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Tuberculosis, more commonly referred to as TB, is an infectious disease stemming from Mycobacterium tuberculosis. While it primarily impacts the lungs, TB can also affect other body areas. Given its severity and global impact, timely and accurate diagnosis is crucial for controlling its spread and improving patient outcomes.
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Trade-offs between cost and accuracy in active case finding for tuberculosis: A dynamic modelling analysis.

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Area of Science:

  • Public Health
  • Infectious Disease Epidemiology
  • Health Economics

Background:

  • Active case finding (ACF) is crucial for tuberculosis (TB) control, but optimal implementation strategies, especially diagnostic test selection, remain debated.
  • Smear microscopy is inexpensive but has limited sensitivity, potentially missing many TB cases, while more sensitive tests like Xpert MTB/RIF are costlier.

Purpose of the Study:

  • To compare the cost-effectiveness and impact of moderate-accuracy versus high-accuracy diagnostic tests within an active case finding strategy for tuberculosis in a high-burden urban slum setting.
  • To evaluate the influence of test accuracy, particularly specificity, and the benefit of detecting rifampicin resistance on the overall impact and cost of TB ACF.

Main Methods:

  • A dynamic transmission model of TB was developed and calibrated to an urban slum population in India.
  • The model simulated two ACF approaches: one using a moderate-accuracy, lower-cost test (microscopy-like) and another using a high-accuracy, higher-cost test (Xpert-like) with rifampicin resistance detection.

Main Results:

  • High-accuracy testing in ACF was projected to avert more TB cases per unit cost compared to moderate-accuracy testing, even with a higher initial test price.
  • Test specificity was a critical driver; high-accuracy tests were significantly more impactful if their specificity was at least 3 percentage points higher.
  • The ability of high-accuracy tests to detect rifampicin resistance offered long-term cost savings and contributed to overall case reduction.

Conclusions:

  • Cheaper diagnostics do not automatically lead to less costly active case finding; savings can be negated by false positives, lower sensitivity, and missed opportunities for cost savings in second-line treatments.
  • Designing cost-effective TB ACF strategies in resource-limited settings requires careful consideration of all factors, including test accuracy, downstream treatment implications, and resistance detection capabilities.