Prevalence and Risk Factors of Substance Use Disorder in Rheumatoid Arthritis
Bibliographic record
Abstract
OBJECTIVE: In this study, we aimed to determine the lifetime prevalence of substance use disorder (SUD) in a Canadian rheumatoid arthritis (RA) cohort and factors associated with SUD in RA. METHODS: Participants with RA (N = 154) were recruited via rheumatology clinics as part of a larger cohort study of psychiatric comorbidity in immune-mediated inflammatory diseases. SUD is defined as the uncontrolled use of a substance despite the harmful consequences of its use. To identify lifetime SUD, the Structured Clinical Interview for Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition was administered to participants. Participants' sociodemographic and RA clinical characteristics were also assessed. We examined factors associated with lifetime SUD using unadjusted and adjusted logistic regression modeling. RESULTS: Twenty-three (14.9%) of 154 participants with RA met the criteria for a lifetime diagnosis of SUD. The majority of the participants were women, were White, had postsecondary education, and were on a disease-modifying antirheumatic drug. Factors associated with increased odds of SUD were male sex (adjusted odds ratio [aOR]: 3.63, 95% confidence interval [CI]: 1.03-12.73), younger age (aOR: 0.94, 95% CI: 0.90-0.98), and ever smoking (aOR: 6.44, 95% CI: 1.53-27.07). CONCLUSION: We found that approximately 1 in 7 individuals with RA had a lifetime diagnosis of SUD, highlighting the importance of identifying and treating SUD in those with RA. In particular, the following factors were associated with higher odds of SUD: male sex, younger age, and smoking behaviors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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 teacher head, 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".