Migrant integration policies, regional social disadvantage, ethnicity and psychosis risk: Findings from the EU-GEI study
Bibliographic record
Abstract
Compared with individual-level factors, macro-level exposures have received less attention in research on the increased risk of psychosis among ethnic minorities. We aimed to investigate the impact of migrant integration policies and area-level social deprivation on higher incidence rates among ethnic minorities. This study, conducted between 2010 and 2015, analysed incidence data from five countries from the EUropean network of national schizophrenia networks studying Gene-Environment Interactions [EU-GEI]. The total population was multiplied by the duration of case-ascertainment to estimate person-years. Cases with a non-organic psychotic disorder were included. Exposures included population group (based on self/parental region of origin/self-ascribed ethnicity) and area-level exposures including country-level migrant integration policies and regional-level proxies of social deprivation (percentages of unemployment, low education, owner-occupied houses, single person-households). Negative binomial mixed-effects regression models were fitted to calculate the association between individual and area-level exposures and incidence of psychotic disorders. The study included 1933 individuals. Supportive migrant policies (IRR: 0.71; 95 % CI 0.68-0.73) and higher percentages of owner-occupied houses (IRR: 0.97; 95 % CI 0.96-0.97) were associated with lower incidence of psychosis. Higher percentages of unemployment (IRR: 1.08; 95 % CI 1.07-1.09) and single person-households (IRR: 1.10; 95 % CI 1.05-1.14) were associated with higher incidence of psychosis. Accounting for policies and area-level social deprivation markers reduced risk estimates among all migrant/ethnic minority groups, compared to the majority population. This is the first study on the impact of migrant integration policies on psychosis incidence. Migrant integration policies and area-level social deprivation influenced psychosis risk in the overall and minority populations. These findings can inform policies and social epidemiological approaches to studying multi-level exposures in psychosis. • Policies supporting migrants are associated with lower incidence of psychosis. • Markers of socio-deprivation are associated with higher incidence of psychosis. • Restrictive migrant policies contribute to higher risk of psychosis among migrants. • Multi-level ecological approaches yield new insights in psychiatric epidemiology.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".