Prevalence of Potentially Inappropriate Medications in Older Adults with Cognitive Impairment or Dementia Attending Memory Clinics: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: Older adults with dementia who are on polypharmacy are more vulnerable to the use of potentially inappropriate medications (PIM), which can significantly increase the risk of adverse events and drug-related problems (DRPs). Objective: This systematic review and meta-analysis were conducted to map the prevalence of PIM use, polypharmacy, and hyper-polypharmacy among older adults with cognitive impairment or dementia attending memory clinics. Methods: Ovid MEDLINE, Ovid EMBASE, Scopus, Cochrane Library, EBSCOhost CINAHL, and Ovid International Pharmaceutical Abstracts (IPA) were systematically searched from inception to April 22, 2024. Observational studies assessing the PIMs use among older adults with CI or dementia were screened. A random- effects meta-analysis was conducted to pool the prevalence estimates. Results: Of 5,787 identified citations, 11 studies including 4,571 participants from 8 countries were included. Among all the included studies the pooled prevalence of PIM use was 38% (95% confidence interval (CIn): 27- 50%), highlighting a notable range from 20% to 78%. The analysis identified anticholinergics, benzodiazepines, and non-benzodiazepine sedatives as the most common PIMs. Subgroup analysis revealed a higher pooled prevalence of PIM in the USA (39%; 95% CIn: 10- 78, I2 (%) = 98, 3 studies) and Australia (36%, 95% CIn: 12- 70, I2 (%) = 96, 2 Studies). Additionally, pooled prevalence of polypharmacy and hyper-polypharmacy was reported as (60%; 95% CIn: 46- 73, I2 (%) = 95, 3 studies), and (The prevalence of hyper-polypharmacy was 17.6%; 1 study) respectively. Conclusions: The definition of PIMs significantly impacts study results, often more than geographical variations. The variability in criteria and tools like the Beers or Screening Tool of Older Persons' Prescriptions (STOPP) criteria across studies and regions leads to differing prevalence rates.
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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 | 0.014 | 0.036 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.043 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".