Usage and Adherence of Seven Advanced Therapies with Differing Mechanisms of Action for Inflammatory Arthritis in Canada
Bibliographic record
Abstract
INTRODUCTION: This retrospective, observational study aimed to analyze and assess adherence, persistence, dosing, and use of concomitant medications of seven self-administered target drugs (abatacept, golimumab, secukinumab, tocilizumab, ustekinumab, apremilast, and tofacitinib) that are currently available in Canada for the treatment of inflammatory arthritis (IA). METHODS: We used IQVIA's longitudinal claims databases, which include private drug plans and public plans. Patients with IA identified using a proprietary indication algorithm who initiated treatment with any of the target drugs between January 2015 and February 2019 were selected and followed for 12 months. RESULTS: Golimumab and apremilast had the highest proportion of patients (~ 75%) who were bio-naïve and secukinumab had the fewest bio-naïve patients (~ 43%). The oral therapies, apremilast and tofacitinib, had the lowest percentage of adherent patients (73% and 71%) followed by abatacept (83%), while the remaining drugs had adherence around 90%. Secukinumab and tofacitinib had the highest 12-month persistence rate (63% and 61%), while abatacept and apremilast had the lowest persistence rate (52% and 47%). Oral corticosteroid (OCS) use was not significantly associated with adherence. Tocilizumab, secukinumab, and ustekinumab had the highest proportion of patients (> 20%) with dose escalation at 3-4 months from index. OCS and conventional disease-modifying antirheumatic drugs (cDMARD) use decreased in post-index period across all target drugs. CONCLUSION: This study identified substantial differences in patient baseline characteristics. Patients on injectable biologics were more likely to be adherent compared with those on oral drugs, possibly owing to longer dosing intervals. Other outcomes at 12 months appeared similar as evidenced by tapering of concomitant medications, although differences in persistence and dose escalation were noted.
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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 | 0.002 |
| 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) | 0.001 | 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 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".