DMARD Utilization Patterns in Community-Dwelling Rheumatoid Arthritis Patients: Insights from Linked Primary Care and Pharmaceutical Data
Bibliographic record
Abstract
Objectives To examine disease-modifying antirheumatic drug (DMARD) dispensation patterns among rheumatoid arthritis (RA) patients in primary care settings and explore associations between patient/physician characteristics and DMARD utilization. Methods This retrospective cohort study utilized linked primary care electronic medical records (EMRs) from the Southern Alberta Primary Care Research Network with provincial pharmaceutical dispensation data. We analyzed DMARD dispensation patterns for 597 RA patients aged ≥19 years with at least 1 primary care encounter between 2008-2020. Logistic regression was used to examine associations between patient/physician characteristics (age, sex, rurality/urbanicity, deprivation category) and DMARD utilization. Results Of 597 RA patients (72% female, mean age 55 years), 67% were dispensed at least 1 DMARD during the average 7-year follow-up. Notably, 58% had DMARD dispensations before RA documentation in their EMR, likely due to PCPs recording diagnosis after specialist confirmation and treatment initiation. Methotrexate (76%) and hydroxychloroquine (74%) were the most commonly dispensed DMARDs. Patients with no DMARDs dispensed were younger at diagnosis (mean age 52 vs 56 years) and more likely to live in deprived areas (Table). Among patients with an incident diagnosis of RA in the primary care EMR, 383/597 used conventional DMARDs, 169/597 advanced therapies (targeted synthetic or biologic), and 17/597 biologics without conventional DMARDs. These prescriptions spanned the entire follow-up period, with incident RA defined using a 2-year run-in period to account for prevalent cases. Conclusion This study suggests potential disparities in DMARD utilization, with younger patients and those from deprived areas less likely to have DMARD dispensations. However, further research is needed to explore whether these differences reflect true disparities in prescribing practices or other factors such as prescription filling behaviors, concerns about medication costs, or delays in obtaining subsidized drug coverage. Future studies should also investigate the coordination between specialists and primary care providers in RA management. These insights could inform targeted interventions to address potential socioeconomic disparities in RA care and improve treatment access and adherence. Characteristics of patients who received DMARDS compared to those who did not, linked dataset.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".