Cost-effectiveness of Targeted Next-Generation Sequencing for Tuberculosis Drug-resistance Testing as an Alternative to the Standard of Care in South Africa
Bibliographic record
Abstract
BACKGROUND: South Africa faces emerging resistance to tuberculosis drugs like bedaquiline. Phenotypic drug-susceptibility testing (DST), the current reference standard for bedaquiline DST, has long turnaround times. Targeted next-generation sequencing (tNGS) offers a comprehensive alternative, potentially delivering faster results. However, these advantages must be weighed against differences in cost and test accuracy. METHODS: We used a decision tree model to evaluate the cost-effectiveness of tNGS against the standard of care (SOC) in South Africa across different levels of tNGS decentralization. Key outcomes included survival rates, time to a correct resistance profile, infectious time, and disability-adjusted life years (DALYs). Sensitivity analyses assessed the impact of drug-resistance prevalence, tNGS sensitivity, and improved DST access on overall cost-effectiveness. RESULTS: tNGS averted 408 DALYs and correctly identified 90.7% of resistance profiles as compared to 87.7% with SOC. Based on model assumptions for South Africa, tNGS had a reduced turnaround time and averted 97 years of infectious time. Centralized tNGS was cost-saving relative to SOC; however, decentralization of tNGS resulted in higher costs per DALY averted ($671-$2454). tNGS performance, relative to the SOC, improved at higher bedaquiline resistance and with increased sensitivity. Any increase in DST access through tNGS would improve cost-effectiveness in decentralized scenarios. CONCLUSIONS: tNGS could be cost-saving (centralized) or cost-effective (decentralized) in South Africa and has the potential to improve patient outcomes by returning a greater number of correct results in a shorter time. This analysis should be replicated across other settings to evaluate the broader feasibility of tNGS for DST.
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".