Predictors of work disability in rheumatoid arthritis patients

Susan Reisine, Julia Mcquillan, Judith Fifield

Research output: Contribution to journalArticlepeer-review

96 Scopus citations


Objective. To evaluate regression models that include social, attitudinal, work structure, health status, and family characteristics, with regard to their prediction of work disability in a national sample of patients with rheumatoid arthritis (RA). Methods. Four hundred ninety‐eight employed RA patients were recruited from a national sample of private rheumatology practices. Three hundred ninety‐two remained in the study after 5 years. Data were collected from patients by telephone interview, and patients' physicians provided written clinical assessments. Only variables on which information was obtained in year 1 were used to predict work status in year 5, using hierarchical multiple logistic regression analysis. Results. The significant predictors of work disability were age (odds ratio [OR] 1.04), number of deformed joints (OR 1.26), number of joints with flare (OR 1.23), the complexity of working with things at work (OR 0.88), and the desire to remain employed (OR 2.3). The risk of work disability increased with increasing age, more severe disease, greater complexity of involvement with things at work, reduced work hours, and desire to not be working outside the home. Conclusion. The risk of becoming work disabled in 5 years was predicted more by clinical status at entry into the study than by work structure. These results, which contradict previous research on work disability in arthritis, prompt a rethinking of future studies of work disability in RA.

Original languageEnglish (US)
Pages (from-to)1630-1637
Number of pages8
JournalArthritis & Rheumatism
Issue number11
StatePublished - Nov 1995
Externally publishedYes

ASJC Scopus subject areas

  • Immunology and Allergy
  • Rheumatology
  • Immunology
  • Pharmacology (medical)

Fingerprint Dive into the research topics of 'Predictors of work disability in rheumatoid arthritis patients'. Together they form a unique fingerprint.

Cite this