Literature DB >> 16641308

Which patients' factors predict the rate of growth of Mycobacterium tuberculosis clusters in an urban community?

Cynthia R Driver1, Michelle Macaraig, Peter D McElroy, Carla Clark, Sonal S Munsiff, Barry Kreiswirth, Jeffrey Driscoll, Benyang Zhao.   

Abstract

Factors influencing tuberculosis cluster growth are poorly understood. The authors examined clusters of two or more culture-confirmed Mycobacterium tuberculosis cases between January 1, 2001, and December 31, 2003, that had insertion sequence 6110 (IS6110) restriction fragment length polymorphism and spoligotype patterns identical to those of another study case. Genotypes first seen in New York, New York, before or during 1993 were considered historical; recent strains were those first seen after 1993. The authors examined the effect of the combined characteristics of infectiousness of the first two cases in a cluster on the rate of cluster growth. Genotyping was performed for 2,408 (91.8%) of the 2,623 tuberculosis cases diagnosed; 873 cases were in 212 clusters. Thirty-one clusters had historical strains, 153 were recent, and 28 were of unknown period. Patients' infectiousness was not associated with the rate of cluster growth among historical strain clusters. Among recent strain clusters, infectiousness of both of the initial cases was associated with a higher rate of cluster growth compared with clusters in which neither initial case was infectious, upon adjustment for male sex (rate ratio = 2.62, 95% confidence interval: 1.19, 5.78). The rate of genotype cluster growth should be monitored regardless of how long the strain has been present in the community. However, infectiousness in the first two cases may be useful to prioritize genotype cluster investigations.

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Year:  2006        PMID: 16641308     DOI: 10.1093/aje/kwj153

Source DB:  PubMed          Journal:  Am J Epidemiol        ISSN: 0002-9262            Impact factor:   4.897


  9 in total

1.  Characterizing tuberculosis genotype clusters along the United States-Mexico border.

Authors:  B J Baker; P K Moonan
Journal:  Int J Tuberc Lung Dis       Date:  2014-03       Impact factor: 2.373

2.  Using tuberculosis patient characteristics to predict future cases with matching genotype results.

Authors:  J E Oeltmann; E S Click; P K Moonan
Journal:  Public Health Action       Date:  2014-03-21

3.  Counting the homeless: a previously incalculable tuberculosis risk and its social determinants.

Authors:  Marsha L Feske; Larry D Teeter; James M Musser; Edward A Graviss
Journal:  Am J Public Health       Date:  2013-03-14       Impact factor: 9.308

4.  RDRio Mycobacterium tuberculosis infection is associated with a higher frequency of cavitary pulmonary disease.

Authors:  Luiz Claudio Oliveira Lazzarini; Silvana Miranda Spindola; Heejung Bang; Andrea L Gibson; Scott Weisenberg; Wania da Silva Carvalho; Claudio José Augusto; Richard C Huard; Afrânio L Kritski; John L Ho
Journal:  J Clin Microbiol       Date:  2008-05-07       Impact factor: 5.948

5.  Using routinely reported tuberculosis genotyping and surveillance data to predict tuberculosis outbreaks.

Authors:  Sandy P Althomsons; J Steven Kammerer; Nong Shang; Thomas R Navin
Journal:  PLoS One       Date:  2012-11-07       Impact factor: 3.240

6.  A joint cross-border investigation of a cluster of multidrug-resistant tuberculosis in Austria, Romania and Germany in 2014 using classic, genotyping and whole genome sequencing methods: lessons learnt.

Authors:  Lena Fiebig; Thomas A Kohl; Odette Popovici; Margarita Mühlenfeld; Alexander Indra; Daniela Homorodean; Domnica Chiotan; Elvira Richter; Sabine Rüsch-Gerdes; Beatrix Schmidgruber; Patrick Beckert; Barbara Hauer; Stefan Niemann; Franz Allerberger; Walter Haas
Journal:  Euro Surveill       Date:  2017-01-12

7.  Transmission of tuberculosis and predictors of large clusters within three years in an urban setting in Tokyo, Japan: a population-based molecular epidemiological study.

Authors:  Kiyohiko Izumi; Yoshiro Murase; Kazuhiro Uchimura; Aya Kaebeta; Keiko Ishihara; Sumi Kaguraoka; Takemasa Takii; Akihiro Ohkado
Journal:  BMJ Open       Date:  2019-05-09       Impact factor: 2.692

8.  Geographic prediction of tuberculosis clusters in Fukuoka, Japan, using the space-time scan statistic.

Authors:  Daisuke Onozuka; Akihito Hagihara
Journal:  BMC Infect Dis       Date:  2007-04-11       Impact factor: 3.090

9.  A pulmonary tuberculosis outbreak in a long-term care facility.

Authors:  C-C Lai; Y-C Hsieh; Y-P Yeh; R-W Jou; J-T Wang; S-L Pan; H-H Chen
Journal:  Epidemiol Infect       Date:  2015-11-23       Impact factor: 2.451

  9 in total

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