Costs of Novel Tuberculosis Diagnostics—Will Countries Be Able to Afford It?
Bibliographic record
Abstract
BACKGROUND: Four priority target product profiles for the development of diagnostic tests for tuberculosis were identified: 1) Rapid sputum-based (RSP), 2) non-sputum Biomarker-based (BMT), 3) triage test followed by confirmatory test (TT), and 4) drug-susceptibility testing (DST). METHODS: We assessed the cost of the new tests in suitable strategies and of the conventional diagnosis of tuberculosis as per World Health Organization guidelines, in 36 high tuberculosis and MDR burden countries. Costs were then compared to the available funding for tuberculosis at country level. RESULTS: Costs of diagnosing tuberculosis using RSP ranged US$93-187 million/year; if RSP unit cost is of US$2-4 it would be lower/similar cost than conventional strategy with sputum smear microscopy (US$ 119 million/year). Using BMT (with unit cost of US$2-4) would cost US$70-121 million/year and be lower/comparable cost than conventional diagnostics. Using TT with TPP characteristics (unit cost of US$1-2) followed by Xpert would reduce diagnostic costs up to US$36 million/year. Costs of using different novel DST strategies for the diagnosis of drug resistance would be higher compared with conventional diagnosis. CONCLUSIONS: Introducing a TT or a biomarker test with optimal characteristics would be affordable from a cost and affordability perspective at the current available funding for tuberculosis. Additional domestic or donor funding would be needed in most countries to achieve affordability for other new diagnostic tests.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".