Development of a clinical tool to identify patients with early inflammatory arthritis at high risk of employment loss: analysis from the National Early Inflammatory Arthritis Audit.

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.