Cost-Effectiveness of All-Oral Regimens for the Treatment of Multidrug-Resistant Tuberculosis in Korea: Comparison With Conventional Injectable-Containing Regimens
Bibliographic record
Abstract
BACKGROUND: Regimens for the treatment of multidrug-resistant tuberculosis (MDR-TB) have been changed from injectable-containing regimens to all-oral regimens. The economic effectiveness of new all-oral regimens compared with conventional injectable-containing regimens was scarcely evaluated. This study was conducted to compare the cost-effectiveness between all-oral longer-course regimens (the oral regimen group) and conventional injectable-containing regimens (the control group) to treat newly diagnosed MDR-TB patients. METHODS: A health economic analysis over lifetime horizon (20 years) from the perspective of the healthcare system in Korea was conducted. We developed a combined simulation model of a decision tree model (initial two years) and two Markov models (remaining 18 years, six-month cycle length) to calculate the incremental cost-effectiveness ratio (ICER) between the two groups. The transition probabilities and cost in each cycle were assumed based on the published data and the analysis of health big data that combined country-level claims data and TB registry in 2013-2018. RESULTS: The oral regimen group was assumed to spend 20,778 USD more and lived 1.093 years or 1.056 quality-adjusted life year (QALY) longer than the control group. The ICER of the base case was calculated to be 19,007 USD/life year gained and 19,674 USD/QALY. The results of sensitivity analyses showed that base case results were very robust and stable, and the oral regimen was cost-effective with a 100% probability for a willingness to pay more than 21,250 USD/QALY. CONCLUSION: This study confirmed that the new all-oral longer regimens for the treatment of MDR-TB were cost-effective in replacing conventional injectable-containing regimens.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".