Economic Evaluation of the Impact of Memantine on Time to Nursing Home Admission in the Treatment of Alzheimer Disease
Bibliographic record
Abstract
OBJECTIVE: An observational study showed that combining memantine with a cholinesterase inhibitor (ChEI) treatment significantly delayed admission to nursing homes in patients with Alzheimer disease (AD). Our study aimed to evaluate the economic impact of the concomitant use of memantine and a ChEI, compared with a ChEI alone, in a Canadian population of patients with AD. METHOD: A cost-utility analysis using a Markov model during a 7-year time horizon was performed according to a societal and Canadian health care system perspective. The Markov model includes the following states: noninstitutionalized, institutionalized, and deceased. The model includes transition probabilities for institutionalization and death, adjusted with mortality rates specific to AD. Utilities associated with institutionalization and noninstitutionalization were included. For the health care system perspective, costs of medication as well as costs of care provided in the community and in nursing homes were considered. For the societal perspective, costs of direct care and supervision provided by caregivers were added. RESULTS: From both perspectives, the concomitant use of a ChEI and memantine is a dominant strategy, compared with the use of a ChEI alone. On a per patient basis, there was a gain of 0.26 quality-adjusted life years with the treatment including memantine and cost decreases of Can$21 391 and Can$30 512, respectively, for the societal and health care system perspective. CONCLUSIONS: This economic evaluation indicates that institutionalization is the largest cost component in AD management and that the use of memantine, combined with a ChEI, to treat AD is a cost-effective alternative, compared with the use of a ChEI alone.
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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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 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".