2025/12/20 by E. Alveyn, Katie Bechman, Maryam Adas +12 · 1 voice
Medicine · Health Professions · #Rheumatoid Arthritis Research and Therapies #Spondyloarthritis Studies and Treatments #Workplace Health and Well-being
paper · pdf · doi:10.1093/rap/rkaf149
openalex publication_date 2025/12/20 · openalex created_date 2025/12/24 · openalex updated_date 2026/07/30
Objectives: Work disability is an early consequence of inflammatory arthritis. Preventive interventions exist but access is limited, highlighting the need for risk stratification. We aimed to develop a tool using routinely collected data to identify patients at greatest risk of employment loss. Methods: This cohort study used data from the National Early Inflammatory Arthritis Audit. Patients ≥16 years with early inflammatory arthritis (EIA), enrolled May 2018-April 2025, employed at diagnosis and with three-month follow-up were included. The outcome was self-reported employment loss at 3 months. Predictors were occupation (manual vs non-manual), age, sex, disease activity (DAS28 > 5.1), mental health (anxiety/depression) and musculoskeletal burden (MSKHQ ≤25v > 25). Employment loss was modelled using Poisson regression. Model discrimination, calibration and bootstrap validation were assessed. A risk score was derived and stratified into low, medium and high-risk groups. Results: Of 11 894 patients with EIA, 6036 were employed at baseline and 1662 had complete work-outcome data. At 3 months, 168(10.1%) reported employment loss. Manual workers had higher risk than non-manual (14.1% vs 7.8%). In multivariable analysis, manual work (IRR: 1.54, 95% CI: 1.15-2.06), older age (per 10 years: IRR: 1.65, 95% CI: 1.43-1.90), high musculoskeletal burden (IRR: 1.55, 1.10-2.19) and anxiety/depression (IRR: 1.45, 1.02-2.06) were associated with employment loss, whereas DAS28 was not. The optimal model (age, occupation, musculoskeletal and mental health) showed good discrimination (C-statistic 0.710) and calibration. An 8-point score stratified patients into low (2.5%), medium (9.1%) and high-risk (19.5%). Conclusion: Employment loss in EIA is driven by occupation, age, musculoskeletal symptoms and mental health. A risk tool incorporating these domains can stratify patients and guide targeted interventions.