Economic Evaluation of Sedative Deprescribing in Older Adults by Community Pharmacists
Bibliographic record
Abstract
BACKGROUND: Sedative use in older adults increases the risk of falls, fractures, and hospitalizations. The D-PRESCRIBE (Developing Pharmacist-Led Research to Educate and Sensitize Community Residents to the Inappropriate Prescriptions Burden in the Elderly), pragmatic randomized clinical trial demonstrated that community-based, pharmacist-led education delivered simultaneously to older adults and their primary care providers reduce the use of sedatives by 43% over 6 months. However, the associated health benefits and cost savings have yet to be described. This study evaluates the cost-effectiveness of the D-PRESCRIBE intervention compared to usual care for reducing the use of potentially inappropriate sedatives among older adults. METHODS: A cost-utility analysis from the public health care perspective of Canada estimated the costs and quality-adjusted life-years (QALYs) associated with the D-PRESCRIBE intervention compared to usual care over a 1-year time horizon. Transition probabilities, intervention effectiveness, utility, and costs were derived from the literature. Probabilistic analyses were performed using a decision tree and Markov model to estimate the incremental cost-effectiveness ratio. RESULTS: Compared to usual care, pharmacist-led deprescribing is less costly (-$1392.05 CAD) and more effective (0.0769 QALYs). Using common willingness-to-pay (WTP) thresholds of $50 000 and $100 000, D-PRESCRIBE was the optimal strategy. Scenario analysis indicated the cost-effectiveness of D-PRESCRIBE is sensitive to the rate of deprescribing. CONCLUSIONS: Community pharmacist-led deprescribing of sedatives is cost-effective, leading to greater quality-of-life and harm reduction among older adults. As the pharmacist's scope of practice expands, consideration should be given to interprofessional models of remuneration for quality prescribing and deprescribing services.
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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 | 0.012 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.004 | 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".