The Effect of Psychiatric Comorbidity on Healthcare Utilization for Youth With Newly Diagnosed Systemic Lupus Erythematosus
Bibliographic record
Abstract
OBJECTIVE: To examine the effect of psychiatric diagnoses on healthcare use in youth with systemic lupus erythematosus (SLE) during their first year of SLE care. METHODS: We conducted a retrospective cohort study using claims from 2000 to 2013 from Clinformatics Data Mart (OptumInsight). Youth aged 10 years to 24 years with an incident diagnosis of SLE (≥ 3 International Classification of Diseases, 9th revision, codes for SLE 710.0, > 30 days apart) were categorized as having: (1) a preceding psychiatric diagnosis in the year before SLE diagnosis, (2) an incident psychiatric diagnosis in the year after SLE diagnosis, or (3) no psychiatric diagnosis. We compared ambulatory, emergency, and inpatient visits in the year after SLE diagnosis, stratified by nonpsychiatric and psychiatric visits. We examined the effect of childhood-onset vs adult-onset SLE by testing for an interaction between age and psychiatric exposure on outcome. RESULTS: We identified 650 youth with an incident diagnosis of SLE, of which 122 (19%) had a preceding psychiatric diagnosis and 105 (16%) had an incident psychiatric diagnosis. Compared with those without a psychiatric diagnosis, youth with SLE and a preceding or incident psychiatric diagnosis had more healthcare use across both ambulatory and emergency settings for both nonpsychiatric and psychiatric-related care. These associations were minimally affected by age at time of SLE diagnosis. CONCLUSION: Psychiatric comorbidity is common among youth with newly diagnosed SLE and is associated with greater healthcare use. Interventions to address preceding and incident psychiatric comorbidity may decrease healthcare burden for youth with SLE.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".