The role of mental health and addiction among high-cost patients: a population-based study
Bibliographic record
Abstract
Aims: Previous work found that, among high-cost patients, those with a majority of mental health and addiction (MHA)-related costs (>50%) incur over 30% more costs than other high-cost patients. However, this work did not examine other high-cost patients in depth or whether they had any MHA-related costs. The objective of this analysis was to examine the role of MHA-related care among other high-cost patients. Methods: Using administrative healthcare data from Ontario, Canada, this study selected all patients in the 90th percentile of the cost distribution in 2012. It focused primarily on two groups based on the percentage of MHA-related costs relative to total costs: (1) high-cost patients with some MHA-related costs (0% > and <50%) and (2) high-cost patients with no MHA-related costs (0%). We examined socio-demographic and clinical characteristics, utilization and costs for both groups, and modeled patient-level costs using appropriate regression techniques. We also compared these groups with high-cost patients with a majority of MHA-related costs (>50%). Results: High-cost patients with some MHA-related costs incurred over 40% more costs than those without ($27,883 vs $19,702). Patients with some MHA-related costs were older, lived in poorer neighborhoods, and had higher levels of comorbidity compared to those without. After controlling for relevant variables, having any type of MHA-related utilization increased costs by $2,698. Having a diagnosis of psychosis had a large impact on costs. Limitations: This study did not examine children and adolescents. We were only able to account for 91% of all costs incurred by the public third-party payer; addiction-related costs from community-based agencies were not available. Conclusions: High-cost patients with MHA incur higher costs compared to those without. When considering interventions aimed at high-cost patients, policy-makers should consider their complex nature, specifically both their physical and MHA-related comorbidities.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.002 | 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".