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. 2021 Feb 10;52(14):2972–2984. doi: 10.1017/S003329172000495X

Migration history and risk of psychosis: results from the multinational EU-GEI study

Ilaria Tarricone 1,2,✉, Giuseppe D'Andrea 1,3, Hannah E Jongsma 4,5, Sarah Tosato 6, Charlotte Gayer-Anderson 7, Simona A Stilo 8,9, Federico Suprani 1, Conrad Iyegbe 9, Els van der Ven 10,11, Diego Quattrone 12, Marta di Forti 12, Eva Velthorst 13,14, Paulo Rossi Menezes 15, Celso Arango 16, Mara Parellada 16, Antonio Lasalvia 6, Caterina La Cascia 17, Laura Ferraro 17, Julio Bobes 18, Miguel Bernardo 19, Iulio Sanjuán 20, Jose Luis Santos 21, Manuel Arrojo 22, Cristina Marta Del-Ben 23, Giada Tripoli 9,24, Pierre-Michel Llorca 25, Lieuwe de Haan 13, Jean-Paul Selten 11, Andrea Tortelli 26, Andrei Szöke 27, Roberto Muratori 2, Bart P Rutten 11, Jim van Os 9,11,28, Peter B Jones 5,29, James B Kirkbride 4, Domenico Berardi 3, Robin M Murray 9, Craig Morgan 7
PMCID: PMC9693676  PMID: 33563347

Abstract

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.

Key words: First-episode psychosis, first-generation migrants, migration adversities, migration history, psychosis risk, social disadvantages

Introduction

There is evidence that some migrant groups are at higher risk of psychosis than the host population (Morgan, Knowles, & Hutchinson, 2019; Selten et al., 2005). A recent meta-analysis reported that the risk for non-affective psychotic disorder among first- and second-generation migrants from countries outside Europe is approximately three times higher than that for the local European population (Selten, Van Der Ven, & Termorshuizen, 2019). There is no evidence that these differences can be explained by an increased incidence in their countries of origin: studies conducted in Jamaica (Hickling & Rodgers-Johnson, 1995), Trinidad (Bhugra et al., 1996), Barbados (Mahy, Mallett, Leff, & Bhugra, 1999) and Surinam (Selten et al., 2005) have found incidence rates of psychosis lower than those observed in migrants from these countries in the UK and the Netherlands. In addition, the magnitude of this risk increase varies by minority group and by host country: in the UK, the incidence is highest among black African and Caribbean groups, while in the Netherlands, the incidence is highest among North African migrants (Bourque, van der Ven, & Malla, 2011). Moreover, studies in Bologna (Tarricone et al., 2016), Turin (Cardano, Scarinzi, Costa, & d'Errico, 2018) and Sweden (Price, Dalman, Zammit, & Kirkbride, 2018) have shown that internal migration is also associated with a higher incidence of psychotic disorders. These studies taken as a whole suggest that environmental factors in the countries to which people migrated (post-migration phase) may play a part in the genesis of psychosis among migrants.

It is intriguing to examine the impact of social adversities during different phases of migration, which can be schematically divided into three phases: pre-migration, migration and post-migration (Bhugra & Becker, 2005).

There is emerging evidence that specific indicators of social disadvantage, which may be related to the migration experience, are associated with psychosis, such as, unemployment, poor living conditions, single status and limited social networks (Stilo et al., 2017). Refugees have been shown to be at an increased risk of non-affective psychotic disorder compared with other migrants from the same region of origin, suggesting that pre-migratory traumas and disadvantage may be important determinants of risk (Hollander et al., 2016). Moreover, most studies conducted after 2001 support an association between low SES at birth and psychosis (Kwok, 2014). Social disadvantages could be relevant in both the pre-migration and post-migration phases. However, some studies conducted in the UK showed that adjustment for socioeconomic status does not fully explain the risk of psychosis in black and minority ethnic groups (Kirkbride et al., 2008, 2017): these studies suggest that there are probably other social determinants of psychosis in these populations. One such example could be the subjective evaluation of a mismatch between expectations and achievements, which has been associated with psychosis onset (Reininghaus et al., 2008); if such a mismatch was more pronounced in migrant groups, this could account for some of the excess risks here. Finally, some variables during the migration phase have been found to be associated with a higher risk of psychosis in migrants, such as lower age at the time of migration (Anderson & Edwards, 2020; Veling, Hoek, Selten, & Susser, 2011), and other characteristics of the migration phase often ascribed to refugees and asylum seekers such as detention/trauma during the journey and leaving the country of origin without any chance of returning because of hostile conditions there (Abubakar et al., 2018; Dapunt, Kluge, & Heinz, 2017).

However, as already noted (Bhugra, 2004), most of the studies available collected only post-migratory social disadvantages and apply to both migrants and more settled minorities, thus missing the opportunity of evaluating pre-migration social disadvantages and adversity during migration.

Along with the socio-developmental model (Morgan et al., 2019), we hypothesized that the likelihood of psychotic disorder in migrants may increase as a consequence of cumulative exposure to social disadvantages and adversities during the migration process and of mismatch between expectations and achievements.

To test these hypotheses, we examined whether social disadvantages in the country of origin, adversities during the migration process, social disadvantages in the country of arrival, and the mismatch between pre-migratory expectations (health, work, income, family, friends) and post-migratory achievements differed between first-generation migrant cases and controls. We tested whether these accounted for differences in odds of psychotic disorders after adjusting for ethnicity and known risk factors for psychosis (family history of psychosis, childhood trauma, education, lifetime cannabis use), length of stay following migration and language fluency in first-generation migrants. We used data from the six-country EUropean network of national schizophrenia networks studying Gene-Environment Interactions (EU-GEI; work package 2) case–control study, which included these exposure measures in an ethnically- and culturally-diverse sample.

Methods

Study design and participants

The aim of the EU-GEI case–control study was to assess in detail 1000 FEP cases and 1000 population-based controls during the same time span. Participants were recruited between 1 May 2010 and 1 April 2015 from 16 centres in England, the Netherlands, Spain, France, Italy and Brazil. All persons aged 18–64 years who made contact with mental health services for a probable first episode of psychosis (FEP) were invited to participate via their mental healthcare provider (Gayer-Anderson et al., 2020). Cases were included if they met the International Classification of Disease (ICD)-10 criteria for non-affective or affective psychotic disorder (F20–33), ascertained using the Operational Criteria Checklist (OPCRIT) algorithm (Williams et al., 1996).

In each centre, we recruited population-based first-generation migrants (controls) aged 18–64 years using a mixture of random and quota-sampling strategies, to maximize representativeness to the population-at-risk by age, sex and ethnicity in each catchment area. Quotas for control sampling were derived based on the most accurate local demographic data. Individuals with a history of psychotic disorder, or taking anti-psychotic medication, were not eligible (Di Forti et al., 2019; Gayer-Anderson et al., 2020). For the purpose of the present study, we included only first-generation migrants (i.e. people born outside the country where they lived at the time of the study). Migrant cases were included in the final study sample only if they had FEP after migration.

Ethical approval was granted in each centre. All participants gave written informed consent.

Measures

Our primary outcome was case–control status, with cases defined as receiving an OPCRIT-confirmed ICD-10 diagnosis of any psychotic disorder (ICD-10 codes F20-F33). Age at onset was determined using the Nottingham Onset Schedule (NOS) (Singh et al., 2005). Our main exposures were variables that operationalized the constructs of social disadvantages and adversities in pre-migration, migration and post-migration phases. Ethnicity was coded by self-ascription to six categories: White (Caucasian), Black, Mixed, Asian, North African and other, and dichotomized into white and non-white groups for analyses.

Data on the social disadvantage and adversity variables in the pre-migration, migration and post-migration phases were collected using an amended version of the Medical Research Council Socioeconomic Schedule (MRC SDS) (Mallet, 1997) and the Bologna Migration History and Social Integration Interview (Bo MH&SI Interview) (Tarricone et al., 2011) (see online Supplementary Materials). Based on previous evidence, we defined social disadvantage as a combination of low parental social class (Agerbo et al., 2015), unemployment (Reininghaus et al., 2008), living arrangement dependent on the family of origin (Tarricone et al., 2012), absence of a long-term relationship (Huang et al., 2019; Van Os, Driessen, Gunther, & Delespaul, 2000) and scarce social network (Gayer-Anderson & Morgan, 2013; Michalska Da Rocha, Rhodes, Vasilopoulou, & Hutton, 2018). Young age at migration, detention during migration and absence of any plans to return to the country of origin were characteristics chosen to describe adversities during migration phase (Anderson & Edwards, 2020; Hollander et al., 2016; Veling et al., 2011). We tried to disaggregate the timing of exposures to social disadvantages and adversities during the migration process and, thus, we defined a set of three indicators of social disadvantage for each phase of migration: the pre-migration social disadvantage index, the migration phase adversity index and the post-migration social disadvantage index (online Supplementary Fig. S1). For the pre-migration social disadvantage index (scoring 0–3), we used parental social class (salaried/intermediate v. working class/long-term unemployed), employment (ever/never employed before migration) and living arrangements (ever lived with people other than the family of origin; yes/no). For the migration adversity index (scoring 0–3), we used: migration prior to 18 years old (yes/no), detention during migration (yes/no), ever returned to the country of origin (yes/no). For the post-migration social disadvantage index (scoring 0–3), we used: employment (ever/never employed in the last 5 years of the post-migration phase), relationship status (ever/never in a long-term relationship in the last 5 years of the post-migration phase), and family and social network in the post-migration phase (any social network outside of their family of origin in the country of arrival: yes/no). The indices were treated as continuous variables in the statistical models. We also adjusted the statistical models with numbers of years spent in the country of arrival after migration. We assessed the self-evaluated mismatch between expectations and achievements following migration (scoring 1 – not at all satisfied, to 4 – completely satisfied). The interview covers five main domains (health, work, income, family and friends) and the possible values range is 1–20. Both years after migration and expectations/achievements mismatch were treated as continuous variables in the logistic regression models.

We also adjusted for known risk factors for psychosis (Di Forti et al., 2019; Esterberg, Trotman, Holtzman, Compton, & Walker, 2010; Frissen, Lieverse, Marcelis, Drukker, & Delespaul, 2015; Ochoa, Usall, Cobo, Labad, & Kulkarni, 2012; Varese et al., 2012): sex, country, educational attainment (0 = higher, 1 college/vocational; 2 = school qualification/tertiary; 3 = no school qualifications), parental history of psychosis, lifetime cannabis use (all categorical), age, childhood trauma and language fluency (all continuous). Age, sex and education were derived from the MRC SDS. Parental history of psychosis was recorded using the Family Interview for Genetic Studies questionnaire (Maxwell, 1992). Childhood total maltreatment score was derived from the Childhood Trauma Questionnaire (CTQ) (Bernstein et al., 2003). Lifetime cannabis use was derived from a modified version of the Cannabis Experience Questionnaire (Di Forti et al., 2009). In addition, we also adjusted for the country where the sample was recruited, using UK as reference category (v. the Netherlands, Spain, France, Italy and Brazil). Finally, we also adjusted for fluency in the majority language of the country of residence as there is some evidence that linguistic distance may be associated with the odds of a psychotic disorder (Jongsma et al., 2020). Fluency in the majority language was self-rated on a 10-point scale and used as a continuous variable.

Missing data

We investigated patterns of missingness by using binary logistic regression to test associations between covariate missingness and case–control outcome. We also compared complete and non-complete cases for confounders and exposures using χ2 tests. Then, we used Multiple Imputation (MI) by chained equations to handle missing data. All covariates and our outcome were included in the MI algorithm (White, Royston, & Wood, 2011). Following the rule of thumb that the number of imputations should be at least equal to the percentage of incomplete cases (White et al., 2011), we imputed 55 data sets, and conducted our analyses combining estimates across them (Manly & Wells, 2015). We also carried out complete-case analyses as a sensitivity analysis (see online Supplementary Materials).

Statistical analyses

Firstly, we calculated weights, which were applied in all analyses, to account for over-sampling of controls relative to the populations at risk. Weights were calculated using inverse probability and were applied in all analyses (Gayer-Anderson et al., 2020). Following MI, we used binary logistic regression to examine the associations between ethnicity, pre-migration, migration and post-migration social disadvantages and adversities and other exposures and confounders as follows:

Unadjusted Model: Crude (univariable) odds ratios (OR) were estimated to quantify the associations between case–control status and pre-migration, migration, post-migration social disadvantage and adversity indices and mismatch between expectations and achievements.

Adjusted Model A: ORs were adjusted for country, age, sex, ethnicity, education, family history of psychosis, childhood trauma, cannabis use, language fluency, years after migration.

Adjusted Model B: Finally, to see which migration indices were still significantly associated with case–control status, ORs were adjusted for all above variables plus pre-migration and post-migration disadvantage indices, migration adversity index, and mismatch between expectations and achievements.

Finally, in order to examine the possible dose–response relationships, we calculated the total number of the migration disadvantages/adversities as the sum of the variables representing pre-migration and post-migration disadvantages and migration adversities. This variable was recoded as zero, one, two, three, four, five or six or more adversities. We treated this variable as an ordinal predictor and calculated unadjusted and adjusted OR of FEP for each level. We controlled for all covariates included in Model A plus achievements/expectations mismatch.

We presented OR and 95% confidence intervals (95% CI) where appropriate, and analysed data using IBM-SPSS-Statistics 25.

Role of the funding source

Study funders contributed to the salaries of the research workers employed but did not participate in the study design, data analyses, data interpretation or writing of the manuscript. All authors had full access to the study data and had final responsibility for the decision to submit for publication.

Results

Between 2010 and 2015, 1.130 cases and 1.497 controls were recruited and assessed across 17 sites in six countries (UK, the Netherlands, France, Spain, Italy and Brazil). Among them, 256 cases (22.7%) and 219 controls (14.6%) were migrants and were included in this study. Seven cases had FEP before migration and were thus excluded from the analyses. This resulted in a sample of 468 individuals (Table 1).

Table 1.

Descriptive characteristics of the case–control sample

Total subjects Casesa Controlsb χ2/U p value
Gender χ2 = 20.56 <0.001*
Males 253 (54.1%) 159 (63.9%) 94 (42.9%)
Females 215 (45.9%) 90 (36.1%) 125 (57.1%)
Missing – – –
Total 468 (100%) 249 (100%) 219 (100%)
Age 32.0 (25.0–42.0)*** 30.0 (24.0–37.0)*** 34.0 (27.0–47.0)*** U = 20.269 <0.001**
Missing – – –
Ethnicity χ2 = 20.91 0.001*
White 162 (34.6%) 66 (26.5%) 96 (43.8%)
Black 145 (31.0%) 90 (36.1%) 55 (25.1%)
Mixed 27 (5.8%) 12 (4.8%) 15 (6.8%)
Asian 40 (8.5%) 21 (8.4%) 19 (8.7%)
North African 43 (9.2%) 30 (12.0%) 13 (5.9%)
Other 51 (10.9%) 30 (12.0%) 21 (9.6%)
Missing – – –
Total 468 (100%) 249 (100%) 219 (100%)
Place of birth χ2 = 14.57 0.024*
East Asia and Pacific 26 (5.6%) 10 (4.1%) 16 (7.3%)
Europe and Central Asia 133 (28.7%) 62 (25.3%) 71 (32.4%)
Latin America and The Caribbean 110 (23.7%) 53 (21.6%) 57 (26.0%)
Middle East and North Africa 49 (10.6%) 33 (13.5%) 16 (7.3%)
North America 15 (3.2%) 6 (2.4%) 9 (4.1%)
South Asia 18 (3.9%) 11 (4.5%) 7 (3.2%)
Sub-Saharan Africa 113 (24.4%) 70 (28.6%) 43 (19.6%)
Total 464 (100%) 245 (100%) 219 (100%)
Missing 4 (0.9%) 4 (1.6%) 0
Education χ2 = 33.38 <0.001*
Higher 65 (14.5%) 16 (7.0%) 49 (22.6%)
College 167 (37.4%) 87 (37.8%) 80 (36.9%)
School (compulsory to tertiary) 175 (39.1%) 95 (41.3%) 80 (36.9%)
No qualification 40 (8.9%) 32 (13.9%) 8 (3.7%)
Total 447 (100%) 230 (100%) 217 (100%)
Missing 21 (4.5%) 19 (7.6%) 2 (0.9%)
Language fluency 9.0 (7.0–10.0)*** 8.0 (7.0–10.0)*** 10.0 (7.0–10.0)*** U = 21.175 0.010**
Missing 25 (5.3%) 14 (5.6%) 11 (5.0%)
Family history of psychosis in first-degree relatives χ2 = 14.74 <0.001*
Yes 43 (11.0%) 33 (17.5%) 10 (5.0%)
No 352 (89.1%) 156 (82.5%) 191 (95.0%)
Total 390 (100%) 189 (100%) 201 (100%)
Missing 78 (16.7%) 60 (24.1%) 18 (8.2%)
Childhood trauma 37.0 (30.0–48.0)*** 41.0 (33.0–54.0)*** 33.0 (29.0–42.0)*** U = 14.959 <0.001**
Cannabis lifetime use χ2 = 24.07 <0.001*
Yes 248 (54.6%) 156 (65.5%) 92 (42.6%)
No 206 (45.4%) 82 (34.5%) 124 (57.4%)
Total 454 (100%) 238 (100%) 216 (100%)
Missing 14 (3.0%) 11 (4.4%) 3 (1.4%)
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*Pearson's χ2 test; **Mann–Whitney U test; ***median and interquartile range; ****Fisher's exact test.

Bold values denote statistical significance at the p<0.05 level.

a

First-generation migrants with FEP.

b

Healthy first-generation migrants.

Missing data

The proportion of participants with missing data (Table 1, Table 2) for socio-demographic and migration variables collected with the Bo MH&SII interview ranged from none missing on sex, age and ethnicity to 145 (31.0%) on the mismatch between income expectations and achievements. Complete data were available for 209 individuals (44.66% of the total sample). Missingness was higher for cases than controls for the following variables: education (χ2 = 12.28, p < 0.001), family history of psychosis (χ2 = 21.15, p < 0.001), childhood trauma (χ2 = 29.67, p < 0.001), ever employed before migration (χ2 = 6.39, p = 0.011), living conditions before migration (χ2 = 5.39, p = 0.02), detention during migration (χ2 = 6.91, p = 0.009), presence of family of origin in the country of arrival (χ2 = 8.48, p = 0.004), expectations and achievements (χ2 = 4.73, p = 0.03). Cases were more likely to yield incomplete data and therefore to be excluded from the final logistic regression model (χ2 = 31.37, p < 0.001). Participants included in the final model had lower education (χ2 = 13.41, p = 0.004) and higher CTQ (χ2 = 11.08, p = 0.001). Analyses on the complete sample have also been carried out and results are reported in the online Supplementary Tables S1 and S2.

Table 2.

Characteristics of the migratory process

Total subjects Casesa Controlsb χ2/U p value
Pre-migration phase
Social functioning before migration:
Parent social class (at birth) χ2 = 8.21 0.038**
Salariat 157 (37.3%) 72 (32.9%) 85 (42.1%)
Intermediate 103 (24.5%) 54 (24.7%) 49 (24.3%)
Working class 140 (32.4%) 86 (40.2%) 68 (33.7%)
Long-term unemployed 5 (1.2%) 5 (2.3%) 0 (0.0%)
Total 421 (100%) 219 (100%) 202 (100%)
Missing 47 (10.0%) 30 (12.0%) 17 (7.8%)
Were you ever employed before migration χ2 = 10.62 0.001*
No 192 (49.7%) 113 (57.9%) 79 (41.4%)
Yes 194 (50.3%) 82 (42.1%) 112 (58.6%)
Total 386 (100%) 195 (100%) 191 (100%)
Missing 82 (17.5%) 54 (21.7%) 28 (12.8%)
Who did you live with before migrating χ2 = 20.86 0.004*
Alone 27 (7.0%) 11 (5.6%) 16 (8.4%)
Alone with children 8 (2.1%) 2 (1.0%) 6 (3.1%)
Partner 17 (4.4%) 7 (3.6%) 10 (5.2%)
Partner and children 19 (4.9%) 4 (2.0%) 15 (7.9%)
Parents 233 (60.1%) 129 (65.5%) 104 (54.5%)
Other family 46 (11.9%) 30 (15.2%) 26 (8.4%)
Friends 16 (4.1%) 4 (2.0%) 12 (6.3%)
Other 22 (5.7%) 10 (5.1%) 12 (6.3%)
Total 388 (100%) 197 (100%) 191 (100%)
Missing 80 (17.1%) 53 (20.7%) 28 (12.8%)
Migration phase
Age of migration 20.0 (10.0–25.0)**** 18.0 (10.0–25.0)**** 21.0 (11.0–25.75)**** U = 21.639 <0.024***
Age of migration dichotomized χ2 = 8.06 0.005*
18 or older 263 (59.1%) 123 (52.8%) 140 (66.0%)
0–17 182 (40.9%) 110 (47.2%) 72 (34.0%)
Total 445 (100%) 233 (100%) 212 (100%)
Missing 23 (4.9%) 16 (6.4%) 7 (3.2%)
Have you been detained for not holding a resident permit χ2 = 4.34 0.037*
Yes 19 (4.9%) 14 (7.2%) 5 (2.6%)
No 366 (95.1%) 180 (92.8%) 186 (97.4%)
Total 385 (100%) 194 (100%) 191 (100%)
Missing 83 (17.7%) 55 (22.1%) 28 (12.8%)
How often do you travel back to your country of origin χ2 = 40.42 <0.001*
At least twice a year 54 (14.2%) 9 (4.7%) 45 (23.7%)
Once a year 75 (19.7%) 34 (17.8%) 41 (21.6%)
Less than once a year 145 (38.1%) 74 (38.7%) 71 (37.4%)
Never 107 (28.1%) 74 (38.7%) 33 (17.4%)
Total 381 (100%) 191 (100%) 190 (100%)
Missing 87 (18.6%) 58 (23.3%) 29 (13.2%)
Post-migration phase
Social integration after migration:
Been unemployed in the last 5 years χ2 = 5.69 0.017*
Yes 19 (4.4%) 15 (6.7%) 4 (2.0%)
No 410 (95.6%) 209 (93.3%) 201 (98.0%)
Total 429 (100%) 224 (100%) 205 (100%)
Missing 39 (8.3%) 25 (10.0%) 14 (6.4%)
Been single in the last 5 years χ2 = 13.53 <0.001*
Yes 79 (18.3%) 56 (24.9%) 23(11.2%)
No 352(81.7%) 169 (75.1%) 183(88.8%)
Total 431 (100%) 225 (100%) 206 (100%)
Missing 37 (7.9%) 25 (9.6%) 13 (5.9%)
Who do you have in this country χ2 = 37.44 0.000*
Children, partner, friends 200 (57.3%) 71 (41.3%) 129 (72.9%)
Parents or other family 149 (42.7%) 101 (58.7%) 48 (27.1%)
Total 349 (100%) 172 (100%) 177 (100%)
Missing 119 (25.4%) 77 (30.9%) 42 (19.2%)
Years after migration 12.0 (6.0–21.0)**** 11.0 (6.0–19.50)**** 12.5 (6.25–24.75)**** U = 22.410 0.091**
Missing 23 (4.9%) 16 (6.4%) 7 (3.2%)
Mismatch expectations/achievements:
Total score (0–20) 10.0 (8.0–12.75)**** 8.0 (7.0–11.0)**** 11.0 (10.0–14.0)**** U = 17.479 <0.001***
Work expectations achieved
Perfectly/partially achieved 210 (64.8%) 92 (57.1%) 118 (72.4%)
Poorly/not at all achieved 114 (35.2%) 69 (42.9%) 45 (27.6%)
Total 324 (100%) 161 (100%) 163 (100%)
Missing 144 (30.8%) 88 (35.3%) 56 (25.6%)
Income expectations achieved χ2 = 2.39 0.122*
Perfectly/partially achieved 206 (63.8%) 96 (59.6%) 110 (67.9%)
Poorly/not at all achieved 117 (36.2%) 65 (40.4%) 52 (32.1%)
Total 323 (100%) 161 (100%) 162 (100%)
Missing 145 (30.9%) 88 (35.3%) 57 (26.0%)
Family expectations achieved χ2 = 21.13 <0.001*
Perfectly/partially achieved 228 (70.4%) 93 (58.5%) 135 (81.8%)
Poorly/not at all achieved 96 (29.6%) 66 (41.5%) 30 (18.2%)
Total 324 (100%) 159 (100%) 165 (100%)
Missing 144 (30.8%) 90 (36.1%) 54 (24.7%)
Health expectations achieved χ2 = 29.59 <0.001*
Perfectly/partially achieved 266 (81.3%) 111 (69.4%) 155 (92.8%)
Poorly/not at all achieved 61 (18.7%) 49 (30.6%) 12 (7.2%)
Total 327 (100%) 160 (100%) 167 (100%)
Missing 141 (30.1%) 89 (35.7%) 52 (23.7%)
Friends expectations achieved χ2 = 35.05 <0.001*
Perfectly/partially achieved 238 (71.9%) 93 (57.1%) 145 (86.3%)
Poorly/not at all achieved 93 (28.1%) 70 (42.9%) 23 (13.7%)
Total 331 (100%) 163 (100%) 168 (100%)
Missing 137 (29.3%) 88 (34.5%) 51 (36.7%)
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*Pearson's χ2 test; **Fisher's exact test; ***Mann–Whitney U test; ****median and interquartile range.

Bold values denote statistical significance at the p<0.05 level.

a

First-generation migrants with FEP.

b

Healthy first-generation migrants.

Characteristics of the sample

Cases were more frequently men (159, 63.9% v. 94, 42.9%; χ2 = 20.59; p ⩽ 0.001), black (90, 36.1% v. 55, 25.1%) or North African (30, 12.0% v. 13, 5.9%; χ2 = 20.1, p ⩽ 0.001) and they were younger (median 30.0, interquartile range 24.0–37.0 v. median 34.0, interquartile range 27.0–47.0; U = 20.269; p ⩽ 0.001) (Table 1). Cases had lower education (p ⩽ 0.001) and lower language fluency (p = 0.01) than controls.

Pre-migration phase

Cases were more likely to have lower parental social class at birth (126, 57.6% v. 134, 66.4%; χ2 = 8.21, p = 0.038) and less likely than controls to have lived with someone other than their family of origin (68, 35.5% v. 87, 45.5%; χ2 = 20.86, p = 0.004) or to have been lifetime-employed in the pre-migration phase (82, 42.1% v. 112, 58.6%; χ2 = 10.62, p = 0.001) (Table 2). A single-point increase in the pre-migration social disadvantage index (range 0–3) was associated with increased odds of psychosis in the unadjusted (OR 1.61, 95% CI 1.19–2.17, p = 0.002, Nagelkerke's R2: 0.058) and in the final model (ORA 1.55, 95% CI 0.99–2.41, p = 0.051, Nagelkerke's R2: 0.392; ORB 1.61, 95% CI 1.06–2.44, p = 0.027, Nagelkerke's R2: 0.485) (Table 3).

Table 3.

Unadjusted and adjusted odds ratios for first-episode psychosis

OR ORA ORB
Pre-migration disadvantages 1.61 (1.19–2.17) (p = 0.002) 1.55 (0.99–2.41) (p = 0.051) 1.61 (1.06–2.44) (p = 0.027)
Migration adversities 1.78 (1.31–2.41) (p < 0.001) 1.53 (1.05–2.25) (p = 0.028) 1.18 (0.672–2.06) (p = 0.568)
Post-migration disadvantages 1.94 (1.19–3.15) (p = 0.008) 2.06 (1.13–3.73) (p = 0.018) 1.89 (1.02–3.51) (p = 0.044)
Expectations/achievements mismatch 1.61 (1.38–1.87) (p < 0.001) 1.81 (1.07–1.29) (p = 0.001) 1.14 (1.03–1.26) (p = 0.014)
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OR = unadjusted.

ORA = adjusted for site, age, gender, ethnicity, education, family history of psychosis, childhood trauma, cannabis use, language fluency, years after migration.

ORB = adjusted for all above variables plus pre-migration and post-migration disadvantages, migration adversities and expectations/achievements mismatch.

ORs in bold are significant (p < 0.05).

Migration phase

Cases were younger than controls when they migrated (median 18.0, interquartile range 10.0–25.0 v. median 21.0, interquartile range 11.0–25.75; U = 21.639; p ⩽ 0.024) and had been detained more often during the migration process (14, 7.2% v. 5, 2.6%; χ2 = 4.34; p = 0.037) (Table 2). More cases than controls had never travelled back to their countries of origin (74, 38.7% v. 33, 17.4%; χ2 = 40.42; p ⩽ 0.001) and considered the country of arrival as the last step in their migration (128, 69.2% v. 98, 53.6%, p = 0.002) (Table 2).

A single-point increase in the migration phase adversity index (range 0–3) was associated with increased odds of psychosis (OR 1.78, 95% CI 1.31–2.41, p ⩽ 0.001, Nagelkerke's R2: 0.090). This was attenuated in the adjusted models (ORA 1.53, 95% CI 1.05–2.25, p = 0.028, Nagelkerke's R2: 0.394; ORB 1.18, 95% CI 0.672–2.06, p = 0.568, Nagelkerke's R2: 0.485) (Table 3).

Post-migration phase

Cases were more likely never to have been employed in the last 5 years of the post-migration phase (15, 6.7% v. 4, 2.0%; χ2 = 5.69; p = 0.017), to be single (56, 24.9% v. 23, 11.2%; χ2 = 13.53; p ⩽ 0.001), and to have only family of origin in the country of arrival (101, 58.7% v. 48, 27.1%; χ2 = 37.44; p ⩽ 0.001). Cases also reported a higher mismatch between expectations and achievements compared with controls (Table 2). A single-point increase in both the post-migration social disadvantage index (range 0–3) and the mismatch between expectations and achievements (range 0–20) was associated with increased odds of psychosis in first-generation migrants in unadjusted (post-migration disadvantages OR 1.94, 95% CI 1.19–3.15, p = 0.008, Nagelkerke's R2: 0.119; mismatch OR 1.61, 95% CI 1.38–1.87, p ⩽ 0.001, Nagelkerke's R2: 0.185) and adjusted models (post-migration disadvantages ORA 2.06, 95% CI 1.13–3.73, p = 0.018, Nagelkerke's R2: 0.437; ORB 1.89, 95% CI 1.02–3.51, p = 0.044, Nagelkerke's R2: 0.485; mismatch ORA 1.81, 95% CI 1.07–1.29, p = 0.001, Nagelkerke's R2: 0.419; ORB 1.14, 95% CI 1.03–1.26, p = 0.014, Nagelkerke's R2: 0.485) (Table 3).

The cumulative effect of social disadvantages and adversities during all phases of the migration history

As reported in Table 4, we found a dose–response relationship between the number of disadvantages/adversities and odds of psychosis. Adjusted OR of FEP increased from 1.21 (95% CI 0.308–4.72, p = 0.788) for migrants reporting two adversities to 14.09 (95% CI 2.06–96.47, p = 0.007) for those reporting six or more social adversities. Nagelkerke's R2 increased from 0.180 (crude) to 0.479 (adjusted).

Table 4.

Dose–response effect of cumulative exposure to disadvantages/adversities

N. of disadvantages/adversities (% cases v. % controls) OR (95% CI) ORA (95% CI)
0 (2.6% v. 11.7%) 1.00 (–) 1.00 (–)
1 (6.4% v. 21.1%) 1.36 (0.433–4.25) (p = 0.601) 1.21 (0.308–4.72) (p = 0.788)
2 (13.1% v. 17.9%) 3.29 (1.12–9.72) (p = 0.031) 2.28 (0.578–9.03) (p = 0.239)
3 (14.0% v. 15.7%) 3.99 (1.37–11.65) (p = 0.011) 2.92 (0.701–12.16) (p = 0.141)
4 (18.7% v. 15.5%) 5.50 (1.85–16.40) (p = 0.002) 5.94 (1.19–29.42) (p = 0.029)
5 (21.7% v. 9.9%) 9.97 (3.22–30.26) (p < 0.001) 9.24 (1.73–49.47) (p = 0.01)
⩾6 (23.5% v. 8.2%) 14.05 (3.86–51.08) (p < 0.001) 14.09 (2.06–96.47) (p = 0.007)
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ORA = adjusted for site, age, gender, ethnicity, education, family history of psychosis, childhood trauma, cannabis use, fluency, years after migration, and achievements/expectations mismatch.

ORs in bold are significant (p < 0.05).

Discussion

Main findings

Our findings provide support for the hypothesis that social disadvantages and adversities during different phases of migration are associated with increased odds of psychosis in first-generation migrants, even when other risk factors are controlled for. Mutual adjustment for different phases suggested that social disadvantages during pre- and post-migration phases were associated with two times increased odds of psychosis. Moreover, our results support the hypothesis that the subjective evaluation of a mismatch between expectations and achievements is associated with increased odds of psychosis in first-generation migrants, even when adjusted for disadvantages and adversities and other exposures.

Pre-migration and post-migration social disadvantages seem to be more strongly associated with the odds of psychosis than adversities during the migration journey. Interestingly, length of stay in the country of arrival did not change the associations found between social disadvantages and psychosis odds. The cumulative effect of the migration social disadvantages and adversities further increased the odds of psychosis in migrants, suggesting a dose–response relationship in first-generation migrants (Stilo et al., 2017).

Strengths and limitations

Our study was based on a large, multi-centre, international sample, using population-based control samples. Controls were recruited with a mixture of random and quota-sampling strategy to maximize the representativeness of at-risk population in terms of age, sex and ethnicity [see Gayer-Anderson et al., 2020 (Gayer-Anderson et al., 2020) for details]. Participants included in the final logistic regression model were about 44.7% of the total sample.

To the best of our knowledge, our study is the first to investigate pre-migration exposures. Case–control studies are typically the most feasible for rare outcomes such as psychosis. The cross-sectional design limits any inferences about causality; however, the case–control study was nested in a population-based incidence study (Jongsma et al., 2018) and this is an effective approach for measuring exposures at psychosis onset. The novelty of our paper lies in trying to disaggregate the timing of exposures of pre-, during and post-migration disadvantages and adversities. While the cross-sectional retrospective design itself represents a significant limitation and this design is subject to recall bias, a stronger longitudinal prospective design to study pre-migration and migration exposures would be very difficult to perform, since participants would have to be followed while moving between different countries.

The total EU-GEI case–control comprised over 1000 cases and 1000 controls. A sample of this size has over 90% power to detect OR of 1.5 or greater at p < 0.05 when the prevalence of the exposure is 15% or greater. Migrants, though, represented only 22.7% of cases (256) and 14.6% of controls (219), resulting in a smaller sample. This may have led to the reduced power of detecting relevant associations.

Although many factors make up the picture of social disadvantages and adversities during the three different migratory phases, the characteristics we have chosen are those most investigated by previous studies and most representative of social disadvantage and migratory adversities (Agerbo et al., 2015; Anderson & Edwards, 2020; Frissen et al., 2015; Gayer-Anderson & Morgan, 2013; Hollander et al., 2016; Huang et al., 2019; Michalska Da Rocha et al., 2018; Tarricone et al., 2012; Van Os et al., 2000; Veling et al., 2011). Moreover, we operationalized the post-migration social disadvantages index considering the 5 years pre-psychosis onset (pre-study inclusion for controls). The 5-year period was conservatively chosen over simple observation at the onset, since when positive symptoms occur, functioning is often compromised already (Stilo et al., 2013).

In the final model, all the associations found were also adjusted for years after migration. The study's power to detect statistically significant associations could have been reduced by the multiple factors investigated; on the other hand, the statistically significant associations found are strengthened by this method.

The most investigated risk factor in the post-migration phase which showed cross-cultural consistency is ethnic density in the country of settlement: both in the UK and the Netherlands, minorities who live in areas with low ethnic density and are therefore exposed to conditions of low social capital and greater isolation have a higher risk of psychosis (Agerbo et al., 2015; Tarricone et al., 2012). We did not use a direct measure of ethnic density but investigated the migrants' relationships in the countries of arrival. We found that migrants relating only with the family of origin are at a higher risk of psychosis. Finally, other risk factors for psychosis, such as discrimination and racism, were not included in the present analysis and further studies should specifically evaluate the role of these exposures for psychosis onset in first-generation migrants.

Comparison with previous evidence

Several studies already support the hypothesis that a personal history of migration is associated with an increased likelihood of psychosis (Cantor-Graae & Pedersen, 2007), but, to the best of our knowledge, no previous studies have simultaneously evaluated the risk factors of the three migration phases. Findings indicating that refugees have a higher risk of developing psychosis compared to non-refugee migrants provide indirect evidence that the circumstances before and during migration matter (Brandt et al., 2019; Hollander et al., 2016). We evaluated the risk factors pre-, during and post-migration directly and provide preliminary evidence that social disadvantages across different migration phases, along with the subjective evaluation of a mismatch between achievements and expectations, may be important factors in the development of psychosis in migrants. These findings add to previous evidence from our study that greater social distance from the majority population negated some of the excess odds of psychosis in migrant and ethnic minority groups (Jongsma et al., 2020). The present study suggests that these forms of psychosocial disempowerment may impact on mismatches between achievements and expectations. Moreover, our results are consistent with those of a recent meta-analysis (Selten et al., 2019) showing a small difference in risk between first- and second-generation migrants: this suggests that the role of the adversities during the migration phase is small compared with the impact of social disadvantages. Further longitudinal evidence will be required to replicate and confirm this possibility.

Consistently with previous evidence, all the factors analysed under the construct of social disadvantages and adversities were found more frequently in cases than in controls (Corcoran et al., 2003; Howes & Murray, 2014; Morgan, Charalambides, Hutchinson, & Murray, 2010; Stilo et al., 2017). Independent of migrant status, many studies have looked at the association between single indicators of social disadvantage and psychosis, while only a few studies have specifically evaluated cumulative effects and long-term associations (Agerbo, Byrne, Eaton, & Mortensen, 2004; Morgan et al., 2008; Stilo et al., 2013, 2017). The AESOP study conducted in the UK (Cooper et al., 2008; Morgan et al., 2008) pointed out that social disadvantage/adversities following migration were associated with a higher risk of FEP in ethnic minorities. Mismatch between expectations and achievements was also found to be associated with psychosis onset in ethnic minorities in the UK (Reininghaus et al., 2008).

Relevance and implications

We found a higher burden of social disadvantage and adversities in all phases of the migration history in migrants who develop psychosis compared with those who do not, consistent with the socio-developmental pathway to psychosis proposed by Morgan et al. (2010). This result, along with the cumulative effect of the history of social disadvantages/adversities on the likelihood of psychosis, may indicate that the burden of socio-environmental risk factors during the migration history contributes to the high rates of psychosis in migrants.

Our findings seem to indicate that social disadvantages and stress during the entire migration history, more than adversities and trauma during the migration travel (e.g. migration phase), put migrants at higher risk of psychosis following a dose–response mechanism. This hypothesis is also consistent with those arising from studies showing that both striatal stress-induced dopamine release and dopamine synthesis capacity are elevated in migrants compared with non-migrants, independent of clinical status (Egerton et al., 2017). This is in accordance with the social defeat hypothesis of psychosis, which posits that long-term experience of outsider status or inferior position leads to a sensitization of the mesolimbic dopamine system and thereby increases the risk for psychosis (Gevonden et al., 2014).

In conclusion, social vulnerability through the whole migration process and the negative post-migration experiences was associated with double the odds of psychosis in first-generation migrants. Social and public health strategies aiming to reduce the negative socio-environmental factors and increase psychological support in the post-migration phase are needed to more effectively address the social drivers of high rates of psychosis among migrants.

Acknowledgements

The European Network of National Schizophrenia Networks Studying Gene-Environment Interactions (EU-GEI) Project was funded by grant agreement Health-F2-2010-241909 (Project EU-GEI) from the European Community's Seventh Framework programme. The Brazilian study was funded by grant 2012-0417-0 from the São Paulo Research Foundation. We deeply thank the whole EU-GEI WP2 Group†

Footnotes

†

EU-GEI WP2 Group: The European Network of National Schizophrenia Networks Studying Gene-Environment Interactions (EU-GEI) WP2 Group members include : Kathryn Hubbard, M.Sc., Department of Health Service and Population Research, Institute of Psychiatry, King's College London, De Crespigny Park, Denmark Hill, London, England; Stephanie Beards, Ph.D., Department of Health Service and Population Research, Institute of Psychiatry, King's College London, De Crespigny Park, Denmark Hill, London, England; Giada Tripoli, M.Sc., Department of Psychosis Studies, Institute of Psychiatry, King's College London, De Crespigny Park, Denmark Hill, London, England, and Department of Experimental Biomedicine and Clinical Neuroscience, Section of Psychiatry, University of Palermo, Palermo, Italy; Pedro Cuadrado, M.D., Villa de Vallecas Mental Health Department, Villa de Vallecas Mental Health Centre, Hospital Universitario Infanta Leonor/Hospital Virgen de la Torre, Madrid, Spain; José Juan Rodríguez Solano, M.D., Puente de Vallecas Mental Health Department, Hospital Universitario Infanta Leonor/Hospital Virgen de la Torre, Centro de Salud Mental Puente de Vallecas, Madrid, Spain; Angel Carracedo, M.D., Ph.D., Fundación Pública Galega de Medicina Xenómica, Hospital Clínico Universitario, Santiago de Compostela, Spain; Gonzalo López, Ph.D., Department of Child and Adolescent Psychiatry, Hospital General Universitario Gregorio Marañón, School of Medicine, Universidad Complutense, Investigación Sanitaria del Hospital Gregorio Marañón (Centro de Investigación Biomédica en Red de Salud Mental), Madrid, Spain; Bibiana Cabrera, M.D., Department of Psychiatry, Hospital Clinic, Institut d'Investigacions Biomèdiques August Pi i Sunyer, Centro de Investigación Biomédica en Red de Salud Mental, Universidad de Barcelona, Barcelona, Spain; Eduardo J. Aguilar, M.D., P.h.D, Department of Medicine, University of Valencia, INCLIVA, CIBERSAM, Valencia, Spain.; Paz Garcia-Portilla, M.D., Ph.D., Department of Medicine, Psychiatry Area, School of Medicine, Universidad de Oviedo, Centro de Investigación Biomédica en Red de Salud Mental, Oviedo, Spain; Javier Costas, Ph.D., Fundación Pública Galega de Medicina Xenómica, Hospital Clínico Universitario, Santiago de Compostela, Spain; Estela Jiménez-López, MSc, Department of Psychiatry, Servicio de Psiquiatría Hospital ‘Virgen de la Luz’, Cuenca, Spain; Mario Matteis, M.D., Department of Child and Adolescent Psychiatry, Hospital General Universitario Gregorio Marañón, School of Medicine, Universidad Complutense, Investigación Sanitaria del Hospital Gregorio Marañón (Centro de Investigación Biomédica en Red de Salud Mental), Madrid, Spain; Emiliano González, Ph.D., Department of Child and Adolescent Psychiatry, Hospital General Universitario Gregorio Marañón, School of Medicine, Universidad Complutense, Investigación Sanitaria del Hospital Gregorio Marañón (Centro de Investigación Biomédica en Red de Salud Mental), Madrid, Spain; Emilio Sánchez, M.D., Department of Psychiatry, Hospital General Universitario Gregorio Marañón, School of Medicine, Universidad Complutense, Investigación Sanitaria del Hospital Gregorio Marañón (Centro de Investigación Biomédica en Red de Salud Mental), Madrid, Spain; Nathalie Franke, M.Sc., Department of Psychiatry, Early Psychosis Section, Academic Medical Centre, University of Amsterdam, Amsterdam, the Netherlands; Jean-Paul Selten, M.D., Ph.D., Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University Medical Centre, Maastricht, the Netherlands, and Rivierduinen Institute for Mental Health Care, Leiden, the Netherlands; Fabian Termorshuizen, Ph.D., Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, South Limburg Mental Health Research and Teaching Network, Maastricht University Medical Centre, Maastricht, the Netherlands, and Rivierduinen Centre for Mental Health, Leiden, the Netherlands; Daniella van Dam, Ph.D., Department of Psychiatry, Early Psychosis Section, Academic Medical Centre, University of Amsterdam, Amsterdam, the Netherlands; Elles Messchaart, M.Sc., Rivierduinen Centre for Mental Health, Leiden, the Netherlands; Marion Leboyer, M.D., Ph.D., AP-HP, Groupe Hospitalier ‘Mondor’, Pôle de Psychiatrie, Créteil, France, Institut National de la Santé et de la Recherche Médicale, U955, Créteil, France, Faculté de Médecine, Université Paris-Est, Créteil, France, and Fondation Fondamental, Créteil, France; Franck Schürhoff, M.D., Ph.D., AP-HP, Groupe Hospitalier ‘Mondor’, Pôle de Psychiatrie, Créteil, France, Institut National de la Santé et de la Recherche Médicale, U955, Créteil, France, Faculté de Médecine, Université Paris-Est, Créteil, France, and Fondation Fondamental, Créteil, France; Stéphane Jamain, Ph.D., Institut National de la Santé et de la Recherche Médicale, U955, Créteil, France, Faculté de Médecine, Université Paris-Est, Créteil, France, and Fondation Fondamental, Créteil, France; Flora Frijda, M.Sc., Etablissement Public de Santé Maison Blanche, Paris, France; Grégoire Baudin, M.Sc., AP-HP, Groupe Hospitalier ‘Mondor’, Pôle de Psychiatrie, Créteil, France, Institut National de la Santé et de la Recherche Médicale, U955, Créteil, France; Aziz Ferchiou, M.D., AP-HP, Groupe Hospitalier ‘Mondor’, Pôle de Psychiatrie, Créteil, France, Institut National de la Santé et de la Recherche Médicale, U955, Créteil, France; Baptiste Pignon, M.D., AP-HP, Groupe Hospitalier ‘Mondor’, Pôle de Psychiatrie, Créteil, France, Institut National de la Santé et de la Recherche Médicale, U955, Créteil, France, and Fondation Fondamental, Créteil, France; Jean-Romain Richard, M.Sc., Institut National de la Santé et de la Recherche Médicale, U955, Créteil, France, and Fondation Fondamental, Créteil, France; Thomas Charpeaud, M.D., Fondation Fondamental, Créteil, France, CMP B CHU, Clermont Ferrand, France, and Université Clermont Auvergne, Clermont-Ferrand, France; Anne-Marie Tronche, M.D., Fondation Fondamental, Créteil, France, CMP B CHU, Clermont Ferrand, France, and Université Clermont Auvergne, Clermont-Ferrand, France; Daniele La Barbera, M.D., Ph.D., Department of Experimental Biomedicine and Clinical Neuroscience, Section of Psychiatry, University of Palermo, Palermo, Italy; Giovanna Marrazzo, M.D., Ph.D., Unit of Psychiatry, ‘P. Giaccone’ General Hospital, Palermo, Italy; Lucia Sideli, Ph.D., Department of Experimental Biomedicine and Clinical Neuroscience, Section of Psychiatry, University of Palermo, Palermo, Italy; Crocettarachele Sartorio, Ph.D., Unit of Psychiatry, ‘P. Giaccone’ General Hospital, Palermo, Italy; Fabio Seminerio, M.Sc., Department of Experimental Biomedicine and Clinical Neuroscience, Section of Psychiatry, University of Palermo, Palermo, Italy; Camila Marcelino Loureiro, M.D., Departamento de Neurociências e Ciencias do Comportamento, Faculdade de Medicina de Ribeirão Preto, Universidade de São Paulo, São Paulo, Brasil, and Núcleo de Pesquina em Saúde Mental Populacional, Universidade de São Paulo, São Paulo, Brasil; Rosana Shuhama, Ph.D., Departamento de Neurociências e Ciencias do Comportamento, Faculdade de Medicina de Ribeirão Preto, Universidade de São Paulo, São Paulo, Brasil, and Núcleo de Pesquina em Saúde Mental Populacional, Universidade de São Paulo, São Paulo, Brasil; Mirella Ruggeri, M.D., Ph.D., Section of Psychiatry, Department of Neuroscience, Biomedicine and Movement, University of Verona, Verona, Italy; Chiara Bonetto, Ph.D., Section of Psychiatry, Department of Neuroscience, Biomedicine and Movement, University of Verona, Verona, Italy; Doriana Cristofalo, M.A., Section of Psychiatry, Department of Neuroscience, Biomedicine and Movement, University of Verona, Verona, Italy; Marco Seri, Department of Medical and Surgical Sciences, Bologna University; Elena Bonora, Department of Medical and Surgical Sciences, Bologna University.

Supplementary material

For supplementary material accompanying this paper visit https://doi.org/10.1017/S003329172000495X.

S003329172000495Xsup001.docx (4MB, docx)

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Author contribution

All the authors in the EU-GEI group collected or supervised the data collection. IT was responsible for the conception and design of the study. IT, GD, FS, CG-A, HEJ and ST cleaned and prepared the data for this paper analysis. IT, GD and FS did the data analysis and wrote the findings in the initial manuscript. GD and FS contributed to the creation of the figures and tables. CM, RMM, J-PS, DB, JBK, HEJ, CI, LdH and EvdV provided a careful statistical and methodological revision of the manuscript and contributed to the final draft. IT, RMM, CM, J-PS and DB contributed to the interpretation of the results. All authors had full access to all data (including statistical reports and tables) in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Conflict of interest

MDF reports personal fees from Janssen, outside the submitted work. RMM reports personal fees from Janssen, Lundbeck, Sunovion and Otsuka, outside of the submitted work. PML reports personal fees from Janssen, Lundbeck and Otsuka, outside of the submitted work. CA has been a consultant to or has received honoraria or grants from Acadia, Angelini, Gedeon Richter, Janssen Cilag, Lundbeck, Minerva, Otsuka, Roche, Sage, Servier, Shire, Schering Plough, Sumitomo Dainippon Pharma, Sunovion and Takeda. HEJ is supported by the Economic and Social Research Council (grant number ES/S011714/1). All authors declare no competing interests. JBK is supported by the National Institute for Health Research University College London Hospital Biomedical Research Centre.

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