Increased incidence of psychiatric disorders in immune-mediated inflammatory disease
Bibliographic record
Abstract
OBJECTIVE: Although psychiatric comorbidity is known to be more prevalent in immune-mediated inflammatory diseases (IMID) than in the general population, the incidence of psychiatric comorbidity in IMID is less understood, yet incidence is more relevant for understanding etiology. METHODS: Using population-based administrative (health) data, we conducted a retrospective cohort study over the period 1989-2012 in Manitoba, Canada. We identified 19,572 incident cases of IMID including 6119 persons with inflammatory bowel disease (IBD), 3514 persons with multiple sclerosis (MS), 10,206 persons with rheumatoid arthritis (RA), and 97,727 age-, sex- and geographically-matched controls. After applying validated case definitions, we estimated the incidence of depression, anxiety disorder, bipolar disorder and schizophrenia in each of the study cohorts. Using negative binomial regression models, we tested whether the incidence rate of psychiatric comorbidity was elevated in the individual and combined IMID cohorts versus the matched cohorts, adjusting for sex, age, region of residence, socioeconomic status and year. RESULTS: The relative incidence of depression (incidence rate ratio [IRR] 1.71; 95%CI: 1.64-1.79), anxiety (IRR 1.34; 95%CI: 1.29-1.40), bipolar disorder (IRR 1.68; 95%CI: 1.52-1.85) and schizophrenia (IRR 1.32; 95%CI: 1.03-1.69) were elevated in the IMID cohort. Depression and anxiety affected the MS population more often than the IBD and RA populations. CONCLUSIONS: Individuals with IMID, including IBD, MS and RA are at increased risk of psychiatric comorbidity. This increased risk appears non-specific as it is seen for all three IMIDs and for all psychiatric disorders studied, implying a common underlying biology for psychiatric comorbidity in those with IMID.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".