Potential Cost-Effectiveness of a New Infant Tuberculosis Vaccine in South Africa - Implications for Clinical Trials: A Decision Analysis
Bibliographic record
Abstract
Novel tuberculosis vaccines are in varying stages of pre-clinical and clinical development. This study seeks to estimate the potential cost-effectiveness of a BCG booster vaccine, while accounting for costs of large-scale clinical trials, using the MVA85A vaccine as a case study for estimating potential costs. We conducted a decision analysis from the societal perspective, using a 10-year time frame and a 3% discount rate. We predicted active tuberculosis cases and tuberculosis-related costs for a hypothetical cohort of 960,763 South African newborns (total born in 2009). We compared neonatal vaccination with bacille Calmette-Guérin alone to vaccination with bacille Calmette-Guérin plus a booster vaccine at 4 months. We considered booster efficacy estimates ranging from 40% to 70%, relative to bacille Calmette-Guérin alone. We accounted for the costs of Phase III clinical trials. The booster vaccine was assumed to prevent progression to active tuberculosis after childhood infection, with protection decreasing linearly over 10 years. Trial costs were prorated to South Africa's global share of bacille Calmette-Guérin vaccination. Vaccination with bacille Calmette-Guérin alone resulted in estimated tuberculosis-related costs of $89.91 million 2012 USD, and 13,610 tuberculosis cases in the birth cohort, over the 10 years. Addition of the booster resulted in estimated cost savings of $7.69-$16.68 million USD, and 2,800-4,160 cases averted, for assumed efficacy values ranging from 40%-70%. A booster tuberculosis vaccine in infancy may result in net societal cost savings as well as fewer active tuberculosis cases, even if efficacy is relatively modest and large scale Phase III studies are required.
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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 | 0.016 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".