Disparities in Psychiatric Diagnosis and Treatment for Youth with Systemic Lupus Erythematosus: Analysis of a National US Medicaid Sample
Bibliographic record
Abstract
OBJECTIVE: To estimate the national prevalence and racial/ethnic differences in psychiatric diagnoses and pharmacologic treatment in a US Medicaid beneficiary population of youth with systemic lupus erythematosus (SLE). METHODS: We included youth aged 10 to 18 years with a diagnosis of SLE (defined as ≥ 3 outpatient visit claims with an International Classification of Diseases, 9th ed. code of 710.0, each > 30 days apart) in the US Medicaid Analytic Extract database from 2006 and 2007. This database contains all inpatient and outpatient Medicaid claims from 49 states and the District of Columbia. We calculated the prevalence of psychiatric diagnoses and treatment, and used logistic regression to compare depression and anxiety diagnoses, antidepressant, and anxiolytic use among racial/ethnic groups. RESULTS: Of 970 youth with SLE, 15% were white, 42% were African American, 27% were Latino, and 16% were of other races/ethnicities. Diagnoses of depression were present for 19%, anxiety for 7%, acute stress/adjustment for 6%, and other psychiatric disorders for 18%. Twenty percent were prescribed antidepressants, 7% were prescribed anxiolytics, 6% were prescribed antipsychotics, and 5% were prescribed stimulants. In adjusted analyses, African Americans were less likely than whites to be diagnosed with depression (OR 0.56, 95% CI 0.34-0.90) or anxiety (OR 0.49, 95% CI 0.25-0.98), or to be prescribed anxiolytics (OR 0.23, 95% CI 0.11-0.48). CONCLUSION: We present population-level estimates showing high psychiatric morbidity in youth with SLE, but less prevalent diagnosis and treatment in African Americans. Mental health interventions should address potential racial/ethnic disparities in care.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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 teacher head, 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".