbreak involving public places

Klovdahl, Alden S.; Graviss, Edward A.; Yaganehdoost, Afshin; Ross, Michael W.; Wanger, Audrey; Adams, Gerald J.; Musser, James M.

doi:10.1016/s0277-9536(00)00170-2

Cited by 187 publications

(99 citation statements)

References 50 publications

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“…Of note, in these data, primary MDR-TB cases were significantly younger than DS-TB cases, which could indicate social networks of younger primary MDR-TB cases or venues frequented by younger persons in the community. 23,24 In addition, biological, social and environmental factors could also explain the clustering of primary MDR-TB around Clinic X, although further data confirming these hypotheses are needed. 25,26 While detailed strain typing was unavailable, the strain virulence could be a consideration, as could host characteristics of the population in the area.…”

Section: Discussionmentioning

confidence: 99%

Geographic predictors of primary multidrug-resistant tuberculosis cases in an endemic area of Lima, Peru

Shah¹,

Choi²,

Berrang‐Ford³

et al. 2014

int j tuberc lung dis

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Section: Discussionmentioning

confidence: 99%

Geographic predictors of primary multidrug-resistant tuberculosis cases in an endemic area of Lima, Peru

Shah¹,

Choi²,

Berrang‐Ford³

et al. 2014

int j tuberc lung dis

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“…Numerous recent studies have documented the unsuspected spread of M. tuberculosis strains through diverse communities in lowincidence areas in the U.S. and have called attention to the inadequacy of standard contact-tracing techniques in identifying potential transmission events (34)(35)(36)(37)(38). Many of these communities focus on targeted testing and treatment of latent TB infection as a means of TB control (52).…”

Section: Discussionmentioning

confidence: 99%

“…Multiple molecular studies of Neisseria meningitides, for example, have used this approach to characterize pandemic strains that were shown to be genetically distinct from the strains isolated in sporadic cases of meningitis (33). Several recent epidemiologic studies of TB have focused on large clusters of cases caused by a single genotype or strain of M. tuberculosis (34)(35)(36)(37)(38). In some cases, these outbreaks were attributed to an increased capacity for transmission and͞or replication by the specific strain (37).…”

mentioning

confidence: 99%

“…In some cases, these outbreaks were attributed to an increased capacity for transmission and͞or replication by the specific strain (37). Other studies described highly mobile or complex social networks that may have facilitated the prolific spread of the organism (34)(35)(36)38).In practice, it can be difficult to distinguish between the effects of variability in strain behavior and the often complex transmission dynamics of TB in the analysis of differential cluster size. One way to address this problem is to consider the range of cluster sizes that might be expected in the absence of strainspecific variation in transmissibility, i.e., to establish an expectation of cluster distributions consistent with the null hypothesis that all strains are equally transmissible.…”

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confidence: 99%

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confidence: 99%

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Determinants of cluster distribution in the molecular epidemiology of tuberculosis

Murray

2002

Proc. Natl. Acad. Sci. U.S.A.

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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...

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Contact Investigations and the Continued Commitment to Control Tuberculosis

Mohle-Boetani¹

2002

JAMA

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Networks and tuberculosis: an undetected community outbreak involving public places

Cited by 187 publications

References 50 publications

Geographic predictors of primary multidrug-resistant tuberculosis cases in an endemic area of Lima, Peru

Geographic predictors of primary multidrug-resistant tuberculosis cases in an endemic area of Lima, Peru

Determinants of cluster distribution in the molecular epidemiology of tuberculosis

Contact Investigations and the Continued Commitment to Control Tuberculosis

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