Twelve-Month Health Care Use and Mortality in Commercially Insured Young People With Incident Psychosis in the United States
Bibliographic record
Abstract
Objective: To assess 12-month mortality and patterns of outpatient and inpatient treatment among young people experiencing an incident episode of psychosis in the United States. Method: Prospective observational analysis of a population-based cohort of commercially insured individuals aged 16-30 receiving a first observed (index) diagnosis of psychosis in 2008-2009. Data come from the US Department of Health and Human Services' Multi-Payer Claims Database Pilot. Outcomes are all-cause mortality identified via the Social Security Administration's full Death Master File; and inpatient, outpatient, and psychopharmacologic treatment based on health insurance claims data. Outcomes are assessed for the year after the index diagnosis. Results: Twelve-month mortality after the index psychosis diagnosis was 1968 per 100000 under our most conservative assumptions, some 24 times greater than in the general US population aged 16-30; and up to 7372 per 100000, some 89 times the corresponding general population rate. In the year after index, 61% of the cohort filled no antipsychotic prescriptions and 41% received no individual psychotherapy. Nearly two-thirds (62%) of the cohort had at least one hospitalization and/or one emergency department visit during the initial year of care. Conclusions: The hugely elevated mortality observed here underscores that young people experiencing psychosis warrant intensive clinical attention-yet we found low rates of pharmacotherapy and limited use of psychosocial treatment. These patterns reinforce the importance of providing coordinated, proactive treatment for young people with psychosis in US community settings.
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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 | 0.000 |
| 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) | 0.001 | 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".