076 Addressing polypharmacy in long-term care: the case for deprescribing low-risk medications and supplements
Bibliographic record
Abstract
Background Polypharmacy is a major concern in long-term care facilities, with upwards of half of residents taking 10 or more medications and supplements daily. While deprescribing initiatives often focus on higher-risk medications, lower-risk medications and supplements that may be more efficient to deprescribe and have questionable benefit are often overlooked. Our objective was to examine the use of such products in Alberta long-term care (LTC) facilities, along with their associated financial, environmental, and nursing costs. Methods We used dispensation data from the Alberta Pharmaceutical Information Network (PIN) within the Alberta Health Services Enterprise Data Warehouse for July to September 2024. This covers 80% of LTC facilities in Alberta and includes 11,738 LTC residents. A LTC physician and pharmacist reviewed the 50 most-dispensed products (80% of total dispensations) to identify ones more efficient to deprescribe and of questionable benefit. Criteria included minimal time needed to assess deprescribing, no tapering required, little post-deprescribing monitoring, and evidence of no net benefit or no net benefit under certain criteria (ex. systolic blood pressure <130 mmHg for antihypertensives). We then calculated the total number of these products dispensed, their share of the 50 most-dispensed products, and their associated costs, including financial cost (sourced from several Alberta references); environmental cost (based on the Medicine Carbon Footprint Formulary); and nursing time (using 45 seconds to dispense, as reported in a previous study). Results Sixteen products were identified including analgesics (acetaminophen), cardiovascular (amlodipine, ramipril, perindopril, candesartan, atorvastatin, rosuvastatin, acetylsalicylic acid), vitamins (vitamin B12, multi-vitamins), fracture prevention (risedronate, vitamin D, calcium), urologic (tamsulosin, dutasteride), and CNS active (melatonin). Over a 4-month period, a total of 6.1 million of these products were dispensed across Alberta LTC facilities, accounting for 41% of the 50 most-dispensed products. Cost and carbon footprint data was estimated for 57% and 46% of products, respectively, it was not available for the remaining products. At minimum, on an annual basis, the total costs of these products is $1M Cdn; their carbon footprint is 108 tonnes Co2e; and dispensing them requires 2,294 nursing hours. Conclusion A substantial proportion of medications and supplements dispensed in Alberta LTC facilities have questionable benefit and are straightforward to deprescribe. Reducing these products may be an effective strategy for addressing polypharmacy in LTC facilities. Next steps are to explore caregiver and patient preferences and approaches for deprescribing these products in LTC facilities.
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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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".