Cost-effectiveness of a Province-wide Quality Improvement Initiative for Reducing Potentially Inappropriate Use of Antipsychotics in Long-Term Care in British Columbia, Canada
Bibliographic record
Abstract
BACKGROUND: Potentially inappropriate use of antipsychotics (PIUA) raises serious concerns about safety, quality, and cost of care for residents in long-term care (LTC). OBJECTIVE: This study aimed to estimate the cost-effectiveness of the Call for Less Antipsychotics in Long-Term Care (Clear) initiative compared with the status quo (pre-Clear, baseline). METHODS: A model-based cost-utility analysis, from a public-payer perspective in British Columbia, was conducted using secondary data of residents in LTC homes from 2013 to 2019. Residents' health resource utilization and quality-adjusted life-year (QALY) measures were extracted from multiple administrative databases. Six Markov states were modelled for post-antipsychotic progression representing PIUA, appropriate use of antipsychotic, complete withdrawal, and death. The primary outcome was the incremental cost per QALY gained. RESULTS: A cohort of 35,669 residents was included in the primary analysis. The Clear initiative, over 10 years, was estimated to have an incremental cost-effectiveness ratio (ICER) of CA$26,055 (2020 Canadian dollars) per QALY gained at an incremental cost of CA$5211 per resident and a QALY gain of 0.20. In the subgroup analyses, our findings were even more favourable for Clear wave 2 (ICER of CA$24,447 per QALY gained) and Clear wave 3 (ICER of CA$25,933 per QALY gained). At a willingness-to-pay of CA$50,000 per QALY gained, the probabilities of Clear waves 2 and 3 were 82% cost-effective. CONCLUSION: This study demonstrated incremental costs and yielded favourable ICERs for Clear compared with the baseline. More research is needed to understand the level of support for individual care homes to sustain the Clear initiative in the long run.
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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) | 0.001 | 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) | 0.000 | 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".