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Using Routinely Reported Tuberculosis Genotyping and Surveillance Data to Predict Tuberculosis Outbreaks

Overview of attention for article published in PLOS ONE, November 2012
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Title
Using Routinely Reported Tuberculosis Genotyping and Surveillance Data to Predict Tuberculosis Outbreaks
Published in
PLOS ONE, November 2012
DOI 10.1371/journal.pone.0048754
Pubmed ID
Authors

Sandy P. Althomsons, J. Steven Kammerer, Nong Shang, Thomas R. Navin

Abstract

We combined routinely reported tuberculosis (TB) patient characteristics with genotyping data and measures of geospatial concentration to predict which small clusters (i.e., consisting of only 3 TB patients) in the United States were most likely to become outbreaks of at least 6 TB cases. Of 146 clusters analyzed, 16 (11.0%) grew into outbreaks. Clusters most likely to become outbreaks were those in which at least 1 of the first 3 patients reported homelessness or excess alcohol or illicit drug use or was incarcerated at the time of TB diagnosis and in which the cluster grew rapidly (i.e., the third case was diagnosed within 5.3 months of the first case). Of 17 clusters with these characteristics and therefore considered high risk, 9 (53%) became outbreaks. This retrospective cohort analysis of clusters in the United States suggests that routinely reported data may identify small clusters that are likely to become outbreaks and which are therefore candidates for intensified contact investigations.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 93 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 2%
Japan 1 1%
Guadeloupe 1 1%
Unknown 89 96%

Demographic breakdown

Readers by professional status Count As %
Student > Master 20 22%
Researcher 13 14%
Student > Bachelor 8 9%
Student > Ph. D. Student 8 9%
Professor 5 5%
Other 13 14%
Unknown 26 28%
Readers by discipline Count As %
Medicine and Dentistry 25 27%
Agricultural and Biological Sciences 14 15%
Social Sciences 7 8%
Nursing and Health Professions 4 4%
Biochemistry, Genetics and Molecular Biology 3 3%
Other 10 11%
Unknown 30 32%