Cost-Effectiveness of Nalmefene Added to Psychosocial Support for the Reduction of Alcohol Consumption in Alcohol-Dependent Patients With High/Very High Drinking Risk Levels: A Microsimulation Model
Bibliographic record
Abstract
OBJECTIVE: A microsimulation model was adapted to evaluate the cost-effectiveness of nalmefene combined with psychosocial support (NMF + PS) versus psychosocial support alone (PS). The economic impact of alcohol reduction using nalmefene treatment was not evaluated. METHOD: The model simulates patient-level alcohol consumption over a 5-year time horizon across different treatment cohorts. Study outcomes included probabilities of alcohol-attributable diseases and injuries as well as deaths from these events. The approach used nalmefene clinical trial data, a time horizon of 1 and 5 years, and a U.K. societal perspective. Extensive deterministic and probabilistic sensitivity analyses were conducted. RESULTS: Compared with the PS strategy, NMF + PS was associated at Year 5 with a gain of 0.047 quality-adjusted life years (QALYs) and an additional £503, leading to an incremental cost-effectiveness ratio (ICER) of £10,613 per QALY gained. When compared with the strategy without treatment, NMF + PS was associated with a gain of 0.228 QALYs and an additional £1,795, leading to an ICER of £1,758 per QALY gained. The NMF + PS strategy dominated both treatment strategies when considering the U.K. societal perspective. Sensitivity analyses confirmed the robustness of the results. CONCLUSIONS: A combination of NMF and PS was better than PS alone, considering a 5-year time horizon and a societal perspective.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".