Migration and psychosis: a meta-analysis of incidence studies
Bibliographic record
Abstract
BACKGROUND: The aims of this meta-analysis are (i) to estimate the pooled relative risk (RR) of developing non-affective psychotic disorder (NAPD) and affective psychotic disorder (APD) among migrants and their children; (ii) to adjust these results for socioeconomic status (SES); (iii) to examine the sources of heterogeneity that underlie the risk of NAPD. METHODS: We included population-based incidence studies that reported an age-adjusted RR with 95% confidence interval (CI) published 1 January 1977-12 October 2017 and used a random-effects model. RESULTS: We retrieved studies performed in Europe (n = 43), Israel (n = 3), Canada (n = 2) and Australia (n = 1). The meta-analysis yielded a RR, adjusted for age and sex, of 2.13 (95% CI 1.99-2.27) for NAPD and 2.94 (95% CI 2.28-3.79) for APD. The RRs diminished, but persisted after adjustment for SES. With reference to NAPD: a personal or parental history of migration to Europe from countries outside Europe was associated with a higher RR (RR = 2.94, 95% CI 2.63-3.29) than migration within Europe (RR = 1.88, 95% 1.62-2.18). The corresponding RR was lower in Israel (RR = 1.22; 0.99-1.50) and Canada (RR = 1.21; 0.85-1.74). The RR was highest among individuals with a black skin colour (RR = 4.19, 95% CI 3.42-5.14). The evidence of a difference in risk between first and second generation was insufficient. CONCLUSIONS: Positive selection may explain the low risk in Canada, while the change from exclusion to inclusion may do the same in Israel. Given the high risks among migrants from developing countries in Europe, social exclusion may have a pathogenic role.
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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 | 0.023 | 0.051 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.061 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".