Health care system costs related to potentially inappropriate medication use involving opioids in older adults in Canada
Bibliographic record
Abstract
Abstract Background Older adults are at risk of potentially inappropriate medication use given polypharmacy, multimorbidity, and age-related changes, which contribute to the growing burden associated with opioid use. The objective of this study was to estimate the costs of health service utilization attributable to opioid use and potentially inappropriate medication use involving opioids in older adults in a public health care system. Methods The sample included 1201 older adults consulting in primary care, covered by the public drug plan, without a cancer diagnosis and opioid use in the year before interview. Secondary analyses were conducted using two data sources: health survey and provincial administrative data. Health system costs included inpatient and outpatient visits, physician billing, and medication costs. Unit costs were calculated using annual financial and activity reports from 2013–2014, adjusted to 2022 Canadian dollars. Opioid use and potentially inappropriate medication use involving opioids were identified over 3 years. Generalized linear models with gamma distribution were employed to model 3-year costs associated with opioid use and potentially inappropriate medication use involving opioids. A phase-based approach was implemented to provide descriptive results on the costs associated with each phase: i) no use, ii) opioid use, and iii) potentially inappropriate medication use involving opioids. Results Opioid use and potentially inappropriate medication use involving opioids were associated with adjusted 3-year costs of $2,222 (95% CI: $1,179-$3,264) and $8,987 (95% CI: $7,370-$10,605), respectively, compared to no use. In phase-based analyses, costs were the highest during inappropriate use. Conclusions Potentially inappropriate medication use involving opioids is associated with higher costs compared to those observed with opioid use and no use. There is a need for more effective use of health care resources to reduce costs for the health care system.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".