Prevalence and Risk Factors of Substance Use Disorder in Inflammatory Bowel Disease
Bibliographic record
Abstract
BACKGROUND: Substance use disorders (SUDs) impose a substantial individual and societal burden; however, the prevalence and associated factors in persons with inflammatory bowel disease (IBD) are largely unknown. We evaluated the prevalence and risk factors of SUD in an IBD cohort. METHODS: Inflammatory bowel disease participants (n = 247) were recruited via hospital- and community-based gastroenterology clinics, a population-based IBD research registry, and primary care providers as part of a larger cohort study of psychiatric comorbidity in immune-mediated inflammatory diseases. The Structured Clinical Interview for Diagnostic and Statistical Manual of Mental Disorders IV was administered to participants to identify lifetime SUD, anxiety disorder, and major depressive disorder. Additional questionnaires regarding participants' sociodemographic and clinical characteristics were also completed. We examined demographic and clinical factors associated with lifetime SUD using unadjusted and adjusted logistic regression modeling. RESULTS: Forty-one (16.6%) IBD participants met the criteria for a lifetime diagnosis of an SUD. Factors associated with elevated odds of SUD were ever smoking (adjusted odds ratio [aOR], 2.96; 95% confidence interval [CI], 1.17-7.50), male sex (aOR, 2.44; 95% CI, 1.11-5.36), lifetime anxiety disorder (aOR, 2.41; 95% CI, 1.08-5.37), and higher pain impact (aOR, 1.08; 95% CI, 1.01-1.16). CONCLUSIONS: One in six persons with IBD experienced an SUD, suggesting that clinicians should maintain high index of suspicion regarding possible SUD, and inquiries about substance use should be a part of care for IBD patients, particularly for men, smokers, and patients with anxiety disorders and pain.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".