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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".