Real-World Experience With Apremilast in the Treatment of Adults With Moderate to Severe Plaque Psoriasis in Québec: A Claims-Based Analysis of Drug Utilization and Healthcare Resource Utilization
Bibliographic record
Abstract
BACKGROUND: In Québec, targeted biologic therapies for moderate to severe plaque psoriasis are restricted to patients who have not responded to phototherapy or conventional systemic treatment, primarily due to high drug costs. Apremilast, an oral treatment for plaque psoriasis, was added to the Québec provincial health insurance plan (Régie de l'assurance maladie du Québec; RAMQ) formulary in 2015, making this the only province in Canada with public drug plan reimbursement for apremilast. OBJECTIVES: The aim of this study is to describe patients' characteristics, treatment patterns, healthcare resource utilization (HCRU), and associated costs and to measure real-world budget impact of using apremilast before biologics in plaque psoriasis. METHODS: This study was performed using RAMQ drug claims and medical services data. Patients diagnosed with psoriasis between January 2015 and December 2017 were identified. Medical services and prescription claims were categorized as all-cause and psoriasis-related. Using RAMQ database estimates, a 3-year budget impact analysis was developed comparing treatment cost with and without the addition of apremilast to the formulary. RESULTS: < .01), mainly driven by drug cost. Using apremilast before biologics resulted in an estimated RAMQ net savings of CAN$49 290 (2015), CAN$746 856 (2016), and CAN$1 216 512 (2017), and a total savings of CAN$2 012 658 since apremilast's addition to the formulary. CONCLUSION: Adding apremilast to the drug formulary of other Canadian provinces could result in significant healthcare savings.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".