Economic Evaluations of Interventions to Optimize Medication Use in Older Adults with Polypharmacy and Multimorbidity: A Systematic Review
Bibliographic record
Abstract
PURPOSE: To conduct a systematic review of the economic impact of interventions intended at optimizing medication use in older adults with multimorbidity and polypharmacy. METHODS: We searched Ovid-Medline, Embase, CINAHL, Ageline, Cochrane, and Web of Science, for articles published between 2004 and 2020 that studied older adults with multimorbidity and polypharmacy. The intervention studied had to be aimed at optimizing medication use and present results on costs. RESULTS: Out of 3,871 studies identified by the search strategy, eleven studies were included. The interventions involved different provider types, with a majority described as a multidisciplinary team involving a pharmacist and a general practitioner, in the decision-making process. Interventions were generally associated with a reduction in medication expenditure. The benefits of the intervention in terms of clinical outcomes remain limited. Five studies were cost-benefit analyses, which had a net benefit that was either null or positive. Cost-utility and cost-effectiveness analyses resulted in incremental cost-effectiveness ratios that were generally within the willingness-to-pay thresholds of the countries in which the studies were conducted. However, the quality of the studies was generally low. Omission of key cost elements of economic evaluations, including intervention cost and payer perspective, limited interpretability. CONCLUSION: Interventions to optimize medication use may provide benefits that outweigh their implementation costs, but the evidence remains limited. There is a need to identify and address barriers to the scaling-up of such interventions, starting with the current incentive structures for pharmacists, physicians, and patients.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| 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 | 0.001 |
| 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".