Primary care reform and funding equity for mental health disorders in Ontario: a retrospective observational population-based study
Bibliographic record
Abstract
BACKGROUND: Mental health disorders are associated with high morbidity and reduced life expectancy, and are largely managed in primary care. We sought to assess the equity of distribution of new alternative payment models and teams introduced under primary care reform in Ontario for patients with mental health disorders. METHODS: We conducted a retrospective observational study using population-level administrative data for insured Ontario adults (age ≥ 18 yr) to identify all primary care payments to physicians that were allocated to individual patients in 2002/03 and 2011/12. We identified patients with mental health disorders using validated algorithms, and modelled the relations between per capita primary care costs and mental health disorders over time, stratified by type of mental health or substance use disorder and type of primary care payment. In an adjusted model, we adjusted for age, sex, rurality, neighbourhood income quintile, immigrant status, comorbidity and primary care model. For comparative purposes, we also examined the distribution of primary care payments for people with diabetes mellitus. RESULTS: Total per capita primary care payments increased more slowly over the study period for patients with mental health disorders (62.0%) than for the general population (88.3%). Total payments for patients with substance use disorders increased by 142.7%, largely owing to urine drug testing in opioid substitution clinics. Adjusted total payments for those with versus without mental health disorders decreased by 10% between 2002/03 and 2011/12, driven by lower alternative payments. Similar decreases, also driven by lower alternative payments, were found for all mental health disorder subgroups except substance use and for diabetes. INTERPRETATION: Payment and team reforms were associated with inequitable resource allocation to people with mental health disorders. The findings suggest the need for monitoring reforms for their impact on high-needs populations and making appropriate adjustments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".