Interferon-gamma release assays and TB screening in high-income countries: a cost-effectiveness analysis.
Bibliographic record
Abstract
OBJECTIVE: Interferon-gamma release assays (IGRA) are now available alternatives to tuberculin skin testing (TST) for detection of latent tuberculosis infection (LTBI). We compared the cost-effectiveness of TST and IGRA in different populations and clinical situations, and with variation of a number of parameters. METHODS: Markov modelling was used to compare expected TB cases and costs over 20 years following screening for TB with different strategies among hypothetical cohorts of foreign-born entrants to Canada, or contacts of TB cases. The less expensive commercial IGRA, Quanti-FERON-TB Gold (QFT), was examined. Model inputs were derived from published literature. RESULTS: For entering immigrants, screening with chest radiograph (CXR) would be the most and QFT the least cost-effective. Sequential screening with TST then QFT was more cost-effective than QFT alone in all scenarios, and more cost-effective than TST alone in selected subgroups. Among close and casual contacts, screening with TST or QFT would be cost saving; savings with TST would be greater than with QFT, except in contacts who were bacille Calmette-Guérin (BCG) vaccinated after infancy. CONCLUSIONS: Screening for LTBI, with TST or QFT, is cost-effective only if the risk of disease is high. The most cost-effective use of QFT is to test TST-positive persons.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| 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".