Predictors of depression trajectories during the COVID-19 pandemic in adults with rheumatoid arthritis: results from the Canadian Early Arthritis Cohort
Bibliographic record
Abstract
Objectives: Growing evidence points to mental health impacts of coronavirus disease 2019 (COVID-19). We explored trajectories of depressive symptoms in the year pre- and 2 years post-pandemic onset in adults with RA. Methods: Data are from the Canadian Early Arthritis Cohort (CATCH), a prospective, multicentre, early RA cohort of Canadian adults treated by rheumatologists. Pre-pandemic, participants completed patient-reported outcome measures and rheumatology exams in person. After March 2020, patient-reported outcomes were collected at in-person and virtual visits. We used group-based trajectory modelling to explore longitudinal patterns of depressive symptoms prior to and throughout the COVID-19 pandemic and multinomial regression to identify factors associated with depression trajectory group. Results: A total of 989 participants had a mean age of 60 years (s.d. 14), RA for 6 years (s.d. 4) and were mostly white (84%) and female (73%) with some college education (60%). Most (77%) were in Clinical Disease Activity Index remission/low disease activity prior to the pandemic. We identified four trajectories: resilient (no symptoms throughout: 60%), worsening (none-mild: 22%), improving (mild-minimal: 8%) and persistent (moderate-severe throughout: 9%). Age, sex, race, education, pain, physical and social functioning, fibromyalgia and history of anxiety/depression were associated with different trajectories. Conclusion: Although 60% had a consistent affect during the first 2 years of the pandemic, mood worsened in greater than one in five, suggesting a cumulative impact over time. Biological, psychological and social factors and worse pre-pandemic symptoms and function were associated with a greater risk of depression during the pandemic. Identifying at-risk groups impacted by major stressors like the pandemic may offer new opportunities to personalize treatment, allocate resources, reduce disease flares and improve outcomes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".