Examining persistency in the high-cost state among mental health high-cost patients : a population-based analysis
Bibliographic record
Abstract
Background: While most literature on high-cost health care users has evaluated this population as a whole, few studies have focused on high-cost patients with mental illness and whether they persist in the high-cost state. We sought to analyze persistent mental health high-cost patients in depth and determine predictors of persistency in the high-cost state. Methods: We used eight years of longitudinal patient-level population data (2010 to 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 patients, based on the proportion of the study period spent in the high-cost state, as persistent (6 to 8 years), sporadic (1 to 2 years) or moderate (3 to 5 years). We compared characteristics between groups and determined predictors of being a persistent mental health high-cost patient. Results: Among 52,638 mental health high-cost patients, 18,149 (34.5%) were persistent high-cost patients. Persistent mental health high-cost patients had higher mean annual costs of care ($44,714, 95% CI $43,724 to $45,703) compared to sporadic ($31,055, 95% CI $30,359 to 31,751) and moderate ($23,205, 95% CI $22,741 to $23,668) patients, largely due to psychiatric hospitalizations. Persistent mental health high-cost patients were more likely to be female, older, long-term residents, living in low-income or urban areas, or to have comorbidities. The strongest predictors of persistent (v. sporadic) high-cost status were HIV (RRR 4.32, 95% CI 3.08 to 6.06), psychosis (RRR 3.41, 95% CI 3.25 to 3.58), and dementia (RRR 3.21, 95% CI 2.81 to 3.68). Interpretation: Among mental health high-cost patients, persistence in the high-cost state was mainly determined by psychosis and other comorbidities. Quality of care interventions directed at the management of psychosis and multimorbidity, as well as preventive interventions to target patients with mental illness before they become persistent high-cost patients 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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| 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".