Impact of Psychiatric Comorbidity on Health Care Use in Rheumatoid Arthritis: A Population‐Based Study
Bibliographic record
Abstract
OBJECTIVE: Psychiatric comorbidity is frequent in rheumatoid arthritis (RA) and complicates treatment. The present study was undertaken to describe the impact of psychiatric comorbidity on health care use (utilization) in RA. METHODS: We accessed administrative health data (1984-2016) and identified a prevalent cohort with diagnosed RA. Cases of RA (n = 12,984) were matched for age, sex, and region of residence with 5 controls (CNT) per case (n = 64,510). Within each cohort, we identified psychiatric morbidities (depression, anxiety, bipolar disorder, and schizophrenia [PSYC]), with active PSYC defined as ≥2 visits per year. For the years 2006-2016, annual rates of ambulatory care visits (mean ± SD per person) categorized by provider (family physician [FP], rheumatologist, psychiatrist, other specialist), hospitalization (% of cohort), days of hospitalization (mean ± SD), and dispensed drug types (mean ± SD per person) were compared among 4 groups (CNT, CNT plus PSYC, RA, and RA plus PSYC) using generalized linear models adjusted for age, sex, rural versus urban residence, income quintile, and total comorbidities. Estimated rates are reported with 95% confidence intervals (95% CIs). We tested within-person and RA-PSYC interaction effects. RESULTS: Subjects with RA were mainly female (72%) and urban residents (59%), with a mean ± SD age of 54 ± 16 years. Compared to RA without PSYC, RA with PSYC had more than additive (synergistic) visits (standardized mean difference [SMD] 10.92 [95% CI 10.25, 11.58]), hospitalizations (SMD 13% [95% CI 0.11, 0.14]), and hospital days (SMD 3.63 [95% CI 3.06, 4.19]) and were dispensed 6.85 more medication types (95% CI 6.43, 7.27). Cases of RA plus PSYC had increased visits to FPs (an additional SMD 8.92 [95% CI 8.35, 9.46] visits). PSYC increased utilization in within-person models. CONCLUSION: Managing psychiatric comorbidity effectively may reduce utilization in RA.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".