Costs and Consequences of Using Interferon-γ Release Assays for the Diagnosis of Active Tuberculosis in India
Bibliographic record
Abstract
BACKGROUND: There is growing concern that interferon-γ release assays (IGRAs) are being used off-label for the diagnosis of active tuberculosis (TB) disease in many high-burden settings, including India, where the background prevalence of latent TB infection is high. We analyzed the costs and consequences of using IGRAs for the diagnosis of active TB in India from the perspective of the Indian TB control sector. METHODS AND FINDINGS: We constructed a decision analytic model to estimate the incremental cost and effectiveness of IGRAs for the diagnosis of active TB in India. We compared a reference scenario of clinical examination and non-microbiological tests against scenarios in which clinical diagnosis was augmented by the addition of either sputum smear microscopy, IGRA, or Xpert MTB/RIF. We examined costs (in 2013 US dollars) and consequences from the perspective of the Indian healthcare sector. Relative to sputum smear microscopy, use of IGRA for active TB resulted in 23,700 (95% uncertainty range, UR: 3,800 - 38,300) additional true-positive diagnoses, but at the expense of 315,700 (95% UR: 118,300 - 388,400) additional false-positive diagnoses and an incremental cost of US$49.3 million (95% UR: $34.9 - $58.0 million) (2.9 billion Indian Rupees). Relative to Xpert MTB/RIF (including the cost of treatment for drug resistant TB), use of IGRA led to 400 additional TB cases treated (95% UR: [-8,000] - 16,200), 370,600 (95% UR: 252,200 - 441,700) more false-positive diagnoses, 70,400 (95% UR: [-7,900] - 247,200) fewer disability-adjusted life years averted, and US$14.6 million (95%UR: [-$7.2] - $28.7 million) (854 million Indian Rupees) in additional costs. CONCLUSION: Using IGRAs for diagnosis of active TB in a setting like India results in tremendous overtreatment of people without TB, and substantial incremental cost with little gain in health. These results support the policies by WHO and Standards for TB Care in India, which discourage the use of IGRAs for the diagnosis of active TB in India and similar settings.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".