Cost-effectiveness of targeted next-generation sequencing (tNGS) for detection of tuberculosis drug resistance in India, South Africa and Georgia: a modeling analysis
Bibliographic record
Abstract
Background: Targeted next-generation sequencing (tNGS) is promising alternative to phenotypic drug susceptibility testing (pDST) for detecting drug-resistant tuberculosis (DRTB). This study explored the potential cost-effectiveness of tNGS for the diagnosis of DR-TB across 3 settings: India, South Africa and Georgia. Methods: To inform WHO guideline development group (GDG) on tNGS we developed a stochastic decision analysis model and assessed cost-effectiveness of tNGS for DST among rifampicin resistance individuals. We also assessed tNGS as initial test for TB drug resistance in bacteriologically confirmed TB. Diagnostic accuracy and cost data were sourced from a systematic review conducted for GDG, covering studies published until September 2022. The primary outcome was incremental cost (2021 US$) per disability-adjusted life year (DALY) averted. Findings: tNGS when compared with in-country DST, tNGS proved cost-effective in South Africa (ICER: $15,619/DALY averted, WTP: $21,165) but not in Georgia (ICER: $18,375/DALY averted, WTP: $15,069). In India, tNGS dominated in-country DST practice, providing greater health impact at lower cost. When comparing tNGS with universal pDST, tNGS was dominated by pDST in all three countries. In Georgia, using tNGS as initial test for TB drug-resistance compared to Xpert MTB/Rif followed by pDST appeared cost-effective. Scenario with 50% reduction in tNGS test kit costs made tNGS cost-effective across all three countries, while a high Bedaquiline resistance prevalence (30%) led to a worsening cost-effectiveness. Interpretation: tNGS may be cost-effective in India, South Africa and Georgia when comprehensive DST is not routinely performed. Thus, existing DST practice and healthcare infrastructure should be considered before implementation and scale-up of tNGS. Funding: Global Tuberculosis Program, World Health Organization (2022/1249364-0).
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".