The Impact of Psychiatric Comorbidity on Health Care Utilization in Inflammatory Bowel Disease: A Population-based Study
Bibliographic record
Abstract
BACKGROUND: Inflammatory bowel disease (IBD) is associated with an increase in psychiatric comorbidity (PC) compared with the general population. We aimed to determine the impact of PC on health care utilization in persons with IBD. METHODS: We applied a validated administrative definition of IBD to identify all Manitobans with IBD from April 1, 2006, to March 31, 2016, and a matched cohort without IBD. A validated definition for PC in IBD population was applied to both cohorts; active PC status meant ≥2 visits for psychiatric diagnoses within a given year. We examined the association of active PC with physician visits, inpatient hospital days, proportion with inpatient hospitalization, and use of prescription IBD medications in the following year. We tested for the presence of a 2-way interaction between cohort and PC status. RESULTS: Our study matched 8459 persons with IBD to 40,375 controls. On crude analysis, IBD subjects had ≥3.7 additional physician visits, had >1.5 extra hospital days, and used 2.1 more drug types annually than controls. Subjects with active PC had >10 more physician visits, had 3.1 more hospital days, and used >6.3 more drugs. There was a synergistic effect of IBD (vs no IBD) and PC (vs no PC) across psychiatric disorders of around 4%. This synergistic effect was greatest for anxiety (6% [2%, 9%]). After excluding psychiatry-related visits and psychiatry-related hospital stays, there remained an excess health care utilization in persons with IBD and PC. CONCLUSION: Inflammatory bowel disease with PC increases health care utilization compared with matched controls and compared with persons with IBD without PC. Active PC further increases health care utilization.
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 | 0.002 | 0.004 |
| 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".