The cost-effectiveness and budget impact of Vietnam's methadone maintenance treatment programme in HIV prevention and treatment among injection drug users
Bibliographic record
Abstract
We analysed the cost-effectiveness and budget impact of the methadone maintenance treatment (MMT) programme in HIV prevention and treatment among injection drug users (DUs) in Vietnam. The costs and health outcomes of providing MMT for opioid-dependent DUs versus non-MMT were estimated using a decision analytical model. Probabilistic sensitivity analysis using Monte Carlo simulation was conducted to justify uncertainties of model parameters simultaneously. The incremental cost-effectiveness ratio (ICER) of MMT in HIV prevention was US$3324 per one averted HIV case. The decision model showed that the cost-effectiveness ratio of MMT and non-MMT strategies was US$480 and US$204 per 1 quality-adjusted life year (QALY), equivalent to 0.43 and 0.18 times the gross domestic product per capita (GDPpc). The ICER for MMT versus non-MMT strategy was US$1964, approximately 1.76 times the GDPpc/QALY, classifying MMT as a cost-effective intervention. At the willingness to pay threshold of three times the GDPpc, the probability of MMT and non-MMT strategies being cost-effective was 80.3 and 19.7%, respectively. The budget impact of scaling up MMT from 2011 to 2015 will be US$97 million for 65% coverage or US$49 million for treating 80,000 DUs. The results indicated that MMT was cost-effective in HIV prevention and treatment among DUs who were opioid dependent.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".