TB antigen-based skin tests and QFT-Plus for Mycobacterium tuberculosis infection diagnosis in Brazilian healthcare workers: a cost-effectiveness analysis
Bibliographic record
Abstract
This study aimed to analyze the cost-effectiveness of three tuberculosis (TB) antigen-based skin tests (TBST) (Diaskintest, C-TST, and Cy-TB) and QFT-Plus for TB infection diagnosis compared to the current standard of care, PPD Rt-23 tuberculin skin test (TST), among healthcare workers in Brazil. A state-transition Markov model was employed, simulating a cohort of healthcare workers (five annual cycles) for testing and treating TB infection with three months of weekly doses of rifapentine and isoniazid (3HP) under the Brazilian public health system perspective. Effects (TB disease averted) and costs for screening and treating TB infection were discounted at 5%. Incremental cost-effectiveness per TB averted was estimated. One-way and probabilistic sensitivity analysis were performed. Brazil, an upper-middle-income country with a high burden of TB, shows one of the largest universal public health systems and provides free-of-charge diagnosis and treatment for TB and TB infection. TST is the standard of care, whereas QFT-Plus is available for very high-risk populations. The three new TBST are under validation for eventual incorporation. Patients or participants: a hypothetical cohort of 10,000 healthcare workers, working at any level of healthcare service, and negative TST results in the previous year of both sexes with a baseline negative TST result. Diaskintest, C-TST, Cy-TB, and QFT-Plus were found to show a higher specificity. Costs with QFT-Plus were higher due to equipment, human labor, and test price. Diaskintest was the most cost-saving strategy, followed by Cy-TB for TB preventive treatment with 3HP. In the Brazilian scenario, Diaskintest and Cy-TB are the most cost-effective tests for sequential testing of healthcare workers.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".