Patients and methods: This cross-sectional study included 167 SLE patients (21 males, 146 females; mean age 38.2±9.8 years; range, 20 to 60 years) recruited from the outpatient Rheumatology and Nephrology clinics. Face-to-face interviews were conducted to record patients' socio- demographics (age, sex, ethnicity, marital status, and occupation) and SLE disease characteristics (system involvement, age onset, and presence of organ damage). Disease activity was assessed using the Systemic Lupus Erythematosus Disease Activity Index-2000 (SLEDAI-2K). Short form 36 (SF-36) was used to determine health-related quality of life (HRQoL) while Work Productivity and Activity Impairment (WPAI) questionnaire was used to assess the four domains of absenteeism, presenteeism, overall work productivity, and non-work related ADL impairment. Univariate analyses and multivariable regression analysis examined the association of demographic variables, SLE disease characteristics, and activity with reduced HRQoL and WPAI scores.

Results: The majority of the patients were Malays (59.3%), followed by Chinese (34.7%) and Indian (3.6%) patients. More than two-thirds of the patients reported some degree of impairment in their work productivity and ADL due to the disease. The absenteeism rate was 10.4% in the past one week and their indirect costs were 2,875.17 Malaysian ringgits (US $701.22) in the past seven days. Significant predictors of higher work productivity and ADL impairment scores were higher disease activity, more frequent SLE flares, lupus nephritis, and hematological involvement of SLE. Patients with higher work productivity and ADL impairment scores were also strongly associated with poor QoL. No ethnic disparities of work productivity and ADL impairment were found.

METHODS: Demographics, diagnosis, comorbidities, disease activity, treatments and PROMIS instrument data were analysed. Primary outcomes were PROMIS Global Physical Health (GPH) and Global Mental Health (GMH) scores. Factors affecting GPH and GMH scores in IIMs were identified using multivariable regression analysis.

METHODS: The first and second COVAD patient self-reported e-surveys were circulated from March to December 2021, and February to June 2022 (ongoing). We collected data on demographics, comorbidities, COVID-19 infection and vaccination history, reasons for hesitancy, and patient reported outcomes. Predictors of hesitancy were analysed using regression models in different groups.

METHODS: The COVAD surveys were used to extract data on flare demographics, comorbidities, COVID-19 history, and vaccination details for patients with AIRDs. Flares following vaccination were identified as patient-reported (a), increased immunosuppression (b), clinical exacerbations (c) and worsening of PROMIS scores (d). We studied flare characteristics and used regression models to differentiate flares among various AIRDs.