Improving the estimation of tuberculosis infection prevalence using T-cell-based assay and mixture models

Int J Tuberc Lung Dis. 2008 Aug;12(8):895-902.


Background: The prevalence of latent tuberculosis infection (LTBI) is traditionally estimated using the tuberculin skin test (TST). Highly specific blood-based interferon-gamma release assays (IGRAs) are now available and could enhance the estimation of LTBI prevalence in combination with model-based methods.

Design: We compared conventional and model-based methods for estimating LTBI prevalence among 719 Indian health care workers who underwent both TST and QuantiFERON-TB Gold In-Tube (QFT-G). In addition to using standard cut-off points on TST and QFT-G, Bayesian mixture model analyses were performed with: 1) continuous TST data and 2) categorical data using both TST and QFT-G results in a latent class analysis (LCA), accounting for prior information on sensitivity and specificity.

Results: Estimates of LTBI prevalence varied from 33.8% to 60.7%, depending on the method used. The mixture model based on TST alone estimated the prevalence at 36.5% (95%CI 28.5-47.0). When results from both tests were combined using LCA, the prevalence was 45.4% (95%CI 39.5-51.1). The LCA provided additional results on the sensitivity, specificity and predictive values of joint results.

Conclusion: The availability of novel, specific IGRAs and development of methods such as mixture analyses allow a more realistic and informative approach to prevalence estimation.

Publication types

  • Comparative Study
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Bayes Theorem
  • Humans
  • Interferon-gamma / immunology
  • Models, Theoretical
  • Prevalence
  • Sensitivity and Specificity
  • T-Lymphocytes / immunology*
  • Tuberculin Test
  • Tuberculosis / epidemiology*


  • Interferon-gamma