POS0951 ASSOCIATION BETWEEN INDIVIDUAL AND COUNTRY-LEVEL SOCIOECONOMIC FACTORS AND HEALTH OUTCOMES IN AXIAL AND PERIPHERAL SPONDYLOARTHRITIS: ANALYSIS OF THE ASAS PERSPA STUDY
Authors: D. Capelusnik, S. S. Zhao, A. Boonen, N. Ziade, C. López-Medina, M. Dougados, E. Nikiphorou, S. Ramiro
Journal: Annals of the Rheumatic Diseases (2021)

Background: Health outcomes in spondyloarthritis (SpA) are largely determined by socioeconomic (SE) factors, leading to the great inequity observed between countries across the world. However, the impact of these SE factors on health outcomes across the different SpA phenotypes (axSpA, pSpA and PsA), is less well known. Objectives: To investigate (1) the association between individual and country-level SE factors and health outcomes in different SpA phenotypes, and (2) to explore whether any effect of these SE factors is mediated by the use of b/tsDMARD therapy. Methods: Patients with axSpA, pSpA or PsA from the multinational cohort ASAS-perSpA were included in the analysis. The effect of individual (age, gender, education and marital status) and country-level SE factors (Gross Domestic Product [GDP], Healthcare Expenditure [HCE], Human Development Index [HDI], Gini Index) over health outcomes (ASDAS≥2.1, continuous ASDAS, BASFI, fatigue and ASAS-HI) were assessed in multivariable mixed-effects logistic and linear regression models (as appropriate), adjusting for confounders. Interactions between each individual and country-level SE factors and disease phenotype and between both levels of SE factors, were tested. Finally, a mediation analysis was conducted to explore whether the impact of country-level SE factors on ASDAS is mediated through b/tsDMARD uptake. Results: A total of 4185 patients from 23 countries were included: 61% males, mean age 45 (SD 14), 65% axSpA, 10% pSpA and 25% PsA. Female gender, lower educational level and marital status (single vs married) were associated with higher ASDAS, without significant differences across disease phenotype. Living in lower-(vs higher) GDP countries was also associated with higher ASDAS (β=0.39 [95%CI 0.16; 0.63], with similar results for other economic indicators (Figure 1). 7% of the association between GDP and ASDAS was mediated by b/tsDMARD uptake. The above-mentioned individual and country-level SE factors remained significant to discriminate active disease (ASDAS≥2.1), with greater impact of gender (OR=1.32 [1.13; 1.54]), educational level (primary vs university OR=1.76 [1.40;2.20]) and lower GDP (OR= 1.74 [1.22;2.46]). Higher BASFI was also associated with gender (female vs male: β=0.12 [0.01; 0.24]), lower education (primary vs university: β=0.29 [0.11; 0.46], and marital status (single vs married: β=0.23 [0.09; 0.38]), without effect of country-level SE factors, and no differences across SpA phenotype. Gender and lower educational level were similarly associated with worse ASAS-HI scores (female vs male β=0.88 [0.68;1.09], and primary vs university β=0.61 [0.31;0.91]), while more fatigue was only associated with female gender and, in an opposite direction, with higher country-level SE factors (Figure 1). No interactions were found between individual and country-level SE factors for any of the outcomes. Conclusion: Individual (female gender and lower education) and country-level SE factors are independently associated with higher disease activity in SpA. Uptake of b/tsDMARD had a small mediating effect on the association between GDP and ASDAS. Lower education and female gender are also associated with worse outcomes of functional disability, global functioning and fatigue. Country-level SE factors are not associated with functional disability or global functioning; in contrast, there is a paradoxical effect with fatigue: living in a country with a higher SE status is independently associated with higher levels of fatigue. Management of disease outcome in SpA requires also awareness of the role of individual and country level SE-factors. Figure 1. Effect of individual and country-level socioeconomic factors on ASDAS and fatigue, derived from multivariable mixed-effects models adjusted by clinical confounders. Disclosure of Interests: Dafne Capelusnik Speakers bureau: BMS, Grant/research support from: Pfizer, Sizheng Steven Zhao: None declared, Annelies Boonen: None declared, Nelly Ziade Speakers bureau: Roche, Abbvie, Eli Lilly, Pfizer, Janssen, Novartis, Pierre Fabre, Apotex, Pharmaline, Paid instructor for: Abbvie, Eli Lilly, Sanofi-Aventis, Pfizer, Janssen, Consultant of: Roche, Abbvie, Eli Lilly, Pfizer, Janssen, Novartis, Gilead, NewBridge, Grant/research support from: Abbvie, NewBridge, Algorithm/Celgene, Clementina López-Medina: None declared, Maxime Dougados: None declared, Elena Nikiphorou Speakers bureau: Pfizer, Lilly, AbbVie, Sofia Ramiro Speakers bureau: Lilly, MSD, Novartis, UCB, Consultant of: AbbVie, Lilly, MSD, Novartis, UCB, Sanofi, Grant/research support from: MSD