Socioeconomic Predictors of Incident Depression in Systemic Lupus Erythematosus
Bibliographic record
Abstract
OBJECTIVE: To assess different measures of socioeconomic status (SES) as predictors of incident depression among women with systemic lupus erythematosus (SLE). METHODS: Data were derived from the 2010-2015 waves of the Lupus Outcomes Study, where individuals with confirmed SLE were interviewed annually by telephone. Depression was assessed using the Center for Epidemiologic Studies Depression Scale, using a validated lupus-specific cutoff (≥23) for major depressive disorder. Women interviewed in ≥2 consecutive waves, with scores <23 in the first wave (T1), were included. The level of financial strain was classified as high, moderate, or none based on responses to 3 questions. Generalized estimating equations were used to assess the impact of poverty status, income, education, and financial strain at T1 on the risk of incident depression the next year (T2), with adjustment for sociodemographic and disease status measures. Individuals could contribute more than one 2-year dyad to the analysis. RESULTS: In total, 682 women contributed 2,097 observations, with 19% having high financial strain, 47% moderate strain, and 34% no strain. There were 166 women who had 184 episodes of incident depression (rate = 8.8/100 person-years). In bivariate analysis, poverty, lower income and education, disease activity, and high financial strain were associated with depression onset; race/ethnicity was not. Poverty, income, and education were not significant in multivariate analyses, but disease activity and high financial strain were (odds ratio 1.85 [95% confidence interval 1.06-3.23]). CONCLUSION: High financial strain was a significant predictor of new-onset depression in women with SLE, controlling for disease factors and other SES measures. Determining specific, modifiable sources of financial strain may help prevent the development of depression.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".