Disparities in Lupus and the Role of Social Determinants of Health: Current State of Knowledge and Directions for Future Research
Bibliographic record
Abstract
Systemic lupus erythematosus (SLE) is a chronic autoimmune disease. The complex relationships between race and ethnicity and social determinants of health (SDOH) in influencing SLE and its course are increasingly appreciated. Multiple SDOH have been strongly associated with lupus incidence and outcomes and contribute to health disparities in lupus. Measures of socioeconomic status, including economic instability, poverty, unemployment, and food insecurity, as well as features of the neighborhood and built environment, including lack of safe and affordable housing, crime, stress, racial segregation, and discrimination, are associated with race and ethnicity in the US and are risk factors for poor outcomes in lupus. In this scientific statement, we aimed to summarize current evidence on the role of SDOH in relation to racial and ethnic disparities in SLE and SLE outcomes, primarily as experienced in the U.S. Lupus Foundation of America's Health Disparities Advisory Panel, comprising 10 health disparity experts, including academic researchers and patients, who met 12 times over the course of 18 months in assembling and reviewing the data for this study. Sources included articles published from 2011 to 2023 in PubMed, Centers for Disease Control and Prevention data, and bibliographies and recommendations. Search terms included lupus, race, ethnicity, and SDOH domains. Data were extracted and synthesized into this scientific statement. Poorer neighborhoods correlate with increased damage, reduced care, and stress-induced lupus flares. Large disparities in health care affordability, accessibility, and acceptability exist in the US, varying by region, insurance status, and racial and minority groups. Preliminary interventions targeted social support, depression, and shared-decision-making, but more research and intervention implementation and evaluation are needed. Disparities in lupus across racial and ethnic groups in the US are driven by SDOH, some of which are more easily remediable than others. A multidimensional and multidisciplinary approach involving various stakeholder groups is needed to address these complex challenges, address these diminish disparities, and improve outcomes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".