title: Use of simulated sputum specimens to estimate the specificity of laboratory-diagnosed tuberculosis.
abstract: METHODS
  Cross-contamination is not uncommon in mycobacteriology laboratories of high-income countries, as documented by bacterial genotyping. The extent of this problem in low-income countries is largely unknown, where this method is impractical.
  
  
  OBJECTIVE
  To estimate the rate of cross-contamination in a high-volume tuberculosis (TB) laboratory in South Africa.
  
  
  METHODS
  Simulated sputum specimens labelled with false names were sent from a TB clinic, interspersed with patient samples, and processed for culture and microscopy. Results were interpreted in the context of the observed proportion of samples with positive microscopy and culture results.
  
  
  RESULTS
  With microscopy, 6/190 (3.2%) simulated specimens were positive (estimated specificity = 96.8%). Considering the 881 positive microscopy results in 6093 clinical samples, we extrapolate that 19.3% (95%CI 7.0-42.8) of positive smears were false-positives. On culture, 2/190 (1.1%) of the simulated specimens were positive for Mycobacterium tuberculosis (estimated specificity = 98.9%). Considering the 1862 positive cultures from 6093 clinical samples, we estimate that 2.4% (95%CI 0.3-8.8) of positive cultures were false-positives.
  
  
  CONCLUSIONS
  Simulated specimens offer a simple means of estimating the proportion of false-positive results, providing information on all sources of potential error from the clinic, through the laboratory and to reporting of results.
authors.affiliation: Département de Microbiologie et Immunologie, Centre Hospitalier Universitaire Sainte-Justine, Université de Montréal, Montréal, Québec, Canada.
authors.forename: A-M
authors.initials: AM
authors.lastname: Demers
authors.forename: A
authors.initials: A
authors.lastname: Boulle
authors.forename: R
authors.initials: R
authors.lastname: Warren
authors.forename: S
authors.initials: S
authors.lastname: Verver
authors.forename: P
authors.initials: P
authors.lastname: van Helden
authors.forename: M A
authors.initials: MA
authors.lastname: Behr
authors.forename: D
authors.initials: D
authors.lastname: Coetzee
mesh_terms: D000293:Adolescent; D003198:Computer Simulation / Q000706:statistics & numerical data*; D003937:Diagnosis, Differential; D005189:False Positive Reactions; D006801:Humans; D015994:Incidence; D009169:Mycobacterium tuberculosis / Q000302:isolation & purification*; D011785:Quality Assurance, Health Care; D012680:Sensitivity and Specificity; D013019:South Africa / Q000453:epidemiology; D013183:Sputum / Q000166:cytology / Q000382:microbiology*; D014376:Tuberculosis / Q000175:diagnosis* / Q000453:epidemiology / Q000382:microbiology