Cost-effectiveness of a new interferon-based blood assay, QuantiFERON-TB Gold, in screening tuberculosis contacts.
Bibliographic record
Abstract
BACKGROUND: Recent approval of interferon-gamma release assays that are more specific for Mycobacterium tuberculosis has given new options for the diagnosis of latent tuberculosis infection (LTBI). OBJECTIVE: To assess the cost-effectiveness of Quanti-FERON-TB Gold (QFT-G) vs. the tuberculin skin test (TST) in diagnosing LTBI in contacts of active TB cases using a decision analytic Markov model. METHODS: Three screening strategies--TST alone, QFT-G alone and sequential screening of TST then QFT-G--were evaluated. The model was further stratified according to ethnicity and bacille Calmette-Guérin (BCG) vaccination status. Data sources included published studies and empirical data. Results were reported in terms of the incremental net monetary benefit (INMB) of each strategy compared with the baseline strategy of TST-based screening in all contacts. RESULTS: The most economically attractive strategy was to administer QFT-G in BCG-vaccinated contacts, and to reserve TST for all others (INMB CA$3.70/contact). The least cost-effective strategy was QFT-G for all contacts, which resulted in an INMB of CA$-11.50 per contact. Assuming a higher prevalence of recent infection, faster conversion of QFT-G, a higher rate of TB reactivation, reduction in utility or greater adherence to preventive treatment resulted in QFT-G becoming cost-effective in more subgroups. CONCLUSIONS: Selected use of QFT-G appears to be cost-effective if used in a targeted fashion.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".