Social determinants of depression in systemic lupus erythematosus: A systematic scoping review

Abstract

Social determinants of health (SDOH) influence inequities in systemic lupus erythematosus (SLE). While these inequities contribute to overall disease experience, there is little consensus guiding our understanding of the psychological implications of SDOH in SLE. Given the paucity of evidence in this area, the aim of this scoping review was to systematically assess the volume and features of available research literature on associations of SDOH with depression in SLE over the past 20 years, from 1 January 2000 to 16 November 2021. We developed a search strategy for PubMed and EMBASE that included keywords for depression and lupus. After screening 2188 articles, we identified 22 original articles that met our inclusion criteria. At least one SDOH was associated with depression in two of the six studies with unadjusted estimates and 13 of the 16 studies with adjusted estimates. Results provide consistent but sparse evidence that SDOH are associated with depression in SLE. Additionally, depression epidemiology in SLE may differ from the general population such that depression risk is more similar across genders and racial/ethnic groups. More work is needed to identify the SDOH that have the greatest impact on depression and mental health among SLE patients, as well as how and when to intervene.

SVM-Based Model Combining Patients’ Reported Outcomes and Lymphocyte Phenotypes of Depression in Systemic Lupus Erythematosus

“…Therefore, a depression risk prediction model should be constructed in conjunction with multidimensional factors to improve its sensitivity and specificity. In recent years, many socioeconomic factors and other patient-reported indicators (such as educational level, income, fatigue, disease duration and so on) have been observed to be significantly correlated with SLE depression [12,13], which will provide helpful information for model construction. However, there are still some limitations, such as a lack of objective parameters and optimized modeling methods.…”