Health Care Expenditures Among Individuals With Chronic Psychotic Disorders in Ontario: An Analysis Over Time
Bibliographic record
Abstract
Chronic psychotic disorders are severe and disabling mental disorders associated with poor psychiatric and medical outcomes, and among the most costly mental disorders to treat. Understanding trends in aggregate health care expenditures over time, and respective drivers, can provide relevant insights for decision makers, namely around appropriate allocation of scarce resources within the health care sector. Using administrative health care times series data from Ontario, this analysis examined trends in aggregate public health care expenditures and activity from 2012 to 2019 among all individuals with a diagnosis of a chronic psychotic disorder. Total aggregate health care expenditures for individuals with a chronic psychotic disorder in Ontario increased at a moderate rate over this time period, in line with the growth of the number of people diagnosed, and thus not likely driven by unit costs or resource use. Psychiatric hospitalizations made up the largest share of health care expenditures (~30%). Nonetheless, among all health services, expenditures of acute medical hospitalizations, outpatient prescription drugs and home care saw the largest growth over time. Mean/per capita health care expenditures were greater for females, and increased with age as well as with the presence of comorbidities/chronic conditions. In particular, mean/per capita health care expenditures increased steadily with the number of comorbidities and were highest for individuals with 5 or more comorbidities and those with congestive heart failure, highlighting the ever-increasing importance of addressing physical health conditions among this patient population. These findings will have important implications for decision makers, namely around the appropriate allocation of health care resources for patients with chronic psychotic disorders. Future research should continue to monitor health care expenditures for individuals with chronic psychotic disorders as well as extend this analysis beyond 2019 to understand how the COVID-19 pandemic, and resulting lockdowns, has impacted aggregate health care expenditures and outcomes for patients living with chronic psychotic disorders.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".