P163 Predictors of work impairment and absence amongst people with psoriatic arthritis (PsA): evidence from the British Society for Rheumatology PsA Register (BSR-PsA)
Bibliographic record
Abstract
Abstract Background/Aims People with psoriatic arthritis (PsA) often report that their condition affects their ability to work. Work impairment, absence and leaving work reduce quality of life and have personal and societal economic consequences. Identifying modifiable risk factors for poor work outcomes can help inform early interventions. This study aimed to determine predictors of future absence and work impairment among people with PsA. Methods This analysis includes people participating in the BSR-PsA register (August 2024 data release), who were of working age at recruitment and reported work-related information. Work absence and impairment were assessed using the Work Productivity and Activity Impairment Questionnaire (WPAI). Generalised estimating equation models were used to identify predictors of absence and work impairment 12 months later among those who remained working, adjusted for age, sex, and deprivation. Potential predictors of work outcomes included clinical factors and patient-reported measures. Forward stepwise regression was used to generate best-fitting multivariable models. Results At baseline, 955 people reported work information, of whom 747 (78%) were working: 51% were female, with a median age of 47 years and time since diagnosis of 4 years, while 38% were about to start a new biologic or tsDMARD. Amongst the 655 who completed the WPAI, with reference to the previous week, 22% reported work absence and 77% work impairment (median % impairment while working due to PsA: 20%; IQR: 10%-50%). The likelihood of working at baseline (adjusted for age, sex, and deprivation) was lower amongst people with comorbidities (OR: 0.76; 95% CI: 0.67-0.87; per additional comorbidity), current smokers (vs. never) (0.31; 0.19-0.49), and those with more impactful disease (PsAID, range: 0-10) (0.73; 0.68-0.80; per unit increase). Of 218 participants working at baseline with available data at 12-month follow-up, 13 (6%) reported leaving work. Among those who remained in work, participants with higher baseline PsAID, activity impairment outside of work, sleep problems, fatigue, poorer physical and mental health and severity of fibromyalgia symptoms were at increased risk of absence and experienced greater work impairment in the week prior. Multivariable models suggested that fatigue (IRR: 1.05; 95% CI: 1.00-1.10; per unit increase) and activity impairment (1.37; 1.19-1.58; per 10 percentage points increase) independently predicted future absence. Fatigue (β: 0.47; p < 0.05) and activity impairment (2.33; p < 0.05) also independently predicted future work impairment. Commencing biologics (-6.18; p = 0.05) and better physical health (-1.62; p < 0.05; per unit increase) were independently associated with reduced future work impairment. Conclusion Managing disease activity with biological treatment reduces work impairment, but improving physical health capacity to carry out daily activities, and impactful symptoms of fatigue, will be important to reduce work impairment further. This requires additional non-pharmacological approaches. Disclosure L. Xu: None. G.T. Jones: None. O. Rotariu: None. P.S. Helliwell: Member of speakers’ bureau; Novartis. S. Siebert: Honoraria; AbbVie, Amgen, AstraZeneca, Janssen, Syncona, Teijin Pharma, UCB. Member of speakers’ bureau; AbbVie, GSK, Janssen, Novartis, Pfizer, UCB. Grants/research support; Eli Lilly, GSK, Janssen, UCB. L. Kay: None. R.J. Hollick: None. G.J. Macfarlane: None.
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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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".