Lower education and living in countries with lower wealth are associated with higher disease activity in rheumatoid arthritis: results from the multinational COMORA study

Objectives To investigate the relationship of socioeconomic status (SES) on an individual and country level with disease activity in rheumatoid arthritis (RA) and explore the mediating role of uptake of costly biological disease-modifying antirheumatic drugs (bDMARDs) in this relationship. Methods Data from a cross-sectional multinational study (COMOrbidities in RA) were used. Contribution of individual socioeconomic factors and country of residence to disease activity score with 28-joint assessment (DAS28) was explored in regression models, adjusting for relevant clinical confounders. Next, country of residence was replaced by gross domestic product (GDP) (low vs high) to investigate the contribution of SES by comparing R2 (model fit). The mediating role of uptake of bDMARDs in the relationship between education or GDP and DAS28 was explored by testing indirect effects. Results In total, 3920 patients with RA were included (mean age 56 (SD 13) years, 82% women, mean DAS28 3.7 (1.6)). After adjustment, women (vs men) and low-educated (vs university) patients had 0.35 higher DAS28. Adjusted country differences in DAS28, compared with the Netherlands (lowest DAS28), varied from +0.2 (France) to +2.4 (Egypt). Patients from low GDP countries had 0.98 higher DAS28. No interactions between individual-level and country-level variables were observed. A small mediation effect of uptake of bDMARDs in the relationship between education and DAS28 (7.7%) and between GDP and DAS28 (6.7%) was observed. Conclusions Female gender and lower individual or country SES were independently associated with DAS28, but did not reinforce each other. The association between lower individual SES (education) or lower country welfare (GDP) with higher DAS28 was partially mediated by uptake of bDMARDs.

[1]  H. Sayles,et al.  Validation of the Rheumatic Disease Comorbidity Index , 2015, Arthritis care & research.

[2]  J. Katz,et al.  Sociodemographic, Disease, Health System, and Contextual Factors Affecting the Initiation of Biologic Agents in Rheumatoid Arthritis: A Longitudinal Study , 2014, Arthritis care & research.

[3]  Jonathan Kay,et al.  Prevalence of comorbidities in rheumatoid arthritis and evaluation of their monitoring: results of an international, cross-sectional study (COMORA) , 2013, Annals of the rheumatic diseases.

[4]  A. Boonen,et al.  Variations in criteria regulating treatment with reimbursed biologic DMARDs across European countries. Are differences related to country's wealth? , 2013, Annals of the rheumatic diseases.

[5]  M. Mckee,et al.  The unequal health of Europeans: successes and failures of policies , 2013, The Lancet.

[6]  A. Boonen,et al.  Inequities in access to biologic and synthetic DMARDs across 46 European countries , 2013, Annals of the rheumatic diseases.

[7]  A. Boonen,et al.  Impact of socioeconomic gradients within and between countries on health of patients with rheumatoid arthritis (RA): lessons from QUEST RA. , 2012, Best practice & research. Clinical rheumatology.

[8]  M. Marmot,et al.  WHO European review of social determinants of health and the health divide , 2012, The Lancet.

[9]  E. Soriano,et al.  Early rheumatoid arthritis in Latin America: Low socioeconomic status related to high disease activity at baseline , 2012, Arthritis care & research.

[10]  F. Wolfe,et al.  Chronic Conditions and Health Problems in Rheumatic Diseases: Comparisons with Rheumatoid Arthritis, Noninflammatory Rheumatic Disorders, Systemic Lupus Erythematosus, and Fibromyalgia , 2010, The Journal of Rheumatology.

[11]  B. Bresnihan,et al.  Disparities in rheumatoid arthritis disease activity according to gross domestic product in 25 countries in the QUEST–RA database , 2009, Annals of the rheumatic diseases.

[12]  Kristopher J Preacher,et al.  Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models , 2008, Behavior research methods.

[13]  D. Symmons Environmental factors and the outcome of rheumatoid arthritis. , 2003, Best practice & research. Clinical rheumatology.

[14]  M. Whitehead,et al.  Developing the Policy Response to Inequities in Health: A Global Perspective , 2001 .

[15]  A. Astrup,et al.  Obesity : Preventing and managing the global epidemic , 2000 .

[16]  J. Jordan Effect of race and ethnicity on outcomes in arthritis and rheumatic conditions. , 1999, Current opinion in rheumatology.

[17]  P. van Riel,et al.  Validation of rheumatoid arthritis improvement criteria that include simplified joint counts. , 1998, Arthritis and rheumatism.

[18]  A. R. Frisancho Physical Status: The Use and Interpretation of Anthropometry , 1996, The American Journal of Clinical Nutrition.

[19]  T. Pincus,et al.  Formal education level as a significant marker of clinical status in rheumatoid arthritis. , 1988, Arthritis and rheumatism.

[20]  M. Liang,et al.  The American Rheumatism Association 1987 revised criteria for the classification of rheumatoid arthritis. , 1988, Arthritis and rheumatism.

[21]  M. Prevoo,et al.  Development and validation of the European League Against Rheumatism response criteria for rheumatoid arthritis. Comparison with the preliminary American College of Rheumatology and the World Health Organization/International League Against Rheumatism Criteria. , 1996, Arthritis and rheumatism.

[22]  P. B. Eveleth,et al.  Physical Status: The Use and Interpretation of Anthropometry. Report of a WHO Expert Committee , 1996 .

[23]  M. Prevoo,et al.  Modified disease activity scores that include twenty-eight-joint counts. Development and validation in a prospective longitudinal study of patients with rheumatoid arthritis. , 1995, Arthritis and rheumatism.