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 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 | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".