The impact of memantine in combination with acetylcholinesterase inhibitors on admission of patients with Alzheimer’s disease to nursing homes: cost-effectiveness analysis in France
Bibliographic record
Abstract
The costs associated with the care of Alzheimer's disease patients are very high, particularly those associated with nursing home placement. The combination of a cholinesterase inhibitor (ChEI) and memantine has been shown to significantly delay admission to nursing homes as compared to treatment with a ChEI alone. The objective of this cost-effectiveness analysis was to evaluate the economic impact of the concomitant use of memantine and ChEI compared to ChEI alone. Markov modelling was used in order to simulate transitions over time among three discrete health states (non-institutionalised, institutionalised and deceased). Transition probabilities were obtained from observational studies and French national statistics, utilities from a previous US survey and costs from French national statistics. The analysis was conducted from societal and healthcare system perspectives. Mean time to nursing home admission was 4.57 years for ChEIs alone and 5.54 years for combination therapy, corresponding to 0.98 additional years, corresponding to a gain in quality adjusted life years (QALYs) of 0.25. From a healthcare system perspective, overall costs were €98,609 for ChEIs alone and €90,268 for combination therapy, representing cost savings of €8,341. From a societal perspective, overall costs were €122,039 and €118,721, respectively, representing cost savings of €3,318. Deterministic and probabilistic (Monte Carlo simulations) sensitivity analyses indicated that combination therapy would be the dominant strategy in most scenarios. In conclusion, combination therapy with memantine and a ChEI is a cost-saving alternative compared to ChEI alone as it is associated with lower cost and increased QALYs from both a societal and a healthcare perspective.
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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 | 0.018 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".