Hospital-Based Health Care Service Utilization in Persons With HIV With or Without Mental Health and Substance Use Comorbidities
Bibliographic record
Abstract
OBJECTIVES: A few large-scale studies have shown that among persons with HIV (PWH), mental health and substance use comorbidities can increase the likelihood of hospital-based treatment. However, there has no evidence about the reasons for the increases in hospital-based health care utilization and the associated costs. We hypothesized that MHS+ (with) will have increased rates of hospital-based treatment utilization compared with MHS- (without), which will result in higher costs. METHODS: A validated claims-based case definition was used to identify PWH with MHS+ and MHS- as of March 31, 2020. Hospital and emergency department (ED) utilization were assessed over a 3-year follow-up period with respect to visit frequency, duration, reasons, and costs between MHS+ and MHS-. RESULTS: MHS+ visited the ED or hospital (78%) more often than MHS- (48%; P < 0.01). This resulted in an average hospitalization cost of $58,190 (98,361) in MHS+ compared with $35,031 (64,203) in MHS- ( P < 0.01). Higher visit rates in MHS+ compared with MHS- included increased ED visits for mental health-related reasons (28% vs. 4%), physical conditions typically treated in ambulatory care (30% vs. 14%), physical trauma (45% vs. 19%), and medication complications (19% vs. 3%). ED admissions after acute mental health crises because of self-harm or suicide attempts were rare in both groups for less than 2% of cases. CONCLUSIONS: Our outcomes highlight the significant burden of health care utilization and costs in MHS+ compared with MHS- among PWH. It emphasizes the need for targeted interventions to manage mental health and substance use comorbidities, potentially reducing health care utilization and associated costs among this population.
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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 | 0.002 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".