Opioid Use and Healthcare Utilization in Adults With Psoriatic Arthritis and Ankylosing Spondylitis
Bibliographic record
Abstract
OBJECTIVE: Opioid use among individuals with spondyloarthritis is common; however, data on whether these individuals have higher utilization of the healthcare system are lacking. We examined the association between opioid use and healthcare utilization and costs among patients with psoriatic arthritis (PsA) and ankylosing spondylitis (AS). METHODS: We included adults with PsA or AS enrolled in the FORWARD registry, with ≥ 1 completed disease activity or disability questionnaire between 2010 and 2019. The exposure was patient-reported opioid use, and the outcomes were annualized healthcare utilization and prescription costs. We used negative binomial regression to assess the association between opioid use and the utilization outcomes, and generalized linear models with γ-distribution and log link function for the association between opioid use and costs. Models were adjusted for age, sex, and hospitalization. AS and PsA were studied separately. RESULTS: Among 828 patients with PsA, 21.4% used opioids; for those with AS, 27.2% of 334 patients reported opioid use. Opioid users had higher healthcare utilization and costs, including increased medical visits, diagnostic tests, direct medical costs, and pharmacy expenses. Opioid users had 32-33% more medical visits annually vs nonusers. Patients using opioids spent more on medical visits annually compared to nonusers (US $3464 vs $2706 for PsA; US $4500 vs $3660 for AS). CONCLUSION: Compared to patients not using opioids, patients with PsA and AS who used opioids had higher healthcare utilization and higher health-related costs. New care pathways are needed to improve care and reduce costs.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.002 | 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".