“What Came First?” <scp>Population‐Based</scp> Evaluation of Health Care Encounters for Depression and Anxiety Before and After Inflammatory Arthritis Diagnosis: Disentangling the Relationship Between Mental Health and Arthritis
Bibliographic record
Abstract
OBJECTIVE: The study objective was to describe patterns of depression and anxiety health care use before and after diagnosis among patients with inflammatory arthritis (IA), namely, ankylosing spondylitis, psoriatic arthritis, and rheumatoid arthritis. METHODS: We used population-based linked administrative health data from British Columbia, Canada, to build a cohort of individuals (≥18 years) with incident IA and individuals without IA ("IA-free controls") matched on age and sex. We computed the proportion of individuals with IA and controls who had one or more depression or one or more anxiety health care encounters and the use of one or more antidepressants or one or more anxiolytics in each yearly interval five years before and after IA diagnosis. We used multivariable logistic regression models to evaluate the association between IA status and aforementioned depression and anxiety health care use outcomes in each yearly interval. RESULTS: A total of 80,238 individuals with IA (62.9% female; mean ± SD age 56.2 ± 16.7 years) and 80,238 IA-free controls (62.9% female; mean ± SD age 56.2 ± 16.6 years) were identified between January 1, 2001, and March 31, 2018. Individuals with IA had significantly increased odds of depression and anxiety health care encounters and dispensation of antidepressants and anxiolytics for each yearly interval before and after diagnosis. Adjusted odds ratios (ORs) were highest in the year immediately before (one or more depression visits: adjusted OR 1.61, 95% confidence interval [CI] 1.55-1.66; one or more anxiolytics: adjusted OR 1.71, 95% CI 1.66-1.77) or after (one or more antidepressants: adjusted OR 1.95, 95% CI 1.89-2.00) IA diagnosis. CONCLUSION: Findings suggest a role for depression and anxiety in characterizing the IA prodrome period and generate hypotheses regarding overlapping biopsychosocial processes that link IA and mental health comorbidities.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".