The cost–effectiveness of buprenorphine maintenance therapy for opiate addiction in the United States
Bibliographic record
Abstract
AIMS: To determine the cost-effectiveness of buprenorphine maintenance therapy for opiate addiction in the United States, particularly its effect on the HIV epidemic. DESIGN: We developed a dynamic model to capture the effects of adding buprenorphine maintenance to the current opiate dependence treatment system. We evaluated incremental costs, including all health-care costs, and incremental effectiveness, measured as quality-adjusted life years (QALYs) of survival. We considered communities with HIV prevalence among injection drug users of 5% and 40%. Because no price has been set in the United States for a dose of buprenorphine, we considered three prices per dose: $5, $15, and $30. FINDINGS: If buprenorphine increases the number of individuals in maintenance treatment by 10%, but does not affect the number of individuals receiving methadone maintenance, the cost-effectiveness ratios for buprenorphine maintenance therapy are less than $45 000 per QALY gained for all prices, in both the low-prevalence and high-prevalence communities. If the same number of individuals enter buprenorphine maintenance (10% of the number currently in methadone), but half are injection drug users newly entering maintenance and half are individuals who switched from methadone to buprenorphine, the cost-effectiveness ratios in both communities are less than $45 000 per QALY gained for the $5 and $15 prices, and greater than $65 000 per QALY gained for the $30 price. CONCLUSIONS: At a price of $5 or less per dose, buprenorphine maintenance is cost-effective under all scenarios we considered. At $15 per dose, it is cost-effective if its adoption does not lead to a net decline in methadone use, or if a medium to high value is assigned to the years of life lived by injection drug users and those in maintenance therapy. At $30 per dose, buprenorphine will be cost-effective only under the most optimistic modeling assumptions.
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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 | 0.002 | 0.006 |
| 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) | 0.003 | 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".