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

TL;DR

A new tool was developed to identify patients with early inflammatory arthritis who are at high risk of losing their jobs, based on factors like occupation, age, and mental health.

Contribution

The study introduces a risk stratification tool using routinely collected clinical data to predict employment loss in early inflammatory arthritis patients.

Findings

Manual workers had a higher risk of employment loss compared to non-manual workers.

A risk score incorporating age, occupation, musculoskeletal burden, and mental health effectively stratified patients into low, medium, and high-risk groups.

The tool demonstrated good discrimination and calibration, with high-risk patients having nearly 20% employment loss.

Abstract

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.

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.

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%).

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.

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