Frequency and cost of potentially inappropriate prescribing for older adults: a cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Many medications pose greater health risks when prescribed for older adults, compared with available pharmacologic and nonpharmacologic alternatives. We sought to quantify the frequency and cost of potentially inappropriate prescribing for older women and men in Canada. METHODS: Using data for 2013 from the National Prescription Drug Utilization Information System database, which contains prescription claims from publicly financed drug plans in all provinces except for Quebec, we identified the frequency of prescribing and cost of potentially inappropriate medications dispensed to provincial drug plan enrollees aged 65 years or more. Potentially inappropriate prescriptions were defined with the use of the American Geriatrics Society's 2012 version of the Beers Criteria for potentially inappropriate medication use in older adults. RESULTS: For the 6 provinces with relatively complete data coverage (British Columbia, Alberta, Saskatchewan, Manitoba, Ontario and Prince Edward Island), 37% of older people filled 1 or more prescription meeting the Beers Criteria. A higher proportion of women (42%) than men (31%) filled potentially inappropriate prescriptions. The highest rates of prescribing of potentially inappropriate medications were among women aged 85 or more (47%). Benzodiazepines and other hypnotics were the leading contributors to the overall frequency of and sex differences in prescribing of potentially inappropriate drugs among older adults. We estimated that $75 per older Canadian, or $419 million in total, was spent on potentially inappropriate medications outside of hospital settings in 2013. INTERPRETATION: Prescribing of potentially inappropriate medications for older adults is common and costly in Canada, especially for women. Multipronged and well-coordinated strategies to reduce the use and cost of potentially inappropriate drugs would likely generate significant health system savings while simultaneously generating major benefits to patient health.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| 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) | 0.001 | 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".