The Effect of Everyday Discrimination on Systemic Lupus Erythematosus Disease Activity and Mental Health Outcomes
Bibliographic record
Abstract
OBJECTIVE: Systemic lupus erythematosus (SLE) disproportionately affects racial and ethnic minority populations in the United States. Though discrimination is linked to worse SLE outcomes, the mechanisms of this association remain underexplored. Among a multiracial, multiethnic cohort with SLE, we explored relationships between discrimination and patient-reported outcomes (PROs) and if mental health affects these relationships. METHODS: We analyzed data from the California Lupus Epidemiology Study, comprising Asian (31%), Black (10%), Hispanic (24%), and White (34%) participants (N = 245). Participants completed the Everyday Discrimination Scale (EDS) and the following PROs: Systemic Lupus Activity Questionnaire (SLAQ), Patient-Reported Outcome Measurement Information System (PROMIS) Pain Interference and Fatigue scales, 8-item Patient Health Questionnaire (PHQ-8) for depression and 7-item Generalized Anxiety Disorder (GAD-7) for anxiety. Multivariable linear regressions modeled associations between discrimination and PROs. Mediation analyses evaluated if depression or anxiety mediated associations between discrimination and SLAQ, pain, or fatigue. RESULTS: = 0.007). By racial and ethnic subgroups, discrimination was significantly associated with higher SLAQ among Black and Asian participants. Discrimination was no longer significantly associated with SLAQ, pain, or fatigue after adjusting for PHQ-8 or GAD-7. CONCLUSION: In this diverse SLE cohort, discrimination was associated with greater patient-reported disease activity, pain, fatigue, depression, and anxiety. Mediation analyses suggest that mental health mediates the relationship between discrimination and PROs. These results highlight the effect of discrimination as a psychosocial stressor on disease outcome variables.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".