Related Experiment Video
Updated: Jul 3, 2026
Micro-Colony Forming Unit Assay for Efficacy Evaluation of Vaccines Against Tuberculosis
Published on: July 28, 2023
Improving the estimation of tuberculosis infection prevalence using T-cell-based assay and mixture models
1Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Canada. madhukar.pai@mcgill.ca
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.
