title: Tuberculosis transmission in nontraditional settings: a decision-tree approach.
abstract: BACKGROUND
  Tuberculosis (TB) transmission in nontraditional settings and relationships (non-TSR) often eludes detection by conventional contact investigation and is increasingly common. The U.S.-based National Tuberculosis Genotyping and Surveillance Network collected epidemiologic data and genotyping results of Mycobacterium tuberculosis isolates from 1996 to 2000.
  
  
  METHODS
  In 2003-2004, we determined the number and characteristics of TB patients in non-TSR that were involved in recent transmission, generated a decision tree to profile those patients, and performed a case-control study to identify predictors of being in non-TSR.
  
  
  RESULTS
  Of 10,844 culture-positive reported TB cases that were genotyped, 4724 (43.6%) M. tuberculosis isolates were clustered with at least one other isolate. Among these, 520 (11%) had epidemiologic linkages discovered during conventional contact investigation or cluster investigation and confirmed by genotyping results. The decision tree identified race/ethnicity (non-Hispanic white or black) as having the greatest predictive ability to determine patients in non-TSR, followed by being aged 15 to 24 years and having positive or unknown HIV infection status. From the 520, 85 (16.4%) had non-TSR, and 435 (83.6%) had traditional settings and relationships (TSR). In multivariate analyses, patients in non-TSR were significantly more likely than those in TSR to be non-Hispanic white (adjusted odds ratio [aOR]=6.1; 95% confidence interval [CI]=1.7-21.1]) or to have an M. tuberculosis isolate resistant to rifampin (aOR=5.2; 95% CI=1.5-17.7).
  
  
  CONCLUSIONS
  Decision-tree analyses can be used to enhance both the efficiency and effectiveness of TB prevention and control activities in identifying patients in non-TSR.
authors.affiliation: Division of Tuberculosis Elimination, National Center for HIV, STD, and TB Prevention, Centers for Disease Control and Prevention, Atlanta, GA 30333, USA. fzk3@cdc.gov
authors.forename: J Steve
authors.initials: JS
authors.lastname: Kammerer
authors.forename: Scott J N
authors.initials: SJ
authors.lastname: McNabb
authors.forename: Jose E
authors.initials: JE
authors.lastname: Becerra
authors.forename: Lisa
authors.initials: L
authors.lastname: Rosenblum
authors.forename: Nong
authors.initials: N
authors.lastname: Shang
authors.forename: Michael F
authors.initials: MF
authors.lastname: Iademarco
authors.forename: Thomas R
authors.initials: TR
authors.lastname: Navin
mesh_terms: D000293:Adolescent; D000328:Adult; D000367:Age Factors; D000368:Aged; D016022:Case-Control Studies; D002648:Child; D002675:Child, Preschool; D015897:Comorbidity; D003663:Decision Trees; D018562:Disease Transmission, Infectious / Q000706:statistics & numerical data*; D005260:Female; D015658:HIV Infections / Q000453:epidemiology; D006801:Humans; D007223:Infant; D007231:Infant, Newborn; D008297:Male; D008875:Middle Aged; D015999:Multivariate Analysis; D009169:Mycobacterium tuberculosis / Q000302:isolation & purification; D016017:Odds Ratio; D044469:Racial Groups / Q000706:statistics & numerical data; D012044:Regression Analysis; D018570:Risk Assessment / Q000379:methods; D012307:Risk Factors; D014376:Tuberculosis / Q000453:epidemiology* / Q000382:microbiology / Q000635:transmission*; D014481:United States / Q000453:epidemiology