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

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.