Economic insecurities and patient-reported outcomes in patients with systemic lupus erythematosus in the USA: a cross-sectional analysis of data from the California Lupus Epidemiology Study

作者：Donavon Sandoval-Heglund, Eric Roberts, Joon‐Suk Park, Maria Dall’Era, Cristina Lanata, Kamil E. Barbour, Kurt J. Greenlund, Caroline Gordon, Patricia Katz, Jinoos Yazdany · 发表于：The Lancet Rheumatology · 年份：2023 · DOI：10.1016/s2665-9913(23)00296-5 · 被引用次数：17 · 研究领域：Systemic Lupus Erythematosus Research、Rheumatoid Arthritis Research and Therapies、Spondyloarthritis Studies and Treatments

BACKGROUND: Social determinants of health are consistently associated with systemic lupus erythematosus (SLE) outcomes. However, social determinants of health are typically measured with conventional socioeconomic status factors such as income or education. We assessed the association of economic insecurities (ie, food, housing, health care, and financial insecurity) with patient-reported outcomes in a cohort of patients with SLE. METHODS: In this cross-sectional analysis, data were derived from the California Lupus Epidemiology Study based in the San Francisco Bay Area, CA, USA. Participants were recruited between Feb 25, 2015, and Jan 10, 2018, from rheumatology clinics. Inclusion criteria were Bay Area residency; oral fluency in English, Spanish, Cantonese, or Mandarin; 18 years or older; ability to provide informed consent; and a physician confirmed SLE diagnosis. Food, housing, health care, and financial economic insecurities were assessed by validated screening tools. Patient-reported outcomes were obtained using PROMIS, Quality of Life in Neurological Disorders (known as Neuro-QoL) Cognitive Function short form, Patient Health Questionnaire (PHQ)-8, and General Anxiety Disorder (GAD)-7 instruments. Poverty was defined as household income of 125% or less of the federal poverty limit. Lower education was defined as less than college-graduate education. The association of economic insecurities with patient-reported outcomes was assessed by multivariable linear regression ...