Regional and sociodemographic variation of incident first-episode psychosis in Ontario, Canada
Bibliographic record
Abstract
Introduction: Psychotic disorders are associated with high levels of disability and poor clinical outcomes but little is known about the regional incidence of psychosis in Ontario. Objective: This study aimed to understand regional incidence variation and demographic and regional characteristics of individuals who may be suitable for receiving early psychosis intervention (EPI) services, as well as evaluate post-diagnosis healthcare utilisation. Methods: A population-based retrospective cohort study captured incident affective and non-affective psychosis cases among Ontario, Canada residents aged 12-50 from 2017-2021. The sociodemographic characteristics of the cohort were described, including Ontario Health region of residence. Incident cases were followed for 6-months post-diagnosis to capture health service utilisation. Logistic regression was used to model post-diagnosis hospitalisations and Poisson regression to model outpatient psychiatrist visits. Results: The cohort contained 44,188 individuals (41,257 non-affective psychosis; 3,058 affective psychosis). We observed substantial regional variation in incidence rates, which were higher in the North Western region for non-affective psychosis (167.44/100,000) and North Eastern region for affective psychosis (14.23/100,000) compared to the provincial average (92.24; 6.84/100,000, respectively). Compared to the Toronto region, post-diagnosis hospitalisations were significantly higher in the North East (non-affective psychosis aOR 1.14, 95%CI 1.01-1.30; affective psychosis aOR 1.69, 95%CI 1.13-2.54). Among those with non-affective psychosis, outpatient psychiatrist visits were significantly lower in all regions compared to Toronto (e.g., East aRR 0.61, 95%CI 0.60-0.62; North West aRR 0.34, 95%CI 0.32-0.36). Conclusions: There is considerable regional variation in incident psychosis and inverse relationships between hospitalisations and outpatient care. To successfully plan for future EPI programs in Ontario, it is essential to understand regional needs using a systematic, population-based approach.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 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) | 0.002 | 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".