Characteristics of patients with mental illness and persistent high-cost status: a population-based analysis
Bibliographic record
Abstract
BACKGROUND: Most of the literature on high-cost users of health care has evaluated this population as a whole, but few studies have focused on high-cost patients with mental illness and whether they persist in the high-cost state. We sought to analyze this patient population in depth and determine predictors of persistency in the high-cost state. METHODS: We used 8 years of longitudinal patient-level population data (2010-2017) from Ontario to follow high-cost patients (those in and above the 90th percentile of the cost distribution) with mental illness. We classified high-cost status, based on the proportion of the study period that patients spent in the high-cost state, as persistent (6-8 yr), sporadic (1-2 yr) or moderate (3-5 yr). We compared characteristics between groups and determined predictors of being a patient with mental illness and persistent high-cost status. RESULTS: Among 52 638 patients with mental illness and high-cost status, 18 149 (34.5%) were considered persistent high cost. These patients had higher mean annual costs of care ($44 714, 95% confidence interval [CI] $43 724-$45 703) than patients with sporadic ($23 205, 95% CI $22 741-$23 668) and moderate ($31 055, 95% CI $30 359-31 751) status, largely owing to psychiatric hospital admissions. Patients with mental illness and persistent high-cost status were more likely to be female, older, long-term residents of Ontario (information ascertained from the Immigrants, Refugees and Citizenship Canada Database), living in low-income or urban areas, or to have comorbidities. The strongest predictors of persistent (v. sporadic) high-cost status were HIV (relative risk ratio [RRR] 4.32, 95% CI 3.08-6.06), psychosis (RRR 3.41, 95% CI 3.25-3.58) and dementia (RRR 3.21, 95% CI 2.81-3.68). INTERPRETATION: Among patients with mental illness and high-cost status, persistence in the high-cost state was determined mainly by psychosis and other comorbidities. Quality-of-care interventions directed at managing psychosis and multimorbidity, as well as preventive interventions to target patients with mental illness before they enter the persistent high-cost state, are needed.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".