Potentially inappropriate medications in older adults: a population-based cohort study
Bibliographic record
Abstract
BACKGROUND: Non-optimal medication use among older adults is a public health concern. A concrete picture of potentially inappropriate medication (PIM) use is imperative to ensure optimal medication use. OBJECTIVE: To assess the prevalence of PIMs in community-dwelling older adults and identify associated factors. METHODS: A retrospective population-based cohort study was conducted using the Quebec Integrated Chronic Disease Surveillance System (QICDSS). The QICDSS includes data on drug claims for community-dwelling older adults with chronic diseases or at risk of developing chronic diseases aged ≥65 years who are insured by the public drug insurance plan. Individuals aged ≥66 years who were continuously insured with the public drug plan between 1 April 2014 and 31 March 2016 were included. PIMs were defined using the 2015 Beers criteria. We conducted multivariate robust Poisson regression analyses to explore factors associated with PIM use. RESULTS: A total of 1 105 295 individuals were included. Of these, 48.3% were prescribed at least one PIM. The most prevalent PIMs were benzodiazepines (25.7%), proton-pump inhibitors (21.3%), antipsychotics (5.6%), antidepressants (5.0%) and long-duration sulfonylureas (3.3%). Factors associated with PIM exposure included being a woman [rate ratio (RR): 1.20; 95% confidence interval (CI): 1.20-1.21], increased number of medications and having a high number of chronic diseases, especially mental disorders (RR: 1.50; 95% CI: 1.49-1.51). CONCLUSION: Almost one out of two community-dwelling older adults use a PIM. It is imperative to reduce the use of PIMs, by limiting their prescription and by promoting their deprescribing, which necessitates not only the active involvement of prescribers but also 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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".