Cost-effectiveness of diagnostic technologies for mycobacterium tuberculosis infection in India and Brazil
Bibliographic record
Abstract
The economic value of new skin-based tests and blood-based interferon-γ release assays (IGRAs) for tuberculosis (TB) infection is not yet well-established. This study evaluates the cost and cost-effectiveness in two high-burden countries by comparing:(a) new skin-based tests(Diaskintest and Cy-Tb) with the purified protein derivative (PPD)-tuberculin test (TST);(b) IGRAs (Standard E TB-Feron ELISA (TBF))with approved IGRAs (QuantiFERON-TB Gold Plus (QFT-GP)and TSPOT.TB); and (c) the best performing skin-based test with the best performing IGRA) based on cost effectiveness. In this paper, we developed a decision tree model for India and Brazil from a health system perspective. To quantify the effect of parameter variability and uncertainty, we performed both univariate and probabilistic sensitivity analysis. The study findings reveal that among skin-based tests, the Diaskintest is more cost-effective compared to TST-PPD at 22.6 USD and 41.0 USD per correctly diagnosed case of TB infection for Brazil and India, respectively. For blood-based assays, TSPOT.TB outperforms QFT-GP and TBF due to its lower cost and higher effectiveness. When compared with Diaskintest, TSPOT.TB has an incremental cost of approximately 8 USD and 6 USD for India and Brazil respectively but is more effective. The incremental cost-effectiveness ratio (ICER) was 74 USD and 55 USD for India and Brazil, respectively. In summary, while Diaskintest is potentially cost-saving when compared to TSPOT.TB in these two high-burden TB countries but the TSPOT.TB demonstrates higher effectiveness.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".