Open AccessUsing Routinely Reported Tuberculosis Genotyping and Surveillance Data to Predict Tuberculosis Outbreaks
Author(s) -
Sandy Althomsons,
J. Steve Kammerer,
g Shang,
Thomas R. Navin
Publication year - 2012
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0048754
Subject(s) - outbreak , genotyping , tuberculosis , medicine , cluster (spacecraft) , extensively drug resistant tuberculosis , retrospective cohort study , cohort , environmental health , virology , mycobacterium tuberculosis , genotype , biology , pathology , biochemistry , computer science , gene , programming language
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.