Cost-effectiveness analysis comparing QuantiFERON test and tuberculin skin test for the diagnosis of latent tuberculosis infection in immunocompetent children under 15 years of age in Colombia
Bibliographic record
Abstract
OBJECTIVE: To determine the cost-effectiveness of the QuantiFERON (QFT) test versus the tuberculin skin test (TST) in diagnosing latent tuberculosis infection (LTBI) in immunocompetent children under 15 years of age who are in contact with active tuberculosis (TB) patients in the context of the Colombian healthcare system. DESIGN: Health economic evaluation. Decision tree over a horizon of <1 year. SETTING: From the perspective of the Colombian healthcare system, the direct healthcare costs related to tests were considered, and diagnostic performance was used as a measure of effectiveness. The currency was the US dollar (US$) for the year 2022, with a cost-effectiveness threshold of US$6666. PARTICIPANTS: A simulated hypothetical cohort of 2000 immunocompetent children under 15 years of age who are in contact with active TB patients and were vaccinated with BCG at birth. INTERVENTIONS: QFT test and TST to detect LTBI. PRIMARY OUTCOME MEASURE: The incremental cost-effectiveness ratio (ICER) was estimated, and univariate deterministic and probabilistic sensitivity analyses were conducted using 5000 simulations. RESULTS: QFT was found to be cost-effective with an ICER of US$705 for each correctly diagnosed case. In the one-way deterministic sensitivity analysis, QFT remained cost-effective across nearly all proposed scenarios; however, the QFT was considered 'potentially cost-effective' when TST specificity reached its highest value. The ICER was unaffected by variations in LTBI prevalence. In the probabilistic sensitivity analysis, QFT was cost-effective in 85.06% of the simulated scenarios, while TST was dominant in 11.8%. CONCLUSIONS: This study provides evidence of the cost-effectiveness of QFT compared with TST in diagnosing LTBI among immunocompetent children under 15 years who have been in contact with active TB patients in the Colombian context.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 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) | 0.004 | 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".