Comparing Risk Factors for Non-affective Psychotic Disorders With Common Mental Disorders Among Migrant Groups: A 25-Year Retrospective Cohort Study of 2 Million Migrants
Bibliographic record
Abstract
BACKGROUND AND HYPOTHESIS: Although migration is a well-established risk factor for psychotic disorders, less is known about factors that modify risk within migrant groups. We sought to assess whether socio-demographic, migration-related, and post-migration factors were associated with the risk of non-affective psychotic disorders (NAPD) among first-generation migrants, and to compare with estimates for common mental disorders (CMD) to explore specificity of the effect. STUDY DESIGN: We constructed a retrospective cohort of first-generation migrants to Ontario, Canada using linked population-based health administrative data (1992-2011; n = 1 964 884). We identified NAPD and CMD using standardized algorithms. We used modified Poisson regression models to estimate incidence rate ratios (IRR) for each factor to assess its effect on the risk of each outcome. STUDY RESULTS: Nearly 75% of cases of NAPD met the case definition for a CMD prior to the first diagnosis of psychosis. Our findings suggest that younger age at migration, male sex, being of African-origin, and not having proficiency in national languages had a specificity of effect for a higher risk of NAPD. Among migrants who were over 19 years of age at landing, higher pre-migratory education and being married/common-law at landing showed specificity of effect for a lower risk of NAPD. Migrant class, rurality of residence after landing, and post-migration neighborhood-level income showed similar effects across disorders. CONCLUSIONS: Our findings help identify high-risk groups to target for intervention. Identifying factors that show specific effects for psychotic disorder, rather than mental disorders more broadly, are important for informing prevention and early intervention efforts.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 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 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".