Incidence of psychotic disorders among first-generation immigrants and refugees in Ontario
Bibliographic record
Abstract
BACKGROUND: Evidence suggests that migrant groups have an increased risk of psychotic disorders and that the level of risk varies by country of origin and host country. Canadian evidence is lacking on the incidence of psychotic disorders among migrants. We sought to examine the incidence of schizophrenia and schizoaffective disorders in first-generation immigrants and refugees in the province of Ontario, relative to the general population. METHODS: We constructed a retrospective cohort that included people aged 14-40 years residing in Ontario as of Apr. 1, 1999. Population-based administrative data from physician billings and hospital admissions were linked to data from Citizenship and Immigration Canada. We used Poisson regression models to calculate age- and sex-adjusted incidence rate ratios (IRRs) and 95% confidence intervals (CIs) for immigrant and refugee groups over a 10-year period. RESULTS: In our cohort (n = 4,284,694), we found higher rates of psychotic disorders among immigrants from the Caribbean and Bermuda (IRR 1.60, 95% CI 1.29-1.98). Lower rates were found among immigrants from northern Europe (IRR 0.50, 95% CI 0.28-0.91), southern Europe (IRR 0.60, 95% CI 0.41-0.90) and East Asia (IRR 0.56, 95% CI 0.41-0.78). Refugee status was an independent predictor of risk among all migrants (IRR 1.27, 95% CI 1.04-1.56), and higher rates were found specifically for refugees from East Africa (IRR 1.95, 95% CI 1.44-2.65) and South Asia (IRR 1.51, 95% CI 1.08-2.12). INTERPRETATION: The differential pattern of risk across ethnic subgroups in Ontario suggests that psychosocial and cultural factors associated with migration may contribute to the risk of psychotic disorders. Some groups may be more at risk, whereas others are protected.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".