The impact of polypharmacy on the health of Canadian seniors
Bibliographic record
Abstract
BACKGROUND: Prescription medication use increases with age. Seniors face an increased risk of adverse drug reactions from medications, partly because the kidneys and liver can lose functional ability with increasing age, resulting in the need for changes in dosage. OBJECTIVE: To use population survey data to understand the extent and impact of multiple medication use and adverse drug events among Canadian seniors. METHODS: This study consists of analysis of data from the Canadian Survey of Experiences with Primary Health Care, which was conducted through telephone by Statistics Canada in 2008. These analyses focussed on the 3132 respondents who were ≥ 65 years of age. RESULTS: Twenty-seven per cent of seniors reported taking five or more medications on a regular basis. Within the past year, 12% of seniors taking five or more medications experienced a side effect that required medical attention compared with 5% of seniors taking only one or two medications. Even when controlling for age and number of chronic conditions, the number of prescription medications was associated with the rate of emergency department use. Less than half of all seniors reported having received medication reviews and having the possible side effects of their prescription medications explained to them by their physician. CONCLUSIONS: Many Canadian seniors have an elevated risk of adverse events due to taking a high number of prescription medications and not having the potential side effects and drug interactions explained to them. There are interventions that can potentially reduce polypharmacy and adverse events, including routine medication reviews.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".