Rates of Mental Illness and Addiction among High-Cost Users of Medical Services in Ontario
Bibliographic record
Abstract
OBJECTIVE: To quantify the burden of mental illness and addiction among high-costing users of medical services (HCUs) using population-level data from Ontario, and compare to a referent group of nonusers. METHOD: We conducted a population-level cohort study using health administrative data from fiscal year 2011-2012 for all Ontarians with valid health insurance as of April 1, 2011 (N = 10,909,351). Individuals were grouped based on medical costs for hospital, emergency, home, complex continuing, and rehabilitation care in 2011-2012: top 1%, top 2% to 5%, top 6% to 50%, bottom 50%, and a zero-cost nonuser group. The rate of diagnosed psychotic, major mood, and substance use disorders in each group was compared to the zero-cost referent group with adjusted odds ratios (AORs) for age, sex, and socioeconomic status. A sensitivity analysis included anxiety and other disorders. RESULTS: Mental illness and addiction rates increased across cost groups affecting 17.0% of the top 1% of users versus 5.7% of the zero-cost group (AOR, 3.70; 95% confidence interval [CI], 3.59 to 3.81). This finding was most pronounced for psychotic disorders (3.7% vs. 0.7%; AOR, 5.07; 95% CI, 4.77 to 5.38) and persisted for mood disorders (10.0% vs. 3.3%; AOR, 3.52; 95% CI, 3.39 to 3.66) and substance use disorders (7.0% vs. 2.3%; AOR, 3.82; 95% CI, 3.66 to 3.99). When anxiety and other disorders were included, the rate of mental illness was 39.3% in the top 1% compared to 21.3% (AOR, 2.39; 95% CI, 2.34 to 2.45). CONCLUSIONS: A high burden of mental illness and addiction among HCUs warrants its consideration in the design and delivery of services targeting HCUs.
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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 | 0.003 |
| 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 | 0.000 |
| Open science | 0.001 | 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".