Polypharmacy in long‐term care residents: Prevalence and relationships with age and dementia
Bibliographic record
Abstract
Abstract Background Polypharmacy is a significant problem of growing interest in healthcare for older adults with the current increase in drug consumption. Inappropriate polypharmacy is a risk factor for numerous adverse health outcomes and creates a growing financial burden to society. Yet, evaluations of objective measures of polypharmacy and their applications in older adults are not well studied. Here, we investigate the medication use in long‐term care residents and their relationships with age and cognition. Method Medical records including medication consumption of 350 residents in long‐term care facilities (mean age: 81.1 ± 10.9 years; range: 52–104; female: 63.7%) diagnosed with or without dementia (203:147) were reviewed, using PointClickCare and the InterRAI‐MDS2.0. Four scores assessing medication use were generated at two time points (at baseline and at 12 months or at time of discharge). The drug scores included the counts of 1) total medications, 2) prescription drugs, 3) Beers Criteria drugs, and 4) over the counter medications and supplements. Distributions of each the drug scores were examined and their relationships with age and cognition scores (MMSE) were tested using correlation and regression analyses. Group mean scores were compared for dementia status. Result The average number of total drugs, prescription drugs, and beer’s drugs taken were 8.6±4.7 (range: 0‐24), 3.29±5.2 (0‐19), and 1.2±1.2 (0‐6) respectively, and differed by dementia status (p‐value<0.050). Each of the drug scores was related with age (r’s> 0.0685, p‐values<0.050). Conclusion Polypharmacy is prevalent in older adults with long‐term care. Residents with dementia have a distinguished profile with medication consumption. Ongoing research in better understanding polypharmacy profiles in relation to frailty and cognition status can further benefit medication optimization of older adults in residential care.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".