One Million Lives Saved Per Year: A Cost–Benefit Analysis of the Global Plan to End Tuberculosis, 2023–2030 and Beyond
Bibliographic record
Abstract
Abstract This report presents a cost–benefit analysis of increased spending on tuberculosis (TB) using impacts and costs drawn from the Global Plan to End Tuberculosis, 2023–2030. The analysis indicates that the return on TB spending is substantial with a centrally estimated benefit–cost ratio (BCR) of 46, meaning every US$ 1 invested in TB yields US$ 46 in benefits. Alternative specifications using different baselines, interventions, cost profiles, and discount rates still yield robustly high BCRs, in the range of 28–84. This report also shows that TB investment would avert substantial mortality, estimated at 27.3 million averted deaths over the 28-year period between 2023 and 2050 inclusive: almost 1 million averted deaths per year on average. Accounting for all estimated direct and indirect costs, the cost per averted death is slightly over US$ 2000. Interventions to address TB represent exceptional value-for-money.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".