Adherence and dosing interval of subcutaneous antitumour necrosis factor biologics among patients with inflammatory arthritis: analysis from a Canadian administrative database
Bibliographic record
Abstract
OBJECTIVES: Subcutaneous tumour necrosis factor alpha TNFαinhibitors (SC-TNFis) such as golimumab (GLM), adalimumab (ADA), etanercept (ETA) and certolizumab pegol (CZP) have been used for many years for the treatment of inflammatory arthritis. Non-adherence to therapy is an important modifiable factor that may compromise patient outcomes. The aim of this analysis was to compare adherence and dosing interval of SC-TNFis in the treatment of people with inflammatory arthritis. DESIGN: We used the IMS Brogan database combining both Canadian private and public drug plan databases of Ontario and Quebec. Target drugs included SC-TNFis for inflammatory arthritis. The index period was from 1 January 2010 to 30 June 2012 and patients were followed for 24 months through 30 June 2014. Inclusion criteria were adult patients newly prescribed a SC-TNFis with at least three prescriptions and retained on therapy at 24 months.Dosing regimens as per the product monographs were used to compare actual versus expected drug utilisation. The mean possession ratio was used as a marker for adherence. Patients who scored >80% were considered adherent. The average days between units was estimated by taking the total days on therapy and divided by the number of units the patient received. RESULTS: 4035 patients were included: 683 (16.9%), 1400 (34.7%), 1765 (43.7%) and 187 (4.6%) were treated with GLM, ADA, ETA and CZP, respectively. The proportion of adherent patients in the GLM cohort (n=595/683, 87%, p<0.0001) was greater compared with ADA (n=1044/1400, 75%), ETA (n=1285/1765, 73%) and CZP-treated patients (132/187, 71%). In addition, the number of patients receiving biological drug at a shorter dosing interval was similar between cohorts, and was 5%, 6%, 12% and 4% in GLM (≤26 days), ADA (≤12 days), ETA (≤6 days) and CZP-treated patients (≤12 days), respectively. CONCLUSIONS: In this real-life administrative database, GLM had better adherence compared with other SC-TNFis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.017 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".