P163 Predictors of work impairment and absence amongst people with psoriatic arthritis (PsA): evidence from the British Society for Rheumatology PsA Register (BSR-PsA)
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,016 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,005 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».