Psychiatric Comorbidity Does Not Enhance Prescription Opioid Use in Inflammatory Bowel Disease as It Does in the General Population
Bibliographic record
Abstract
INTRODUCTION: Little is known about patterns of opioid prescribing in inflammatory bowel disease (IBD), but pain is common in persons with IBD. We estimated the incidence and prevalence of opioid use in adults with IBD and an unaffected reference cohort and assessed factors that modified opioid use. METHODS: Using population-based health administrative data from Manitoba, Canada, we identified 5233 persons with incident IBD and 26 150 persons without IBD matched 5:1 on sex, birth year, and region from 1997 to 2016. New and prevalent opioid prescription dispensations were quantified, and patterns related to duration of use were identified. Generalized linear models were used to test the association between IBD, psychiatric comorbidity, and opioid use adjusting for sociodemographic characteristics, physical comorbidities, and healthcare use. RESULTS: Opioids were dispensed to 27% of persons with IBD and to 12.9% of the unaffected reference cohort. The unadjusted crude incidence per 1000 person-years of opioid use was nearly twice as high for the IBD cohort (88.63; 95% CI, 82.73-91.99) vs the reference cohort (45.02; 95% CI, 43.49-45.82; relative risk 1.97; 95% CI, 1.86-2.08). The incidence rate per 1000 person-years was highest in those 18-44 years at diagnosis (98.01; 95% CI, 91.45-104.57). The relative increase in opioid use by persons with IBD compared to reference cohort was lower among persons with psychiatric comorbidity relative to the increased opioid use among persons with IBD and reference cohort without psychiatric comorbidity. DISCUSSION: The use of opioids is more common in people with IBD than in people without IBD. This does not appear to be driven by psychiatric comorbidity.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".