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Background: Psychosis rates are higher among some migrant groups. We hypothesized that psychosis in migrants is associated with cumulative social disadvantage during different phases of migration. Methods: We used data from the EUropean Network of National Schizophrenia Networks studying Gene-Environment Interactions (EU-GEI) case–control study. We defined a set of three indicators of social disadvantage for each phase: pre-migration, migration and post-migration. We examined whether social disadvantage in the pre- and post-migration phases, migration adversities, and mismatch between achievements and expectations differed between first-generation migrants with first-episode psychosis and healthy first-generation migrants, and tested whether this accounted for differences in odds of psychosis in multivariable logistic regression models. Results: In total, 249 cases and 219 controls were assessed. Pre-migration (OR 1.61, 95% CI 1.06–2.44, p = 0.027) and post-migration social disadvantages (OR 1.89, 95% CI 1.02–3.51, p = 0.044), along with expectations/achievements mismatch (OR 1.14, 95% CI 1.03–1.26, p = 0.014) were all significantly associated with psychosis. Migration adversities (OR 1.18, 95% CI 0.672–2.06, p = 0.568) were not significantly related to the outcome. Finally, we found a dose–response effect between the number of adversities across all phases and odds of psychosis (⩾6: OR 14.09, 95% CI 2.06–96.47, p = 0.007). Conclusions: The cumulative effect of social disadvantages before, during and after migration was associated with increased odds of psychosis in migrants, independently of ethnicity or length of stay in the country of arrival. Public health initiatives that address the social disadvantages that many migrants face during the whole migration process and post-migration psychological support may reduce the excess of psychosis in migrants. [ABSTRACT FROM AUTHOR]

BackgroundThere are no risk models for the prediction of anxiety that may help in prevention. We aimed to develop a risk algorithm for the onset of generalized anxiety and panic syndromes.MethodFamily practice attendees were recruited between April 2003 and February 2005 and followed over 24 months in the UK, Spain, Portugal and Slovenia (Europe4 countries) and over 6 months in The Netherlands, Estonia and Chile. Our main outcome was generalized anxiety and panic syndromes as measured by the Patient Health Questionnaire. We entered 38 variables into a risk model using stepwise logistic regression in Europe4 data, corrected for over-fitting and tested it in The Netherlands, Estonia and Chile.ResultsThere were 4905 attendees in Europe4, 1094 in Estonia, 1221 in The Netherlands and 2825 in Chile. In the algorithm four variables were fixed characteristics (sex, age, lifetime depression screen, family history of psychological difficulties); three current status (Short Form 12 physical health subscale and mental health subscale scores, and unsupported difficulties in paid and/or unpaid work); one concerned country; and one time of follow-up. The overall C-index in Europe4 was 0.752 [95% confidence interval (CI) 0.724–0.780]. The effect size for difference in predicted log odds between developing and not developing anxiety was 0.972 (95% CI 0.837–1.107). The validation of predictA resulted in C-indices of 0.731 (95% CI 0.654–0.809) in Estonia, 0.811 (95% CI 0.736–0.886) in The Netherlands and 0.707 (95% CI 0.671–0.742) in Chile.ConclusionsPredictA accurately predicts the risk of anxiety syndromes. The algorithm is strikingly similar to the predictD algorithm for major depression, suggesting considerable overlap in the concepts of anxiety and depression. [ABSTRACT FROM PUBLISHER]