Cost-effectiveness of Targeted Next Generation Sequencing for TB drug-resistance testing as an alternative to the standard of care in South Africa
Bibliographic record
Abstract
Abstract Background South Africa faces emerging resistance to key TB drugs, including bedaquiline. Phenotypic drug susceptibility testing (pDST), the current reference standard for bedaquiline DST, while accurate has long turnaround times. Targeted next-generation sequencing (tNGS) offers a comprehensive alternative to pDST, potentially delivering faster results. However, its advantages must be weighed against differences in implementation 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 at different levels of tNGS decentralization (1, 3, 4, or 6 sites). Key outcomes considered were survival rates, time to a correct resistance profile, duration of infectiousness, and disability-adjusted life years (DALYs). Sensitivity analyses assessed the impact of drug resistance prevalence, tNGS sensitivity, and improved DST access on DALYs and incremental cost per DALY averted. Results tNGS averted 408 DALYs and returned a greater number of correct resistance profiles (90.7%) as compared to the SOC (87.7%). Based on model and scenario assumptions for South Africa, tNGS returned results with a reduced turnaround time and averted 96 years of infectious time. Centralized tNGS was determined to be cost-saving relative to the SOC, however decentralization of tNGS resulted in higher incremental costs per DALY averted ($671-$2,454). tNGS performance relative to the SOC improved at higher bedaquiline resistance prevalence and when tNGS sensitivity increased. Access gains through tNGS increased the number of DALYs averted and decreased the respective incremental cost per DALY averted for decentralized scenarios. Conclusions Centralized tNGS testing is likely to be cost-saving in South Africa and decentralised tNGS would result in higher costs but could be cost-effective under current assumptions. Additionally, tNGS has the potential to reduce DALYs, shorten result turnaround times, and decrease infectious duration while improving the percentage of individuals receiving correct DST results.
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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 | 0.005 | 0.015 |
| 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".