Vol.:(0123456789) Social Psychiatry and Psychiatric Epidemiology (2020) 55:645–657 https://doi.org/10.1007/s00127-020-01831-x STUDY PROTOCOLS AND SAMPLES The EUropean Network of National Schizophrenia Networks Studying Gene–Environment Interactions (EU‑GEI): Incidence and First‑Episode Case–Control Programme Charlotte Gayer‑Anderson1  · Hannah E. Jongsma2,3 · Marta Di Forti4 · Diego Quattrone4 · Eva Velthorst5,6,7 · Lieuwe de Haan7 · Jean‑Paul Selten8,9 · Andrei Szöke10,11,12 · Pierre‑Michel Llorca13 · Andrea Tortelli14 · Celso Arango15 · Julio Bobes16 · Miguel Bernardo17 · Julio Sanjuán18 · José Luis Santos19 · Manuel Arrojo20 · Mara Parellada15 · Ilaria Tarricone21 · Domenico Berardi21 · Mirella Ruggeri22 · Antonio Lasalvia22,23 · Laura Ferraro24 · Caterina La Cascia24 · Daniele La Barbera24 · Paulo Rossi Menezes25 · Cristina Marta Del‑Ben26 · EU‑GEI WP2 Group · Bart P. Rutten9 · Jim van Os9,27,28 · Peter B. Jones3,29 · Robin M. Murray28 · James B. Kirkbride2 · Craig Morgan1 Received: 15 July 2019 / Accepted: 6 January 2020 / Published online: 23 January 2020 © The Author(s) 2020 Abstract Purpose The EUropean Network of National Schizophrenia Networks Studying Gene–Environment Interactions (EU-GEI) study contains an unparalleled wealth of comprehensive data that allows for testing hypotheses about (1) variations in incidence within and between countries, including by urbanicity and minority ethnic groups; and (2) the role of multiple environmental and genetic risk factors, and their interactions, in the development of psychotic disorders. Methods Between 2010 and 2015, we identified 2774 incident cases of psychotic disorders during 12.9 million person-years at risk, across 17 sites in 6 countries (UK, The Netherlands, France, Spain, Italy, and Brazil). Of the 2774 incident cases, 1130 cases were assessed in detail and form the case sample for case–control analyses. Across all sites, 1497 controls were recruited and assessed. We collected data on an extensive range of exposures and outcomes, including demographic, clinical (e.g. premorbid adjustment), social (e.g. childhood and adult adversity, cannabis use, migration, discrimination), cognitive (e.g. IQ, facial affect processing, attributional biases), and biological (DNA via blood sample/cheek swab). We describe the methodology of the study and some descriptive results, including representativeness of the cohort. Conclusions This resource constitutes the largest and most extensive incidence and case–control study of psychosis ever conducted. Keywords Case–control · Environment–environment interactions · EU-GEI · First-episode psychosis · Gene–environment interactions · Incidence Introduction The lifetime prevalence of psychotic disorders is around 3% [1]. The associated individual, familial, social, and economic costs are vast. Psychotic disorders cause considerable dis- tress to sufferers and their families and often lead to marked social dysfunction and exclusion. The economic costs are huge: in Europe, an estimated €94 billion per year [2], of which over half is due to the indirect costs of unemployment, lost productivity, and informal care [3]. The World Health Organisation estimated that in Western countries, the treat- ment and care of patients with a psychotic disorder range from 1.6 to 2.6% of total healthcare expenditures [4]. Fur- ther, individuals with a psychotic disorder are far more likely The members of the EU-GEI WP2 Group are listed in Acknowledgements. Electronic supplementary material The online version of this article (https ://doi.org/10.1007/s0012 7-020-01831 -x) contains supplementary material, which is available to authorized users. * Craig Morgan craig.morgan@kcl.ac.uk Extended author information available on the last page of the article 646 Social Psychiatry and Psychiatric Epidemiology (2020) 55:645–657 1 3 to have a physical health problem [5] and to die younger, by as much as 20 years on average, compared with the general population [6]. Our knowledge of the distribution and determinants of psychotic disorders has increased in recent years. The inci- dence varies by area (e.g. higher in some urban versus rural areas) [7, 8] and social group (e.g. higher in some minority ethnic groups) [9, 10] and, in addition to well-established genetic and neurodevelopmental risk factors [11, 12], there is now substantial evidence implicating several environmen- tal risk factors [13], such as childhood adversity [14, 15] and cannabis use [16]. Pooled relative risks for these risk fac- tors range between two and four, and population attributable risk fractions range between 20 and 35% [17, 18]. Further, there is accumulating evidence that these myriad risk factors interact in complex ways to increase risk of psychosis via effects on the dopaminergic system, dysregulation of which may be the biological process that underpins the formation of psychotic experiences. However, there remain many gaps, inconsistencies, and unanswered questions, and recent work hints at different patterns of risk in different settings. For example, recent evidence has failed to show a universal association between city living and psychosis [19, 20]. To further add to this conundrum, Colodro-Conde et al. [21] found that the high prevalence of psychosis in some urban areas may be due to gene–environment selection, such that individuals with higher genetic loading for psychosis live in more densely populated areas. More generally, this points to a major limitation to our current knowledge of psychotic disorders: we know that environments affect onset and outcomes, but research so far has been conducted—with some important exceptions—in a remarkably small number of settings (i.e. select centres in the US, UK, and Australasia). Combined, these points emphasise the need for research in more diverse contexts to examine more nuanced hypotheses on the com- plex interplay between biology and environments in the aeti- ology of psychotic disorders. Our knowledge of psychotic disorders is limited, in part, because of heterogeneity in methods, which limits our abil- ity to compare findings across populations [22]. For exam- ple, differences in study design (i.e. case-register, versus cohort-based designs, versus first-contact studies), the age structures of populations at risk, case-identification proce- dures, diagnostic criteria, definitions and measurement of environmental factors, and analytic strategies have made cross-country comparisons difficult and likely obscured important clues to aetiology [23]. The only large-scale international comparative studies conducted to date are the World Health Organisation’s multi-country projects of the 1970s and 1980s, which compared the incidence and clinical and social characteristics of treated cases of psychoses from twelve diverse settings in ten countries using a standardised procedure for case identification and data collection [24]. However, since this landmark programme, there have been far-reaching economic and social changes (e.g. migration patterns, cannabis availability and use, and distribution of social risks) with conceivable impacts on the social epide- miology and aetiology of the psychoses. Moreover, studies of environment–gene interactions in psychotic disorders are rare and have typically involved small samples, with limited phenotyping and limited assessment of environmental fac- tors [21, 25, 26]. The EU-GEI programme was established to address these gaps and limitations [27]. EU-GEI is a multi-national research collaboration that was funded for 5 years (1 May 2010–30 April 2015). It consisted of 11 Work Packages (see Supplementary Table S1). This paper profiles the incidence and case–control programme of work (Work Package 2), which comprises the largest multi-site study of psychotic disorders ever conducted. In this paper, we describe the objectives and main aspects of the study. Objectives The overall goal of the present work package was to investi- gate the role of multiple environmental and genetic risk fac- tors, and their interactions, in the development of psychotic disorders. Specifically, our aims were (1) to investigate the impact of hypothesized environmental exposures, measured at individual and area levels, on (a) risk of psychotic dis- orders, and (b) high rates of disorder in urban areas and in migrant and minority ethnic groups; and (2) to examine hypothesized (a) gene × environment interactions (GxE), and (b) environment × environment interactions (ExE) across the life course. Methods Study design The data resource comprises a multi-site population-based incidence and case–control sample of cases with a first epi- sode of psychosis [International Classification of Diseases (ICD)-10 diagnoses F20–29 and F30–33] and controls drawn from tightly defined catchment areas in 17 sites in 6 coun- tries (England, The Netherlands, France, Spain, Italy, and Brazil; see Fig. 1). The sites were purposefully selected to include a mix of urban and rural areas, with varying propor- tions from minority ethnic groups (see Table 1). Sample Recruitment and data collection were conducted over a 5-year period between 2010 and 2015 (Table 1). We also 647Social Psychiatry and Psychiatric Epidemiology (2020) 55:645–657 1 3 added data from the Veneto region, Italy, collected as part of an earlier study [the Psychosis Incident Cohort Out- come Study (PICOS); 2005–2007], but with sufficiently similar methods to be pooled with that collected for this study. The incidence sample comprised 2774 individu- als with a first episode of psychosis. Of these, 1519 were approached, and 1130 were consented and assessed (41% of the total incidence sample). Reasons for non-partici- pation among cases who were approached were refusal to participate, language barriers, and exclusion after con- senting as they did not meet the age inclusion criteria. In addition, 1497 controls were recruited and assessed. Statistical power Our sample of 1130 cases and 1497 controls has high statis- tical power to test our primary study hypotheses, even after accounting for missing data and for the current necessity of restricting genetic analyses to individuals of non-Afri- can ancestry. For example, in a restricted sample of cases 1031 and 1438 controls, we have greater than 80% power to detect an interaction odds ratio of 1.2 at p ≤ 0.05, assuming an odds ratio of 2.0 for an environmental exposure and of 1.2 for each unit increase in polygenic score [assuming N (0.1) distribution]. Fig. 1 Map of EU-GEI settings for the incidence and case–control Work Package 648 Social Psychiatry and Psychiatric Epidemiology (2020) 55:645–657 1 3 Case ascertainment and recruitment All cases presenting to one of the 17 participating centres in 6 countries with a suspected first episode of psychosis were potentially eligible for inclusion in the study. The inclusion criteria for cases were (a) presence of at least one positive psychotic symptom for at least 1 day duration or two nega- tive psychotic symptoms (for at least 6 months duration) within the timeframe of the study; (b) aged between 18 and 64 years (inclusive); and (c) resident within a clearly defined catchment area at the time of their first presentation. Resi- dence was defined as a minimum of a one night stay at a resi- dential address within the catchment areas. Exclusion crite- ria were (a) previous contact with specialist mental health services for psychotic symptoms outside of the study period at each site; (b) evidence of psychotic symptoms precipitated by an organic cause (ICD-10: F09); (c) transient psychotic symptoms resulting from acute intoxication (F1X.5); (d) severe learning disabilities, defined by an IQ less than 50 or diagnosis of intellectual disability (F70–F79); and, for the case–control part only, (e) insufficient fluency of the primary language at each site to complete assessments. Case identification procedures involved teams of researchers regularly screening both general adult and spe- cialist mental health services (both in- and out-patients). The screening process involved researchers regularly liaising with clinical staff and checking clinical records to identity potential cases. The researchers only included those indi- viduals who they could be sure met the criteria based on the symptoms reported in the clinical notes. Potential cases were then approached when considered appropriate by clinical staff and informed consent sought. Control recruitment Inclusion criteria for controls were (a) aged between 18 and 64 years; (b) resident within a clearly defined catch- ment area at the time of consent into the study; (c) sufficient command of the primary language at each site to complete assessments; and (d) no current or past psychotic disorder. Table 1 Recruitment period and duration, and number of incidence and consented cases and controls, per site a Urban site of comparison b Number of incidence cases recruited in London pertains only to the first 12 months of the total recruitment dates for London Setting Incidence cases n Consented cases n % Controls n Recruitment start date Recruitment end date Recruitment duration in months England  Southeast Londona 262b 201 n/a 230 01/05/2010 01/05/2013 36  Cambridgeshire 266 45 16.9 106 01/10/2010 30/09/2013 36 The Netherlands  Amsterdama 292 96 32.9 101 01/10/2010 01/10/2013 36  Gouda and Voorhout 167 100 59.9 109 01/12/2010 01/12/2013 36 Spain  Madrida 89 39 43.8 38 23/02/2011 31/12/2012 22  Barcelonaa 108 31 28.7 37 20/12/2010 31/12/2012 25  Valenciaa 58 49 84.5 32 22/12/2010 31/12/2012 24  Oviedo 82 39 47.6 39 13/12/2010 31/12/2012 25  Santiago 36 28 77.8 38 13/12/2010 31/12/2012 25  Cuenca 27 18 66.7 38 08/02/2011 31/12/2012 23 France  Parisa 120 36 30.0 0 01/06/2012 01/06/2014 24  Val-de-Marnea 212 54 25.5 100 01/06/2010 01/06/2014 48  Puy-de-Dôme 42 15 35.7 47 01/09/2010 31/08/2012 24 Italy  Bolognaa 165 70 42.4 65 01/01/2011 31/12/2014 48  Veneto 104 59 56.7 115 02/01/2005 31/12/2007 36  Palermoa 179 58 32.4 100 02/10/2010 31/05/2014 44 Brazil  Ribeirão Preto 565 192 34.0 302 01/04/2012 01/04/2015 36  Total 2774 1130 40.7 1497 649Social Psychiatry and Psychiatric Epidemiology (2020) 55:645–657 1 3 To select a population-based sample of controls broadly rep- resentative of local populations in relation to age, gender, and ethnicity, a mixture of random and quota sampling was used. Quotas for control recruitment were based on the most accurate local demographic data available. Quotas were then filled using a variety of recruitment methods, including (1) random sampling from lists of all postal addresses (e.g. in London); (2) stratified random sampling via GP lists (e.g. in London and Cambridge) from randomly selected surger- ies; and (3) ad hoc approaches (e.g. internet and newspa- per adverts, leaflets at local stations, shops, and job cent- ers). In some sites (e.g. London), some groups (e.g. black African and black Caribbean) were oversampled to enable subsequent sub-group analyses. To deal with this in subse- quent analyses, weights were generated, based on the most accurate local demographic data available, to minimize any resulting bias in estimating the prevalence of exposures among controls. Individuals who agreed to take part were screened for a history of psychosis. Those who reported previous or current treatment for psychosis were excluded. Those who responded positively to any question in the screening instru- ment, indicating a possible psychotic experience, were interviewed further with standardised interviews to assess symptoms and to establish the presence or otherwise of a psychotic disorder. On this basis, no potential controls were found to have a past or current psychotic disorder. Data contents We collected data on an extensive range of exposures and outcomes across multiple domains using previously vali- dated questionnaires, tasks, and procedures: demographic, clinical, social, psychological, cognitive, and biological (Table 2). All environmental exposures and cognitive and psychological tests were measured using previously vali- dated questionnaires and tasks. Genetic risk was assessed both indirectly, using a familial liability score for psychosis [28], and directly, using DNA extracted from two 9 ml non-fasting venous blood sam- ples and/or via saliva samples (Oragene). Samples were genotyped using custom Illumina HumanCoreExome-24 BeadChip genotyping arrays containing probes for 570,038 genetic variants (Illumina Inc., San Diego, CA, USA). Genotype data were called using the GenomeStudio pack- age, transferred into PLINK format for further analysis, and underwent quality control based on genotype variants and samples. Quality assurance and control Prior to and during data collection, annual multi-site meet- ings were arranged to bring together principal investigators and core researchers to ensure that standardised procedures were being implemented, to provide training, to discuss issues with data collection, and to conduct inter-rater reli- ability exercises. The study was designed to ensure compara- ble procedures and methods across settings, with some local adaptation to allow for variations in healthcare provision and health service contact points. The primary deviation from protocol was in the Veneto region, Italy, where data were derived from a previous study which used comparable methods [29], but had a lower upper-age limit of 54. Training of researchers who were responsible for admin- istrating the assessments was performed at the outset and throughout the study. This was organised by a technical working committee of the overall EU-GEI study (Work Package 11). An online resource was made available with taped interviews, samples of recordings, and written sum- maries for staff training purposes. Inter-rater reliability was assessed annually. Researchers were required to attain and maintain a minimum threshold of correct ratings before being allowed to administer the core assessments. Sufficient levels of inter-rater reliability for the core measurements, ranging from 0.70 to 0.91, were achieved, and are shown in Table 3. Data management Data were collected on paper and, for some cognitive tasks (e.g. the White Noise Task), on laptops and securely stored at each of the participating centres, and was entered locally using an encrypted web-based system, using commercial software (4D) that was adapted specifically for EU-GEI pur- poses. Data were entered once with field codes restricted to logical values where possible, to minimise data entry errors. Blood or saliva samples were taken at approved clinical research facilities by an experienced researcher and were fully anonymized and identified by bar code, and sent to the Institute of Psychological Medicine and Clinical Neurology at Cardiff University for genotyping. The data resource has undergone a rigorous period of validation checks and clean- ing by a small number of experienced researchers. This has involved checks of missing data and corroboration of these against the paper files at each of the 17 sites. Ethical approval All participants who agreed to take part in the study pro- vided informed, written consent following full explanation of the study. Ethical approval for the study was provided by relevant research ethics committees in each of the study sites [30]. 650 Social Psychiatry and Psychiatric Epidemiology (2020) 55:645–657 1 3 Table 2 EU-GEI study battery summary for the case–control study Instruments Variables/purpose Clinical and biological OPCRIT 4a [1, 2] Research diagnosisc Nottingham Onset Scalea [3] Onset of psychotic symptoms Date of first contact with services Medicated treatment start date for psychosis Duration of untreated psychosis Record of clinical diagnosis Schedule for deficit syndromea [4] Presence of any deficit syndrome Community assessment of psychic experiencesb [5] Assessment of psychopathology in control participants Structured interview for schizotypy—revisedb [6, 7] Assessment of schizotypy in control participants Global assessment of functioning scalesa,b [8] Severity of symptoms Impairment of function Family interview for genetic studiesa, b [9] Family history of psychosis or other mental illness in first degree relatives of the proband Medication lista, b Past and present medication use Premorbid Adjustment Scale—shorteneda,b [10, 11] Child and adolescent social adjustment Child and adolescent academic adjustment Adolescent sexual adjustment Blood sample and cheek swabsa,b DNA Socio-demographic MRC socio-demographic schedule—modifieda,b [12] Age, gender, and ethnicity Place of birth (participant and parents) Age of migration Social class (participant and parents) Past and present addresses Household and living circumstances (past and present) Educational attainment Employment status (past and present) Relationship status (past and present) Income and poverty status (past and present) Religion Environmental exposures Childhood experiences of care and abusea,b [13, 14] Number of household arrangements Separation from or death of parents Other adverse events (taken into care, excluded from school, run away from home, physical neglect) Absence of peer or adult supports Perceived loneliness Household discord Childhood abuse (physical, sexual, emotional) Amended Bullying Questionnairea,b [15, 16] Victim of childhood bullying Childhood Trauma Questionnairea,b [17] Abuse (physical, sexual, emotional) Neglect (physical, emotional) List of threatening eventsa, b [18, 19] Stressful events and difficulties in the year prior to onset (cases), prior to interview (controls) Social environment assessment toola,b [20] Subjective rating of participant’s neighbourhood (e.g. trust and cooperation) Major Experiences of Discrimination Scalea,b [21, 22] Lifetime exposure to discrimination Cannabis Experience Questionnaire—modifieda,b [23] Detailed use of cannabis (past and present) and other recreational drugs CIDI—tobacco and alcohol lista,b [24] Present alcohol and tobacco use Bologna migration historya,b [25] Migration history Devaluation of Consumers Scalea,b [26] Perception of stigma 651Social Psychiatry and Psychiatric Epidemiology (2020) 55:645–657 1 3 Results Sample representativeness and characteristics There were similar proportions from minority ethnic groups among consented and non-consented cases (43% vs. 40%). However, the proportion of men and the propor- tions in younger age groups were higher among consented, compared with non-consented, cases (men: 62% vs. 57%; aged 18–34 years: 69% vs. 60%) (Table 4a). Compared with the general population, controls were more likely to belong to a minority ethnic group (controls: 28%, popu- lation at-risk: 23%) and were younger (aged between 18 and 34 years, controls: 56%, population at-risk: 38%) (Table 4b). The greater proportion of controls who were from minority ethnic groups  reflects oversampling in some sites (e.g. London) to enable subsequent sub-group analyses. Cases were younger than controls {median age of cases was 29 years [interquartile range (IQR) 22–37], and con- trols 33 years old [IQR 26–47]}. Compared with controls, a greater proportion of the cases were men (62% vs. 49%), migrants (28% vs. 22%), and left school without any quali- fications (16% versus 6%); a smaller proportion was of white ethnicity (63% versus 73%) (see Supplementary Table S2). Discussion This study was conducted in a diverse range of settings across Europe and one setting in Brazil, selected to ensure a mix of urban and rural areas with large migrant and minor- ity ethnic populations. This maximises its applicability to and importance for public health initiatives, with potential implications for both prevention and intervention, particu- larly among minority ethnic groups, and in urban areas, and in relation to cannabis and other substance use and develop- mental adversity. Our primary hypotheses centre on examin- ing variations in incidence and symptoms, environmental risk factors, and the interplay between environment and genetic factors in the development of psychotic disorders. Incidence and symptoms We have already published findings of the overall varia- tions in incidence of psychoses by site [30]. Our findings suggest marked geographical differences in the incidence of psychotic disorders, with around an eightfold variation among study sites after accounting for age, sex, and minor- ity ethnic status. At an area level, initial analyses suggest that some of this variation may be related to the proportion Table 2 (continued) Instruments Variables/purpose Cognitive and psychological Brief Core Schema Scalea,b [27] Attributional bias Brief Impact of Event Scalea,b [28, 29] Post-traumatic impact of stressful events Jumping to conclusions beads taska,b [30] Probabilistic reasoning bias White noise taska, b [31] Attributional bias to random events Degraded facesa,b [32] Deficits in facial affect processing Benton facial recognitiona,b [33] Deficits in unfamiliar face recognition WAIS—shorteneda,b [34] IQ Data collected from a Cases b Controls c OPCRIT assessment was based on a semi-structured clinical interview, or review of case notes and other relevant information. OPCRIT has been shown to have high inter-rater reliability generally [35, 36], and in our study following training (κ = 0.7) [1 –34]See Supplementary Appendix A1 for reference list of relevant assessments Table 3 Inter-rater reliability scores of 115 core researchers Reliability κ SIS-R overall 0.79 Positive symptom scale 0.79 Negative symptom scale 0.80 GAF 0.83 OPCRIT 0.70 List of threatening events 0.71 Childhood experiences of care and abuse 0.82 Bullying 0.91 Social class 0.81 652 Social Psychiatry and Psychiatric Epidemiology (2020) 55:645–657 1 3 of owner-occupied homes in an area (a tentative proxy for social cohesion or socioeconomic deprivation), i.e. areas with more owner-occupied homes had, on average, lower rates of psychotic disorder. Analyses of variations in inci- dence by ethnic group are ongoing. Analyses of symptom data on incident cases, collated using the OPCRIT, have examined the validity of a transdiagnostic dimensional structure of psychopathology and, in doing so, have chal- lenged the common binary categorisation of psychoses into non-affective and affective disorders [31]. Our findings sug- gest that a bifactor model of psychopathology, comprising one general factor and five dimensions (positive, negative, manic, disorganised, and depressive symptoms), best rep- resents the structure of symptoms among those with a psy- chotic disorder. We further found, compared with majority populations, cases in minority ethnic groups scored higher on the positive psychotic symptom dimension; and, com- pared with rural areas, cases in urban areas scored higher on the general symptom dimension. Environmental risk The initial focus of analyses of our case–control data resource is the associations and population impact of Table 4 Representativeness of (a) the consented case sample compared with the incidence sample, and (b) the control sample compared with the population-at-risk Missing data on a 6 incidence cases b 4 incidence cases c 47 incidence cases (42 of whom were from Puy-de-Dôme), and 3 assessed cases d 2 controls e 5 controls f This does not include Paris, as no controls were recruited here Incidence cases Consented cases χ2 p value n % n % Agea  18–24 808 29.2 415 36.7 35.24 < 0.01  25–34 868 31.4 365 32.3  35–44 558 20.2 204 18.1  45–54 382 13.8 104 9.2  55–64 152 5.5 42 3.7 Sexb  Male 1578 56.9 697 61.6 7.34 < 0.01  Female 1192 43.1 433 38.4 Ethnic minority statusc  Majority 1639 60.2 648 57.5 2.24 0.13  Minority 1088 39.8 479 42.5 Population at-risk Controlsf χ2 p value n % n % Aged  18–24 1,828,075 14.1 322 21.5 210.70 < 0.01  25–34 3,057,640 23.6 512 34.3  35–44 3,058,837 23.7 232 15.5  45–54 2,856,614 21.9 254 17.0  55–64 2,152,499 16.6 175 11.7 Sex  Male 6,337,783 49.5 706 47.2 3.29 0.07  Female 6,464,653 50.5 791 52.8 Ethnic minority statuse  Majority 9,881,660 77.2 1084 72.1 17.54 < 0.01  Minority 2,917,823 22.8 408 27.9 653Social Psychiatry and Psychiatric Epidemiology (2020) 55:645–657 1 3 putative environmental risk factors, including childhood adversity and abuse, adult adversity, discrimination, and cannabis use. In analyses of cannabis use data, for example, we found that, compared with those who did not use canna- bis, the odds of psychosis were (1) around three times higher among those who used cannabis daily; (2) around two times higher among those who spent more than 20 Euros a week on cannabis; and (3) around 50% higher among those who used cannabis high in THC [32]. In addition, we found vari- ations in population attributable fractions for daily canna- bis use on psychosis [32], with population attributable frac- tions (i.e. the proportion of psychosis, assuming causality, attributable to daily use) ranging from 1 (in Puy-de-Dôme, France) to 44% (in Amsterdam). Similar analyses examining childhood and adult adversity are ongoing, focusing on type, severity, and age of exposure (Morgan et al., in preparation). These analyses will be further extended to examine envi- ronment–environment and gene–environment interactions and to more clearly elucidate the pathogenic processes underpinning observed variations in incidence across study sites [30] and high rates of psychotic disorders in urban areas [7, 8], and in migrant and minority ethnic groups [9, 10]. Strengths and weaknesses To our knowledge, this is the most extensive multi-site inci- dence and case–control study of first-episode psychosis ever conducted, with comprehensive data on a variety of envi- ronmental, psychological, and genetic risk factors. The pri- mary strength of the EU-GEI study is its potential to provide ground-breaking and important information about the devel- opment of psychoses, by investigating the complex interrela- tionships between candidate environmental, psychological, and biological (genetic) factors and psychotic disorders, including the mechanisms through which they increase risk. In addition, given that our study was carried out in major urban and rural sites with heterogeneous populations sug- gests that our external validity may extend to other centres with similar population profiles. The combined incidence and case–control methodology allows for precise identifi- cation of, and ability to account for, any potential selec- tion biases amongst the recruited and assessed cases. The richness of the exposure information available will allow for more nuanced analyses and a more fine-grained under- standing of their impact on psychotic disorder than has been possible to date. Importantly, the inclusion of only cases with a first episode of psychosis (rather than individuals with long-standing disorder) allows inferences to be made about causal connections and processes. The primary limitation of these data resource relates to case identification. As in all previous studies, we relied on first contact with mental health services as a proxy for first onset. While it is likely most individuals who develop a psychotic disorder do present to services, at least in sites with well-developed public health systems, some who do not present will be missed and this may introduce selection biases. Any rate estimates should, therefore, be considered as treated incidence. Further, variations in referral procedures of patients with psychosis from primary to secondary mental health care settings and in the organization of secondary mental health care services across catchment areas may have influenced the identification of cases, and may explain some of the variation in estimates of incidence across study sites and countries. For example, unlike in other settings, patients in Madrid are not constrained to using mental health services in their residential catchment areas [33]. However, as high- lighted by Jongsma et al. [30], the divergences in service provision and cultural context are unlikely to fully explain the eightfold variation in incidence across sites. There are also several limitations that are inherent to case–control designs. First, while substantial efforts were made at the outset to reduce the potential biases in the iden- tification of cases (e.g. recruitment of participants from a number of sources using a variety of methods, including inpatient wards and community teams) and controls (e.g. use of mixture of random and quota sampling), we were not entirely successful; our cases are not fully representative of the sample identified in the incidence study, and our controls not of the population-at-risk. For example, reliance in some sites on recruitment of controls through ad hoc methods, such as newspaper advertisements, may have biased sam- ples. Interpretations of estimated effects (odds ratios) should be considered with this in mind. Second, there is the potential for both recall and observer bias. To minimise these, and validate environmental expo- sures, several steps were taken. For core environmental exposures (e.g. childhood adversity and cannabis use), we used extensive, well-validated measures, that drew on life course methods to anchor memories and improve recall. All researchers administering these assessments went through intensive training, with regular top-ups. Further, where possible, we drew on corroborative sources of information in the assessment of exposure to childhood and adulthood adversity [e.g. clinical records, interviews with siblings of a subsample of cases (n = 272)]. Third, measurement of exposure occurred after onset of disorder, making causal inferences problematic. To establish the temporal ordering of exposure and outcome, we carefully established the date of onset of disorder and, for measures of exposures in childhood and adulthood, ensured that all assessments related to the period pre-onset. Finally, given the large battery of tests and interviews conducted with our participants, data were missing for some assessments, particularly towards the end of the study bat- tery. Where appropriate, a standardised procedure for multi- ple imputation will be used to minimise the loss of precision 654 Social Psychiatry and Psychiatric Epidemiology (2020) 55:645–657 1 3 or selection biases which may otherwise be introduced in complete case analyses. Data resource access The EU-GEI WP2 principal investigators (contact: craig. morgan@kcl.ac.uk) welcome formal requests for access to the data, biological samples, and/or collaborative projects. Researchers will be required to complete an EU-GEI WP2 data interest form to state their intended hypotheses and analysis plan, which will be reviewed by the PIs to determine whether the proposal can be addressed by this data resource, does not duplicate on-going or completed analyses with this dataset, and lies within the scope of current ethical approv- als. More information about the study can be found on the study website (https ://www.eu-gei.eu/). Acknowledgements The EU-GEI Study is funded by grant agreement HEALTH-F2-2010-241909 (Project EU-GEI) from the European Com- munity’s Seventh Framework Programme, and Grant 2012/0417-0 from the São Paulo Research Foundation. The European Network of National Schizophrenia Networks Study- ing Gene–Environment Interactions (EU-GEI) WP2 Group non-author members include Kathryn Hubbard (Department of Health Service and Population Research, Institute of Psychiatry, Psychology and Neurosci- ence (IoPPN), King’s College London, De Crespigny Park, Denmark Hill, London SE5 8AF, UK), Stephanie Beards (Department of Health Service and Population Research, Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King’s College London, De Crespigny Park, Denmark Hill, London SE5 8AF, UK), Ulrich Reininghaus (Department of Health Service and Population Research, Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King’s College London, De Crespigny Park, Denmark Hill, London SE5 8AF, UK (Rivierduinen Centre for Mental Health, Leiden, Sandifortdreef 19, 2333 ZZ Leiden, The Netherlands), Giada Tripoli (Department of Psy- chosis Studies, Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King’s College London, De Crespigny Park, Denmark Hill, London SE5 8AF, UK, Department of Experimental Biomedicine and Clinical Neuroscience, Section of Psychiatry, University of Palermo, Via G. La Loggia n.1, 90129 Palermo, Italy), Simona A. Stilo (Depart- ment of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King’s College London, De Crespigny Park, Denmark Hill, London SE5 8AF, UK), Mara Parellada (Department of Child and Adolescent Psychiatry, Hospital General Universitario Gre- gorio Marañón, School of Medicine, Universidad Complutense, Inves- tigación Sanitaria del Hospital Gregorio Marañón (IiSGM), Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, Spain), Laura Roldán (Department of Child and Adolescent Psychiatry, Hospital General Universitario Gregorio Marañón, School of Medicine, Universidad Complutense, Investigación Sanitaria del Hospital Gregorio Marañón (IiSGM), Centro de Investigación Biomé- dica en Red de Salud Mental (CIBERSAM), Madrid, Spain), Gonzalo López (Department of Child and Adolescent Psychiatry, Hospital Gen- eral Universitario Gregorio Marañón, School of Medicine, Universidad Complutense, Investigación Sanitaria del Hospital Gregorio Marañón (IiSGM), Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, Spain), Mario Matteis (Department of Child and Adolescent Psychiatry, Hospital General Universitario Gregorio Marañón, School of Medicine, Universidad Complutense, Investigación Sanitaria del Hospital Gregorio Marañón (IiSGM), Centro de Investi- gación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, Spain), Marta Rapado (Department of Child and Adolescent Psychiatry, Hospital General Universitario Gregorio Marañón, School of Medicine, Universidad Complutense, Investigación Sanitaria del Hospital Gregorio Marañón (IiSGM), Centro de Investigación Biomé- dica en Red de Salud Mental (CIBERSAM), Madrid, Spain), Emiliano González (Department of Child and Adolescent Psychiatry, Hospital General Universitario Gregorio Marañón, School of Medicine, Univer- sidad Complutense, Investigación Sanitaria del Hospital Gregorio Marañón (IiSGM), Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, Spain), Covadonga Martínez (Depart- ment of Child and Adolescent Psychiatry, Hospital General Universi- tario Gregorio Marañón, School of Medicine, Universidad Com- plutense, Investigación Sanitaria del Hospital Gregorio Marañón (IiSGM), Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, Spain), Pedro Cuadrado (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 (Puente de Vallecas Men- tal Health Department, Hospital Universitario Infanta Leonor/Hospital Virgen de la Torre, Centro de Salud Mental Puente de Vallecas, C/Peña Gorbea 4, 28018 Madrid, Spain), Angel Carracedo (Fundación Pública Galega de Medicina Xenómica, Hospital Clínico Universitario, Choupana s/n, 15782 Santiago de Compostela, Spain), Javier Costas (Fundación Pública Galega de Medicina Xenómica, Hospital Clínico Universitario, Choupana s/n, 15782 Santiago de Compostela, Spain), Enrique García Bernardo (Department of Psychiatry, Hospital General Universitario Gregorio Marañón, School of Medicine, Universidad Complutense, Investigación Sanitaria del Hospital Gregorio Marañón (IiSGM), Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), C/Doctor Esquerdo 46, 28007 Madrid, Spain), Emilio Sánchez (Department of Psychiatry, Hospital General Universitario Gregorio Marañón, School of Medicine, Universidad Complutense, Investigación Sanitaria del Hospital Gregorio Marañón (IiSGM), Cen- tro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), C/Doctor Esquerdo 46, 28007 Madrid, Spain), Ma Soledad Olmeda (Department of Psychiatry, Hospital General Universitario Gregorio Marañón, School of Medicine, Universidad Complutense, Investigación Sanitaria del Hospital Gregorio Marañón (IiSGM), Centro de Investi- gación Biomédica en Red de Salud Mental (CIBERSAM), C/Doctor Esquerdo 46, 28007 Madrid, Spain), Bibiana Cabrera (Department of Psychiatry, Hospital Clinic, Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Universidad de Barcelona, C/ Villarroel 170, escalera 9, planta 6, 08036 Barcelona, Spain), Esther Lorente-Rovira (Department of Psychiatry, School of Medicine, Uni- versidad de Valencia, Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), C/Avda. Blasco Ibáñez 15, 46010 Valen- cia, Spain), Paz Garcia-Portilla (Department of Medicine, Psychiatry Area, School of Medicine, Universidad de Oviedo, Centro de Investi- gación Biomédica en Red de Salud Mental (CIBERSAM), C/Julián Clavería s/n, 33006 Oviedo, Spain), Estela Jiménez-López (Department of Psychiatry, Servicio de Psiquiatría Hospital “Virgen de la Luz”, C/ Hermandad de Donantes de Sangre, 16002 Cuenca, Spain), Nathalie Franke (Department of Psychiatry, Early Psychosis Section, Academic Medical Centre, University of Amsterdam, Meibergdreef 5, 1105 AZ Amsterdam, The Netherlands), Daniella van Dam (Department of Psy- chiatry, Early Psychosis Section, Academic Medical Centre, University of Amsterdam, Meibergdreef 5, 1105 AZ Amsterdam, The Nether- lands), Fabian Termorshuizen (Department of Psychiatry and Neu- ropsychology, School for Mental Health and Neuroscience, South Limburg Mental Health Research and Teaching Network, Maastricht University Medical Centre, P.O. Box 616, 6200 MD Maastricht, The Netherlands, Rivierduinen Centre for Mental Health, Leiden, Sandi- fortdreef 19, 2333 ZZ Leiden, The Netherlands), Elsje van der Ven (Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, South Limburg Mental Health Research and Teaching Network, Maastricht University Medical Centre, P.O. 655Social Psychiatry and Psychiatric Epidemiology (2020) 55:645–657 1 3 Box 616, 6200 MD Maastricht, The Netherlands, Rivierduinen Centre for Mental Health, Leiden, Sandifortdreef 19, 2333 ZZ Leiden, The Netherlands), Elles Messchaart (Rivierduinen Centre for Mental Health, Leiden, Sandifortdreef 19, 2333 ZZ Leiden, The Netherlands), Marion Leboyer (AP-HP, Groupe Hospitalier “Mondor”, Pôle de Psy- chiatrie, 51 Avenue de Maréchal de Lattre de Tassigny, 94010 Créteil, France, Institut National de la Santé et de la Recherche Médicale (INSERM), U955, Equipe 15, 51 Avenue de Maréchal de Lattre de Tassigny, 94010 Créteil, France, Faculté de Médecine, Université Paris-Est, 51 Avenue de Maréchal de Lattre de Tassigny, 94010 Créteil, France, Fondation Fondamental, 40 Rue de Mesly, 94000 Créteil, France), Franck Schürhoff (AP-HP, Groupe Hospitalier “Mondor”, Pôle de Psychiatrie, 51 Avenue de Maréchal de Lattre de Tassigny, 94010 Créteil, France, Institut National de la Santé et de la Recherche Médi- cale (INSERM), U955, Equipe 15, 51 Avenue de Maréchal de Lattre de Tassigny, 94010 Créteil, France, Faculté de Médecine, Université Paris-Est, 51 Avenue de Maréchal de Lattre de Tassigny, 94010 Créteil, France, Fondation Fondamental, 40 Rue de Mesly, 94000 Créteil, France), Grégoire Baudin (AP-HP, Groupe Hospitalier “Mondor”, Pôle de Psychiatrie, 51 Avenue de Maréchal de Lattre de Tassigny, 94010 Créteil, France, Institut National de la Santé et de la Recherche Médi- cale (INSERM), U955, Equipe 15, 51 Avenue de Maréchal de Lattre de Tassigny, 94010 Créteil, France), Aziz Ferchiou (AP-HP, Groupe Hospitalier “Mondor”, Pôle de Psychiatrie, 51 Avenue de Maréchal de Lattre de Tassigny, 94010 Créteil, France, Institut National de la Santé et de la Recherche Médicale (INSERM), U955, Equipe 15, 51 Avenue de Maréchal de Lattre de Tassigny, 94010 Créteil, France), Baptiste Pignon (AP-HP, Groupe Hospitalier “Mondor”, Pôle de Psychiatrie, 51 Avenue de Maréchal de Lattre de Tassigny, 94010 Créteil, France, Institut National de la Santé et de la Recherche Médicale (INSERM), U955, Equipe 15, 51 Avenue de Maréchal de Lattre de Tassigny, 94010 Créteil, France, Fondation Fondamental, 40 Rue de Mesly, 94000 Cré- teil, France), Stéphane Jamain (Institut National de la Santé et de la Recherche Médicale (INSERM), U955, Equipe 15, 51 Avenue de Maréchal de Lattre de Tassigny, 94010 Créteil, France, Faculté de Médecine, Université Paris-Est, 51 Avenue de Maréchal de Lattre de Tassigny, 94010 Créteil, France, Fondation Fondamental, 40 Rue de Mesly, 94000 Créteil, France), Jean-Romain Richard (Institut National de la Santé et de la Recherche Médicale (INSERM), U955, Equipe 15, 51 Avenue de Maréchal de Lattre de Tassigny, 94010 Créteil, France, Fondation Fondamental, 40 Rue de Mesly, 94000 Créteil, France), Thomas Charpeaud, Fondation Fondamental, 40 Rue de Mesly, 94000 Créteil, France, CMP B CHU, BP 69, 63003 Clermont Ferrand, Cedex 1, France, Université Clermont Auvergne, EA 7280, Clermont-Ferrand 63000, France),Anne-Marie Tronche(Fondation Fondamental, 40 Rue de Mesly, 94000 Créteil, France, CMP B CHU, BP 69, 63003 Clermont Ferrand, Cedex 1, France, Université Clermont Auvergne, EA 7280, Clermont-Ferrand 63000, France), Flora Frijda (Etablissement Public de Santé (EPS), Maison Blanche, Paris 75020, France), Lucia Sideli (Department of Experimental Biomedicine and Clinical Neuroscience, Section of Psychiatry, University of Palermo, Via G. La Loggia n.1, 90129 Palermo, Italy), Fabio Seminerio (Department of Experimental Biomedicine and Clinical Neuroscience, Section of Psychiatry, Uni- versity of Palermo, Via G. La Loggia n.1, 90129 Palermo, Italy), Croc- ettarachele Sartorio (Department of Experimental Biomedicine and Clinical Neuroscience, Section of Psychiatry, University of Palermo, Via G. La Loggia n.1, 90129 Palermo, Italy, Unit of Psychiatry, “P. Giaccone” General Hospital, Via G. La Loggia n.1, 90129 Palermo, Italy), Giovanna Marrazzo (Unit of Psychiatry, “P. Giaccone” General Hospital, Via G. La Loggia n.1, 90129 Palermo, Italy), Camila Marcelino Loureiro (Departamento de Neurociências e Ciencias do Comportamento, Faculdade de Medicina de Ribeirão Preto, Universi- dade de São Paulo, Av. Bandeirantes, 3900-Monte Alegre- CEP 14049- 900, Ribeirão Preto, SP, Brasil, Núcleo de Pesquina em Saúde Mental Populacional, Universidade de São Paulo, Avenida Doutor Arnaldo 455, CEP 01246-903, SP, Brasil), Rosana Shuhama (Departamento de Neurociências e Ciencias do Comportamento, Faculdade de Medicina de Ribeirão Preto, Universidade de São Paulo, Av. Bandeirantes, 3900- Monte Alegre- CEP 14049-900, Ribeirão Preto, SP, Brasil, Núcleo de Pesquina em Saúde Mental Populacional, Universidade de São Paulo, Avenida Doutor Arnaldo 455, CEP 01246-903, SP, Brasil), Mirella Ruggeri (Section of Psychiatry, Department of Neuroscience, Biomedi- cine and Movement, University of Verona, Piazzale L.A. Scuro 10, 37134 Verona, Italy), Sarah Tosato (Section of Psychiatry, Department of Neuroscience, Biomedicine and Movement, University of Verona, Piazzale L.A. Scuro 10, 37134 Verona, Italy), Chiara Bonetto (Section of Psychiatry, Department of Neuroscience, Biomedicine and Move- ment, University of Verona, Piazzale L.A. Scuro 10, 37134 Verona, Italy), Doriana Cristofalo (Section of Psychiatry, Department of Neu- roscience, Biomedicine and Movement, University of Verona, Piazzale L.A. Scuro 10, 37134 Verona, Italy). Compliance with ethical standards Conflict of interest On behalf of all authors, the corresponding author states that there is no conflict of interest. Open Access This article is licensed under a Creative Commons Attri- bution 4.0 International License, which permits use, sharing, adapta- tion, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. 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Accessed Dec 2018 Affiliations Charlotte Gayer‑Anderson1  · Hannah E. Jongsma2,3 · Marta Di Forti4 · Diego Quattrone4 · Eva Velthorst5,6,7 · Lieuwe de Haan7 · Jean‑Paul Selten8,9 · Andrei Szöke10,11,12 · Pierre‑Michel Llorca13 · Andrea Tortelli14 · Celso Arango15 · Julio Bobes16 · Miguel Bernardo17 · Julio Sanjuán18 · José Luis Santos19 · Manuel Arrojo20 · Mara Parellada15 · Ilaria Tarricone21 · Domenico Berardi21 · Mirella Ruggeri22 · Antonio Lasalvia22,23 · Laura Ferraro24 · Caterina La Cascia24 · Daniele La Barbera24 · Paulo Rossi Menezes25 · Cristina Marta Del‑Ben26 · EU‑GEI WP2 Group · Bart P. Rutten9 · Jim van Os9,27,28 · Peter B. Jones3,29 · Robin M. Murray28 · James B. Kirkbride2 · Craig Morgan1 657Social Psychiatry and Psychiatric Epidemiology (2020) 55:645–657 1 3 1 Department of Health Services and Population Research, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, 16 De Crespigny Park, London SE5 8AF, England 2 PsyLife Group, Division of Psychiatry, University College London, London, England 3 Department of Psychiatry, University of Cambridge, Cambridge, England 4 Social, Genetic, and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, England 5 Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, USA 6 Seaver Autism Center for Research and Treatment, Icahn School of Medicine at Mount Sinai, New York, USA 7 Early Psychosis Section, Department of Psychiatry, Academic Medical Centre, University of Amsterdam, Amsterdam, The Netherlands 8 Institute for Mental Health, GGZ Rivierduinen, Leiden, The Netherlands 9 Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University Medical Centre, Maastricht, The Netherlands 10 Institut National de la Santé et de la Recherche Médicale, U955, Equipe 15 Neuro-Psychiatrie Translationnelle, Créteil, France 11 AP-HP, Pôle de Psychiatrie des Hôpitaux Universitaires Henri Mondor, Créteil, France 12 Fondation FondaMental, Créteil, France 13 EA 7280 Npsydo, Université Clermont Auvergne, Clermont-Ferrand, France 14 Establissement Public de Santé, Maison Blanche, Paris, France 15 Department of Child and Adolescent Psychiatry, Hospital General Universitario Gregorio Marañón, School of Medicine, Universidad Complutense, ISGM, CIBERSAM, Madrid, Spain 16 Department of Medicine, Psychiatry Area, School of Medicine, Universidad de Oviedo, Centro de Investigación Biomédica en Red de Salud Mental, Oviedo, Spain 17 Barcelona Clinic Schizophrenia Unit, Hospital Clinic, Department of Medicine, Neuroscience Institute, University of Barcelona, Institut d’Investigacions Biomèdiques, August Pi I Sunyer, Centro de Investigación Biomédica en Red de Salud Mental, Barcelona, Spain 18 Department of Psychiatry, School of Medicine, Universidad de Valencia, Centro de Investigación Biomédica en Red de Salud Mental, Valencia, Spain 19 Department of Psychiatry, Hospital “Virgen de la Luz”, Cuenca, Spain 20 Department of Psychiatry, Psychiatric Genetic Group, Instituto de Investigación Sanitaria de Santiago de Compostela, Complejo Hospitalario Universitario de Santiago de Compostela, Santiago, Spain 21 Department of Biomedical and NeuroMotor Sciences, Psychiatry Unit, Alma Mater Studiorium Università di Bologna, Bologna, Italy 22 Section of Psychiatry, Department of Neuroscience, Biomedicine and Movement, University of Verona, Verona, Italy 23 Section of Psychiatry, Azienda Ospedaliera Universitaria Integrata di Verona, Verona, Italy 24 Department of Experimental Biomedicine and Clinical Neuroscience, Section of Psychiatry, University of Palermo, Palermo, Italy 25 Department of Preventive Medicine, Faculdade de Medicina, Universidade de São Paulo, São Paulo, Brazil 26 Division of Psychiatry, Department of Neuroscience and Behaviour, Ribeirão Preto Medical School, Universidade de São Paulo, São Paulo, Brazil 27 Department of Psychiatry, Brain Center Rudolf Magnus, Utrecht University Medical Centre, Utrecht, The Netherlands 28 Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, England 29 CAMEO Early Intervention Service, Cambridgeshire and Peterborough National Health Service Foundation Trust, Cambridge, England