MENTAL HEALTH DIAGNOSES AND ACUTE CARE USE AMONG INDIVIDUALS WITH SYSTEMIC LUPUS ERYTHEMATOSUS IN THE ALL OF US RESEARCH PROGRAM
Bibliographic record
Abstract
PV080 / #137 Poster Topic: AS11 - Epidemiology and Public Health Background/Purpose Mental health disorders, including depression, anxiety, and post-traumatic stress disorder, are prevalent among people living with SLE. We hypothesized that these mental health conditions may increase recurrent acute care use (emergency department [ED] visits, hospitalizations) among patients with SLE. Methods We used data from the nationwide All of Us Research Program (version 7), an NIH cohort of >287,000 U.S. adults who enrolled and consented for linkage to their electronic health records. We identified those with ≥ 2 ICD-10 or SNOMED codes for SLE in the ≤ 2 years preenrollment. We assessed the exposure of concomitant mental health diagnoses during that time, identified by ≥ 2 ICD-10 or SNOMED codes for major depression, anxiety, or PTSD (mental health diagnoses) in the ≤ 2 years preenrollment. The outcome was number of emergency department visits (only) and hospitalizations (including those from emergency department) after All of Us enrollment date. We used multivariable negative binomial regression models to examine associations between having a mental health diagnosis and acute care use. Models were adjusted for age, sex, race, ethnicity, calendar year of enrollment, other baseline period sociodemographic factors and comorbidities. Results We identified 1,683 with SLE, among whom 1,029 (61.1%) had depression, anxiety, and/or PTSD. Mean age overall was 49.45 (14.26) and 89.4% were female. (Table 1) Those with diagnoses of ≥ 1 mental health condition were more likely to be less educated, in a low-income group, to have ever smoked, and to have more baseline comorbidities. Patients were followed for a mean of 29.9 months (SD 15.3) after enrollment. In adjusted analyses (Table 2), we found associations between having mental health diagnoses and higher rates of acute care use, both emergency department visits (adjusted IRR 1.84, 95% CI 1.52-2.22) and hospitalizations (IRR 1.40, 95% CI 1.11-1.78). This was true both for all emergency visits and hospitalizations for SLE (IRR 1.61, 95% CI 1.32-1.97) and for all diagnoses (IRR 1.45, 95% CI 1.19, 1.78). Table 1. Characteristics of the Patients with SLE in the All of Us Research Program (version 7) by presence of Concomitant Mental Health Diagnoses Table 2. Incidence Rate Ratios for Acute Care Use for Patients with Mental Health Conditions compared to those without among Patients with Systemic Lupus Erythematosus in the All of Us Research Program, v.7 (14,680 person-years) Conclusions In this large, diverse US-wide population of patients with SLE, we found that those with mental health diagnoses had higher rates of recurrent acute care use compared to those without these diagnoses. Mental health conditions may complicate the treatment and severity of SLE, leading to increased recurrent acute care use. As patients with frequent acute care use are less likely to receive standard-of-care long-term medications and preventive care, contributing to inequities, further research is needed to develop interventions to decrease this recurrent acute care use for patients with SLE and mental health disorders.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".