Pain and Sedative Medication Use Among Individuals With Inflammatory Bowel Disease: A Nationwide Population‐Based Cohort Study
Bibliographic record
Abstract
BACKGROUND: Individuals with inflammatory bowel disease (IBD) often experience pain, mood disturbances, and sleep disruption, which may lead to greater use of pain-relieving and sedative medications compared with the general population. These are associated with increased mortality, paradoxical worsening of pain, and inappropriate IBD treatment discontinuation. Chronic prescribing and co-prescribing increase the risk of respiratory depression, dependence, and overdose. METHODS: Using Clinical Practice Research Datalink, a large nationally representative dataset, we examined the annual prevalence of total, chronic (> 90 days opioids; > 28 days sedatives), and co-prescribed opioids, gabapentinoids and sedatives in adults with incident IBD from January 2010 to December 2019. Multivariable regression identified predictors of chronic or co-prescribing. RESULTS: Among 17,388 individuals, over 20% were prescribed a pain or sedative medication each year. Annual prevalence for opioids and gabapentinoids increased (13.6%-14% and 2.5%-5.6%, respectively) while sedative prevalence remained stable (8.4%). Chronic prescribing increased for strong opioids (3.6%-4.6%), weak opioids (3.6%-3.7%) and sedatives (4.2%-4.4%). Between 4.2% and 6.9% of individuals per year were co-prescribed opioids, gabapentinoids, and/or sedatives. Female sex, smoking, older age at diagnosis, Crohn's disease, and a diagnosis of inflammatory arthropathy, irritable bowel syndrome, fibromyalgia, or anxiety/depression were significantly associated with chronic and/or co-prescriptions of opioids or sedatives. CONCLUSION: A substantial proportion of individuals with IBD are prescribed pain and sedative medications, including long-term and co-prescriptions. Identifying high-risk patients is essential to ensure they are prioritised for limited resources, such as psychological therapies, as alternatives to harmful prescriptions.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".