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.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".