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Variation of subclinical psychosis across 16 sites in Europe and Brazil: findings from the multi-national EU-GEI study

Published online by Cambridge University Press:  30 January 2024

Giuseppe D'Andrea
Affiliation:
University of Montreal Hospital Reseach Centre (CRCHUM), Montréal, Québec, Canada Douglas Mental Health University Institute, Prevention and Early Intervention Program for Psychosis (PEPP-Montréal), Montréal, Québec, Canada Community Mental Health Center of Sassuolo, Department of Mental Health and Drug Abuse, AUSL Modena, Modena, Italy
Diego Quattrone
Affiliation:
Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
Kathryn Malone
Affiliation:
Central and North West London NHS Foundation Trust, London, UK
Giada Tripoli
Affiliation:
Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK Department of Biomedicine, Neuroscience and Advanced Diagnostics (BiND), Psychiatry Section, University of Palermo, Palermo, Italy Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties, University of Palermo, Piazza delle Cliniche, Palermo, Italy
Giulia Trotta
Affiliation:
Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
Edoardo Spinazzola
Affiliation:
Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
Charlotte Gayer-Anderson
Affiliation:
ESRC Center for Society and Mental Health, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK
Hannah E Jongsma
Affiliation:
Centre for Transcultural Psychiatry ‘Veldzicht’, Balkbrug, The Netherlands University Centre for Psychiatry, University Medical Centre Groningen, Groningen, The Netherlands
Lucia Sideli
Affiliation:
Department of Human Science, LUMSA University, Rome, Italy
Simona A Stilo
Affiliation:
Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK Department of Mental Health and Addiction Services, ASP Crotone, Crotone, Italy
Caterina La Cascia
Affiliation:
Department of Biomedicine, Neuroscience and Advanced Diagnostics (BiND), Psychiatry Section, University of Palermo, Palermo, Italy
Laura Ferraro
Affiliation:
Department of Biomedicine, Neuroscience and Advanced Diagnostics (BiND), Psychiatry Section, University of Palermo, Palermo, Italy
Antonio Lasalvia
Affiliation:
Section of Psychiatry, Department of Neuroscience, Biomedicine and Movement, University of Verona, Piazzale L.A. Scuro 10, Verona, Italy
Sarah Tosato
Affiliation:
Section of Psychiatry, Department of Neuroscience, Biomedicine and Movement, University of Verona, Piazzale L.A. Scuro 10, Verona, Italy
Andrea Tortelli
Affiliation:
Etablissement Public de Santé Maison Blanche, Paris, France
Eva Velthorst
Affiliation:
Department of Research, Community Mental Health Service, GGZ Noord-Holland-Noord, The Netherlands
Lieuwe de Haan
Affiliation:
Department of Psychiatry, Amsterdam UMC, Amsterdam, The Netherlands
Pierre-Michel Llorca
Affiliation:
Université Clermont Auvergne, EA 7280 Npsydo, Clermont-Ferrand, France
Paulo Rossi Menezes
Affiliation:
University Hospital, Section of Epidemiology, University of São Paulo, São Paulo, Brazil
Jose Luis Santos
Affiliation:
Department of Psychiatry, Servicio de Psiquiatría Hospital “Virgen de la Luz”, Cuenca, Spain
Manuel Arrojo
Affiliation:
Department of Psychiatry, Psychiatric Genetic Group, Instituto de Investigación Sanitaria de Santiago de Compostela, Complejo Hospitalario Universitario de Santiago de Compostela, Santiago, Spain
Julio Bobes
Affiliation:
Department of Medicine, Psychiatry Area, School of Medicine, Universidad de Oviedo, ISPA, Ineuropa, CIBERSAM, Oviedo, Spain
Julio Sanjuán
Affiliation:
Department of Psychiatry, School of Medicine, Universidad de Valencia, Centro de Investigación Biomédica en Red de Salud Mental, Valencia, Spain
Miguel Bernardo
Affiliation:
Barcelona Clinic Schizophrenia Unit, Hospital Clinic, Departament de Medicina, Institut de Neurociències (UBNeuro), Universitat de Barcelona (UB), Institut d'Investigacions Biomèdiques August Pi I Sunyer (IDIBAPS), CIBERSAM, ISCIII, Barcelona, Spain
Celso Arango
Affiliation:
Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, IiSGM, School of Medicine, Universidad Complutense, CIBERSAM, Centro de Investigación Biomédica en Red de Salud Mental, Madrid, Spain
James B Kirkbride
Affiliation:
PsyLife Group, Division of Psychiatry, UCL, London, England, UK
Peter B Jones
Affiliation:
Department of Psychiatry, University of Cambridge, Cambridge, England, UK
Bart P Rutten
Affiliation:
Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University Medical Centre, Maastricht, The Netherlands
Jim Van Os
Affiliation:
Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK School for Mental Health and Neuroscience, University of Maastricht, Maastricht, The Netherlands
Jean-Paul Selten
Affiliation:
School for Mental Health and Neuroscience, University of Maastricht, Maastricht, The Netherlands
Evangelos Vassos
Affiliation:
Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
Franck Schürhoff
Affiliation:
Univ Paris Est Creteil, INSERM, IMRB, AP-HP, Hôpitaux Universitaires « H. Mondor », DMU IMPACT, Fondation Fondamental, Creteil, France
Andrei Szöke
Affiliation:
Univ Paris Est Creteil, INSERM, IMRB, AP-HP, Hôpitaux Universitaires « H. Mondor », DMU IMPACT, Fondation Fondamental, Creteil, France
Baptiste Pignon
Affiliation:
Univ Paris Est Creteil, INSERM, IMRB, AP-HP, Hôpitaux Universitaires « H. Mondor », DMU IMPACT, Fondation Fondamental, Creteil, France
Michael O'Donovan
Affiliation:
Division of Psychological Medicine and Clinical Neurosciences, MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, UK
Alexander Richards
Affiliation:
Division of Psychological Medicine and Clinical Neurosciences, MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, UK
Craig Morgan
Affiliation:
ESRC Center for Society and Mental Health, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK
Marta Di Forti
Affiliation:
Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
Ilaria Tarricone*
Affiliation:
Department of Medical and Surgical Sciences, Bologna Transcultural Psychosomatic Team (BoTPT), University of Bologna, Bologna, Italy Department of Mental Health and Pathological Addiction, AUSL Bologna, Bologna, Italy
Robin M Murray
Affiliation:
Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
*
Corresponding author: Ilaria Tarricone; Email: ilaria.tarricone@unibo.it
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Abstract

Background

Incidence of first-episode psychosis (FEP) varies substantially across geographic regions. Phenotypes of subclinical psychosis (SP), such as psychotic-like experiences (PLEs) and schizotypy, present several similarities with psychosis. We aimed to examine whether SP measures varied across different sites and whether this variation was comparable with FEP incidence within the same areas. We further examined contribution of environmental and genetic factors to SP.

Methods

We used data from 1497 controls recruited in 16 different sites across 6 countries. Factor scores for several psychopathological dimensions of schizotypy and PLEs were obtained using multidimensional item response theory models. Variation of these scores was assessed using multi-level regression analysis to estimate individual and between-sites variance adjusting for age, sex, education, migrant, employment and relational status, childhood adversity, and cannabis use. In the final model we added local FEP incidence as a second-level variable. Association with genetic liability was examined separately.

Results

Schizotypy showed a large between-sites variation with up to 15% of variance attributable to site-level characteristics. Adding local FEP incidence to the model considerably reduced the between-sites unexplained schizotypy variance. PLEs did not show as much variation. Overall, SP was associated with younger age, migrant, unmarried, unemployed and less educated individuals, cannabis use, and childhood adversity. Both phenotypes were associated with genetic liability to schizophrenia.

Conclusions

Schizotypy showed substantial between-sites variation, being more represented in areas where FEP incidence is higher. This supports the hypothesis that shared contextual factors shape the between-sites variation of psychosis across the spectrum.

Type
Original Article
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
Copyright
Copyright © The Author(s), 2024. Published by Cambridge University Press

Introduction

The incidence of psychotic disorders varies substantially across geographic regions. A recent meta-analysis (Jongsma, Turner, Kirkbride, & Jones, Reference Jongsma, Turner, Kirkbride and Jones2019) estimated an almost 15-times variation across 17 different countries. Individual characteristics have been linked to a higher incidence of psychotic disorders, including younger age, male sex (Aleman, Kahn, & Selten, Reference Aleman, Kahn and Selten2003; Van Der Werf et al., Reference Van Der Werf, Hanssen, Köhler, Verkaaik, Verhey, Van Winkel and Allardyce2014), migration (Selten, Van Der Ven, & Termorshuizen, Reference Selten, Van Der Ven and Termorshuizen2019; Tarricone et al., Reference Tarricone, D'Andrea, Jongsma, Tosato, Gayer-Anderson, Stilo and Morgan2021), education (Dickson et al., Reference Dickson, Hedges, Ma, Cullen, Maccabe, Kempton and Laurens2020), genetic liability(Lewis & Knight, Reference Lewis and Knight2012), adverse childhood experiences (Morgan & Gayer-Anderson, Reference Morgan and Gayer-Anderson2016; Varese et al., Reference Varese, Smeets, Drukker, Lieverse, Lataster, Viechtbauer and Bentall2012), and cannabis use (Di Forti et al., Reference Di Forti, Quattrone, Freeman, Tripoli, Gayer-Anderson, Quigley and van der Ven2019). Site-level factors such as urbanicity (March et al., Reference March, Hatch, Morgan, Kirkbride, Bresnahan, Fearon and Susser2008; Vassos, Pedersen, Murray, Collier, & Lewis, Reference Vassos, Pedersen, Murray, Collier and Lewis2012), neighbourhood ethnic density (Schofield et al., Reference Schofield, Thisted Horsdal, Das-Munshi, Thygesen, Pedersen, Morgan and Agerbo2023), higher latitude (Saha, Chant, Welham, & McGrath, Reference Saha, Chant, Welham and McGrath2006), socioeconomic inequality (Burns & Esterhuizen, Reference Burns and Esterhuizen2008; Kirkbride, Jones, Ullrich, & Coid, Reference Kirkbride, Jones, Ullrich and Coid2014), social fragmentation (Allardyce et al., Reference Allardyce, Gilmour, Atkinson, Rapson, Bishop and McCreadie2005; Ku, Compton, Walker, & Druss, Reference Ku, Compton, Walker and Druss2021), and patterns of cannabis abuse (Di Forti et al., Reference Di Forti, Quattrone, Freeman, Tripoli, Gayer-Anderson, Quigley and van der Ven2019) have also been associated with increased rates.

The traditional concept of psychosis as occurring only in those who are ill has been challenged by increasing evidence of several psychosis phenotypes which are below the threshold for being clinically relevant or impairing one's global functioning (Van Os, Linscott, Myin-Germeys, Delespaul, and Krabbendam, Reference Van Os, Linscott, Myin-Germeys, Delespaul and Krabbendam2009). Manifestations of non-clinical psychosis are commonly referred to as ‘subclinical psychosis’ (SP) and encompass a broad spectrum of entities along a continuum of frequency and severity.

Psychotic-like Experiences (PLEs) are defined as ‘psychotic symptoms in the absence of illness’ (Kelleher & Cannon, Reference Kelleher and Cannon2011). The estimated prevalence is 7.2% (Linscott & Van Os, Reference Linscott and Van Os2013), more than 2-times higher than the comparable life-time rate of psychotic disorders (3.06%) (Perälä et al., Reference Perälä, Suvisaari, Saarni, Kuoppasalmi, Isometsä, Pirkola and Lönnqvist2007). PLEs are mostly transient and only about 7–8% progress to full-blown psychotic disorder (Hanssen, Bak, Bijl, Vollebergh, & van Os, Reference Hanssen, Bak, Bijl, Vollebergh and van Os2005; Linscott & Van Os, Reference Linscott and Van Os2013).

Schizotypy is a multidimensional range of personality traits on a dimensional continuity with schizophrenia. Schizotypy traits cluster into positive, negative, and disorganized symptoms domains (Kwapil & Barrantes-Vidal, Reference Kwapil and Barrantes-Vidal2015). Contrarily to the initial postulations (Meehl, Reference Meehl1962, Reference Meehl1990; Rado, Reference Rado1953), it is nowadays assumed that schizotypy traits are normally distributed within the general population along continuous dimensions(Claridge, Reference Claridge1997) and are not necessarily linked to psychopathology (Mohr & Claridge, Reference Mohr and Claridge2015). Nevertheless, schizotypy can predict the onset of psychosis (Debbané et al., Reference Debbané, Eliez, Badoud, Conus, Flückiger and Schultze-Lutter2015; Flückiger et al., Reference Flückiger, Ruhrmann, Debbané, Michel, Hubl, Schimmelmann and Schultze-Lutter2016; Salokangas et al., Reference Salokangas, Dingemans, Heinimaa, Svirskis, Luutonen, Hietala and Klosterkötter2013; Shah et al., Reference Shah, Eack, Montrose, Tandon, Miewald, Prasad and Keshavan2012).

Given the continuity between SP and threshold psychotic disorders, some authors have proposed the use of population-level measures of PLEs or schizotypy as surrogates of clinical disorders for research purposes (Szöke, Kirkbride, & Schürhoff, Reference Szöke, Kirkbride and Schürhoff2014). While prior research has shown a substantial overlap in terms of risk factors between clinical and subclinical forms of psychosis (Linscott & Van Os, Reference Linscott and Van Os2013; Pries et al., Reference Pries, Guloksuz, Ten Have, De Graaf, Van Dorsselaer, Gunther and Van Os2018), less is known about geographic variation of SP.

In this context, we aimed to examine whether measures of SP had a within-site variation across 16 sites and 6 different countries and if this variation was in parallel with the previously estimated incidence rates of first-episode psychosis (FEP) within the same catchment areas and timespan. Furthermore, we sought to examine environmental and genetic contributions to the phenotypic SP expression.

Methods

Eu-GEI study

This study is part of the EUropean network of national schizophrenia networks studying Gene–Environment Interactions (EU-GEI study, http://www.eu-gei.eu), a multi-national incidence and case-sibling-control study of genetic and environmental determinants of psychotic disorders (Gayer-Anderson et al., Reference Gayer-Anderson, Jongsma, Di Forti, Quattrone, Velthorst, de Haan and Morgan2020). Three groups of participants were recruited: (1) first-episode psychosis (FEP) patients aged 18–64; (2) population-based healthy controls within the same age-span and catchment area; (3) siblings of FEP participants. The recruitment took place between 1 May 2010 and 1 April 2015 and involved 17 centres in England (South-East London, Cambridgeshire & Peterborough), France (20th arrondissement of Paris, Val-de-Marne, Puy-de-Dôme), the Netherlands (central Amsterdam, Gouda&Voorhout), Italy (part of the Veneto region, Bologna municipality, and Palermo), Spain (Madrid [Vallecas], Barcelona, Valencia, Oviedo, Santiago, and Cuenca), and Brazil (Ribeirão Preto). Ethical approval was granted in each study centre.

In this investigation, we included only population-based controls.

Study population

Population-based controls aged 18–64 years were recruited from the same catchment areas as FEP patients and over the same time span. In each area, the recruitment was conducted using a mixture of random and quota-sampling strategies, to maximize representativeness to the population-at-risk by age, sex and ethnicity. Quotas for sampling were derived from 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., Reference Di Forti, Quattrone, Freeman, Tripoli, Gayer-Anderson, Quigley and van der Ven2019; Gayer-Anderson et al., Reference Gayer-Anderson, Jongsma, Di Forti, Quattrone, Velthorst, de Haan and Morgan2020).

For the current study, one French site (20th arrondissement of Paris) was excluded as no controls were recruited there.

Measures

Outcome. Our primary outcomes were dimensions of SP in the general population. We measured schizotypy with the Structured Interview for Schizotypy-Revised (SIS-R) (Kendler, Lieberman, & Walsh, Reference Kendler, Lieberman and Walsh1989; Vollema & Ormel, Reference Vollema and Ormel2000) and PLEs with the Community Assessment of Psychic Experiences (CAPE) (Konings, Bak, Hanssen, Van Os, & Krabbendam, Reference Konings, Bak, Hanssen, Van Os and Krabbendam2006).

SIS-R

The SIS-R (Kendler et al., Reference Kendler, Lieberman and Walsh1989; Vollema & Ormel, Reference Vollema and Ormel2000) is a semi-structured interview containing 20 schizotypal symptoms and 11 schizotypal signs rated on a 4-point scale (from ‘absent’ to ‘severe’). It covers three dimensions of schizotypal personality: cognitive-perceptual alterations, disorganization, and negative dimension. Details of how we operationalised SIS-R scores as our outcome measure for statistical analyses are provided below.

CAPE

The CAPE (Konings et al., Reference Konings, Bak, Hanssen, Van Os and Krabbendam2006) (www.cape42.homestead.com) provides a self-reported measure of lifetime PLEs. It has 42 items rated on a 4-point scale (from ‘never’ to ‘nearly always’). The CAPE covers three domains: depressive, negative, and positive symptomatology. Details of how we operationalised CAPE scores as our outcome measure for statistical analyses are provided below.

FEP incidence

A previous EU-GEI study (Jongsma et al., Reference Jongsma, Gayer-Anderson, Lasalvia, Quattrone, Mulè, Szöke and Cristofalo2018) estimated the incidence of FEP across 17 sites in the counties involved in the project. All individuals who had contact with mental health services in the catchment areas for a suspected FEP were identified and ascertained. Potential participants were included if: (1) were resident within the catchment area; (2) were aged between 18 and 64 years; and (3) met the diagnostic criteria for FEP according to the International Classification of Diseases, Tenth Revision (ICD-10), codes F20-F33. Individuals who had previous contact with mental health services for psychosis, organic psychosis, or transient psychotic symptoms due to acute intoxication, as defined by ICD-10 codes F1X.5, were excluded. The most accurate local census data stratified by age, sex, and ethnicity were used in each catchment area to estimate the population-at-risk. Person-years at risk were estimated multiplying the population-at-risk by the duration of case ascertainment in each study site. Crude incidence rates of FEP (ICD-10 codes F20-F33) per 100 000 person-years at risk were then estimated for each study site. We used standardized incidence rates of FEP to examine the association between variation of SP across study sites by local FEP incidence.

Socio-demographic characteristics

We collected data on age, sex (male/female), migrant status (foreign-born), education (no qualification/school-college-vocational/higher), relational (single/other) and employment status (unemployed/other), using an amended version of the Medical Research Council Socioeconomic Schedule (Mallet, Reference Mallet1997).

Other exposures

Current cannabis (no/yes) use was derived from a modified version of the Cannabis Experience Questionnaire (Di Forti et al., Reference Di Forti, Quattrone, Freeman, Tripoli, Gayer-Anderson, Quigley and van der Ven2019). Childhood trauma was assessed through Childhood Trauma Questionnaire (CTQ) (Bernstein et al., Reference Bernstein, Stein, Newcomb, Walker, Pogge, Ahluvalia and Zule2003) and Childhood Experience of Care and Abuse (CECA) (Bifulco, Brown, & Harris, Reference Bifulco, Brown and Harris1994). From CTQ, we derived the mean score of the five subscales (emotional abuse, emotional neglect, physical abuse, physical neglect, and sexual abuse). From the CECA interview, we only used the item on having been the victim of bullying.

Genetic liability

We used Schizophrenia Polygenic Risk Scores (SCZ-PRS) as a measure of genetic liability to schizophrenia (Lewis & Knight, Reference Lewis and Knight2012). Samples for genomic study were processed at the MRC Centre for Neuropsychiatric Genetics and Genomics in Cardiff (UK). The calculation of SCZ-PRS was based on the latest Psychiatric Genomics Consortium (PGC) data (Trubetskoy et al., Reference Trubetskoy, Pardiñas, Qi, Panagiotaropoulou, Awasthi, Bigdeli and van Os2022). The procedure is detailed elsewhere (Quattrone et al., Reference Quattrone, Reininghaus, Richards, Tripoli, Ferraro, Quattrone and D'Andrea2021).

Missing data

The proportion of missing values was low, ranging from 0.1% on ethnicity to 11.6% on one SIS-R item. Missing data were handled by multiple imputation (details in online Supplementary Materials).

Statistical analyses

First, we ran descriptive analyses on the whole sample to obtain frequencies and means of the study variables.

Then, data from SIS-R and CAPE were analysed using Multidimensional Item Response Theory (MIRT) to operationalise our outcome measures. Bifactor models were chosen for both phenotypes based on a previous EU-GEI publication (Quattrone et al., Reference Quattrone, Reininghaus, Richards, Tripoli, Ferraro, Quattrone and D'Andrea2021) and on empiric evidence. For SIS-R we thus extracted a general factor (SIS-RGEN) along with three uncorrelated factors, i.e. Cognitive-Perceptual (SIS-RCOG−PER), Negative (SIS-RNEG), and Paranoid (SIS-RPAR). For CAPE, we estimated the general factor (CAPEGEN) and the depressive (CAPEDEP), negative (CAPENEG), and positive (CAPEPOS) domains. For details see online Supplementary Materials.

The CAPE and SIS-R factor scores were extracted and compared across the EU-GEI sites using one-way ANOVA followed by Tukey HSD post-hoc analysis. For each site, the mean score of each factor was compared with FEP incidence using Pearson's product-moment correlations.

Then, we used multilevel regression to investigate schizotypy and PLEs by study site considering both individual and contextual factors (FEP incidence) (Von Korff, Koepsell, Curry, & Diehr, Reference Von Korff, Koepsell, Curry and Diehr1992). All models were random intercept models allowing our outcome measures to vary across catchment areas (N = 15 sites for SIS-RGEN analyses and N = 16 sites for CAPEGEN analyses). First, we looked for evidence of significant between-sites variation of SIS-RGEN and CAPEGEN by comparing null single-level model with the correspondent null two-level model (individuals – level 1 – nested in recruitment sites – level 2) with a likelihood-ratio test, by inspecting the estimated shrunken residuals on a ‘caterpillar’ plot (Rasbash, Steele, Browne, Goldstein, & Charlton, Reference Rasbash, Steele, Browne, Goldstein and Charlton2012), and by examining the intra-class correlation coefficients (ICC). In two-level models the ICC represents the proportion of variance that is accounted for by the group level and ranges between 0 and 1, with the value of 0 or close suggesting that a multilevel structure is probably absent (Merlo, Chaix, Yang, Lynch, & Merlo, Reference Merlo, Chaix, Yang, Lynch and Merlo2005). Moreover, we compared the variance explained across three different models at both individual and site levels, measuring their proportional change in variance (PCV) (Merlo et al., Reference Merlo, Chaix, Yang, Lynch and Merlo2005). Model 1 was adjusted for age and sex. Model 2 was further adjusted for the following individual-level variables: education, relational status, employment status, current cannabis use, migrant status, childhood trauma, and bullying. Finally, in Model 3 we added standardised FEP incidence rate as site-level variable. The choice of covariates was made a priori based on extensive literature review on the subject (Linscott & Van Os, Reference Linscott and Van Os2013; Pignon et al., Reference Pignon, Lajnef, Kirkbride, Peyre, Ferchiou, Richard and Schürhoff2021; Van Os et al., Reference Van Os, Linscott, Myin-Germeys, Delespaul and Krabbendam2009) and consensus of the research team. Multicollinearity was checked by examining the correlation between the independent variables and estimating the variance inflation factors (VIF) for each covariate (online Supplementary Materials). We further tested the distribution of the standardized residuals by visually inspecting the qq-plots and using the Kolmogorov–Smirnov test (online Supplementary Materials).

Lastly, we tested for the association between SP and SCZ-PRS, to see whether genetic liability to schizophrenia explained schizotypy or PLEs in our sample. We ran two-level linear regression models with either SIS-R or CAPE general factor scores as dependent variables. To control for population stratification, we conducted a principal component analysis to generate 10 principal components which were used as covariates in the regression models. Analyses were further adjusted conducted covarying for age and sex.

Sensitivity analyses were performed for the Models 1–3 on the complete-cases sample. Inverse probability weights were generated for each participant based on key demographics (age, sex, ethnicity) to account for the potential over- or under-sampling and used in sensitivity analyses (online Supplementary Materials).

Analyses were performed using RStudio R version 3.6.3 (RStudio Team, 2020) and Stata 17 (StataCorp., 2021).

Results

We recruited 1497 controls. The sample characteristics are presented in online Supplemental Table S1.

Bifactor model of SIS-R and CAPE SP

The bifactor model was found to be the best fit for the SIS-R items (online Supplemental Table S2). The factor loadings and communalities are shown in online Supplemental Table S3. All items showed moderate to strong positive loading on the general dimension. The magnitude of item loadings on the specific factors was also moderate to strong, apart from the items ‘hypersensitivity’ and ‘suspiciousness’ which were therefore kept in the general factor only.

The factor loadings of CAPE items are presented in online Supplemental Table S4.

Variation of SIS-R and CAPE and correlation with FEP incidence by study site

One-way ANOVA showed variation in all the domains of the SIS-R by study site (online Supplemental Figure S1). Scores on the SIS-RGEN (F = 21.419; p < 0.001, partial-η 2 = 0.180) were higher in Amsterdam, Barcelona, Gouda&Voorhout, and London compared with the other sites. SIS-RCOG−PER factor scores were higher in Amsterdam, Barcelona, London, and Sao Paulo and were lower in Oviedo, Santiago, and Palermo (F = 18.859; p < 0.001, partial-η 2 = 0.162). SIS-RNEG scores were higher in Amsterdam, Paris, Gouda&Voorhout, and London (14.567; p < 0.001, partial-η 2 = 0.130). Finally, SIS-RPAR had the lowest degree of variation by site (F = 4.116; p < 0.001, partial-η 2 = 0.040), with greater scores in the Spanish sites (Oviedo, Santiago, and Valencia).

Compared with SIS-R, CAPE SP domains showed less dispersion by study sites (online Supplemental Figure S2), especially regarding the CAPENEG (F = 1.517; p = 0.097, partial-η 2 = 0.014). The CAPEGEN scores were greater in Bologna, Palermo, and Santiago (F = 3.366; p < 0.001, partial-η 2 = 0.035), while the CAPEPOS was more represented in Bologna, Cuenca, London, and Palermo (F = 9.345; p < 0.001, partial-η 2 = 0.094). The CAPEDEP scores were generally higher among the French sites (F = 1.854; p = 0.024, partial-η 2 = 0.019).

Both SIS-RGEN (Pearson's r = 0.684; p = 0.005) and SIS-RNEG (Pearson's r = 0.642; p = 0.010) were strongly correlated with the incidence of FEP across the study centres. The correlation coefficients for SIS-RCOG−PER and SIS-RPAR factors were of 0.439 (p = 0.102) and −0.384 (p = 0.158) respectively. On the other hand, CAPE domains were not correlated with FEP incidence. The Pearson's coefficients ranged between −0.146 (p = 0.590) for CAPEGEN and 0.157 (p = 0.564) for CAPENEG (Fig. 1).

Figure 1. Crude incidence rates for all psychosis across the EU-GEI sites plotted against the mean z-scores of dimensions of schizotypy (A) and PLEs (B).

Multi-level regression analysis

Random and fixed effects from the two-level regression models are presented in Tables 12.

Table 1. Multilevel regression analysis of SIS-R general factor score from 15 catchment areas in Europe and Brazil

CTQ Childhood Trauma Questionnaire; SCZ-PRS, Schizophrenia Polygenic Risk Score; FEP, First-Episode Psychosis; PCV, Proportional change in variance; ICC, Intraclass correlation coefficitent; AIC, Akaike Information Criterion. Coefficients in bold are statistically significant (p < 0.05).

Table 2. Multilevel regression analysis of CAPE General factor score from 16 catchment areas in Europe and Brazil

CTQ, Childhood Trauma Questionnaire; SCZ-PRS, Schizophrenia Polygenic Risk Score; FEP, First-Episode Psychosis; PCV, Proportional change in variance; ICC, Intraclass correlation coefficitent; AIC, Akaike Information Criterion; Bayesian Information Criterion. Coefficients in bold are statistically significant (p < 0.05).

Random effects

The site-level variance of the SIS-RGEN null model was 0.10 (95%CI = 0.05–0.21), with an ICC of 0.15 (95%CI = 0.007–0.27), suggesting that about 15% of the individual differences in schizotypy general factor was at the site level. The likelihood-ratio test comparing the null single-level model (log-likelihood = −1693) with the null two-level model (log-likelihood = −1583) was significant (χ2 = 220.09; p < 0.001). The caterpillar plot (Fig. 2A) shows the distribution of the shrunken residuals around the mean, with 9 out of 15 study sites included in SIS-R analysis having a significant shift from the mean. The individual-level variance, however, was much higher (0.56, 95%CI = 0.52–0.61) and accounted for the rest of the total variance. Adding age and sex to the null model did not alter the PCV at both site- and individual levels. The addition of the other individual-level covariates (Model 2) brought a 20% reduction in the unexplained site-level variance and a 12.5% reduction in the unexplained individual-level variable. Finally, adding the incidence of FEP as a level-two variable (Model 3) resulted in a further reduction of the unexplained site-level variance of about 40.0%, while the individual-level variance remained unchanged. The ICC of the final model was 0.08 (95%CI = 0.03–0.17), with a PCV of 60.0% compared with the null model.

Figure 2. Caterpillar plot of shrunken residuals from the SIS-RGEN (A) and CAPEGEN (B) multilevel regression models.

Site-level variation estimated from the CAPEGEN null two-level model was extremely low (0.02, 95%CI = 0.01–0.08) with an ICC of 0.03 (0.01–0.08). The likelihood-ratio test, however, showed that a two-level model (log-likelihood = 1954) was still significantly better than the single-level model (log-likelihood = 1962) (χ2 = 14.90; p < 0.001). As shown in the caterpillar plot (Fig. 2B), only for 2 sites (Palermo and Madrid) we observed a significant shift from the mean. In this case, the individual-level variance accounted for almost the total CAPEGEN variance and for this reason we considered only the change in the individual-level PCV to compare the models. Adjusting the null model for age and sex only decreased the individual unexplained variance of 1.3%. When all the individual-level variables were added (Model 2), there was a 12.6% decrease in the PCV. The addition of FEP incidence did not affect the PCV (Model 3).

Fixed effects

Schizotypy, as measured by SIS-RGEN was associated with sociodemographic characteristics such as level of education (school/college/vocational v. higher: β = 0.201,95%CI = −0.118 to 0.284; p < 0.001), unemployment (β = 0.121,95%CI = 0.013–0.229; p = 0.027), and migrant status (β = 0.108,95%CI = 0.012–0.205; p = 0.028). The mean CTQ score (β = 0.070,95%CI = −0.053 to 0.088; p < 0.001) and experiences of bullying (β = 0.260,95%CI = −0.172 to 0.349; p < 0.001) were also associated with schizotypy. Finally, we found a 0.227 increase in the SIS-RGEN factor score per each unit of standardised incidence rate (β = 0.227,95%CI = 0.101–0.354; p < 0.001).

PLEs, as measured by the CAPEGEN factor score, were lower in males (β = −0.118,95%CI = −0.204 to −0.032; p = 0.007), and higher in individuals who declared to be single (β = 0.137,95%CI = 0.041–0.234; p = 0.005) or unemployed (β = 0.168,95%CI = 0.046–0.291; p = 0.007). PLEs were also associated with current cannabis use (β = 0.153,95%CI = 0.011–0.295; p = 0.035), CTQ (β = 0.114,95%CI = 0.093–0.134; p < 0.001) and bullying (β = 0.310,95%CI = −0.210 to 0.410; p < 0.001). In the final model higher standardised incidence was associated with a slight CAPEGEN reduction (β = −0.098,95%CI = −0.173 to −0.023; p = 0.010).

Association of SP with genetic liability

We found evidence of an association between SCZ-PRS and SIS-RGEN in both unadjusted (β = 0.076,95%CI = −0.021 to 0.131; p = 0.006) and adjusted regression analysis (β = 0.078,95%CI = −0.023 to 0.133; p = 0.007). The SCZ-PRS was also associated with an increased score on the CAPEGEN (β = 0.075,95%CI = 0.011–0.139; p = 0.021). The association withheld adjustment for age and sex (β = 0.075,95%CI = 0.011–0.138; p = 0.021) (Table 3).

Table 3. SP by SCZ-PRS

Significant coefficients (<0.05) are in bold. adjusted for age, sex, and ten principal components.

SCZ-PRS, schizophrenia polygenic risk score; SIS-RGEN, SIS-R general factor; CAPEGEN, CAPE general factor.

β: regression coefficient; 95%CI: 95% confidence interval.

Sensitivity analyses

Sensitivity analyses on the weighted complete-cases sample yielded very similar results to those from analyses conducted on the imputed dataset (online Supplementary Materials).

Discussion

Main findings

We measured two different SP phenotypes along with their symptom dimensions and examined their variation across several sites from different countries. The factor structures of both phenotypes were better explained by bifactor models, supporting the hypothesis of general psychopathological constructs of SP linking specific symptomatic domains.

PLEs had a more uniform distribution across study sites, with barely 3% of variance attributable to the environment. There was no evidence of a correlation between any of the PLEs dimensions and incidence of psychosis. PLEs expression relied almost uniquely on individual factors, such as female sex, single status, unemployment, current cannabis use, and childhood adverse events.

Schizotypy varied largely between sites and up to 15% of its variance could be attributed to site-level characteristics. Adding local FEP incidence saw a 60% reduction in the unexplained between-sites variance compared with the null model. Furthermore, general and negative dimensions of schizotypy were strongly correlated with incidence of FEP, being more represented in those sites where FEP incidence was higher. At the individual level, schizotypy was associated with lower education, unemployment, migrant status, and childhood adversities.

Finally, we found that SCZ-PRS was associated with both phenotypes.

Comparison with previous literature

To the best of our knowledge, this is the first study which examined variation of SP phenotypes across precisely defined catchment areas. Previous studies have only provided information on the variation of schizotypal traits or PLEs between countries, not allowing speculation on specific site-level potential determinants, making it difficult to directly compare our results with existing literature.

One study (Fonseca-Pedrero et al., Reference Fonseca-Pedrero, Chan, Debbané, Cicero, Zhang, Brenner and Ortuño-Sierra2018) involving 27 001 individuals from 12 countries found a considerable variation in the expression of schizotypal traits by country, but effect sizes were generally smaller than those we found. In line with our findings, both Spain and Italy scored lower than the UK on overall schizotypy. Pre-existing studies had only compared two or four countries (Fonseca-Pedrero et al., Reference Fonseca-Pedrero, Ortuño-Sierra, Sierro, Daniel, Cella, Preti and Mason2015; Fonseca-Pedrero, Cohen, Ortuño-Sierra, de Álbeniz, & Muñiz, Reference Fonseca-Pedrero, Cohen, Ortuño-Sierra, de Álbeniz and Muñiz2017; Kwapil, Ros-Morente, Silvia, & Barrantes-Vidal, Reference Kwapil, Ros-Morente, Silvia and Barrantes-Vidal2012; Ortuño-Sierra et al., Reference Ortuño-Sierra, Badoud, Knecht, Paino, Eliez, Fonseca-Pedrero and Debbané2013; Sierro, Rossier, Mason, & Mohr, Reference Sierro, Rossier, Mason and Mohr2015). Moreover, none of these used clinician-rated instruments and the study designs were not conceived purposedly to recruit samples representative of the population at risk for FEP in each area.

A multi-national study (Nuevo et al., Reference Nuevo, Chatterji, Verdes, Naidoo, Arango and Ayuso-Mateos2012) found that the prevalence of at least one positive PLEs varied almost 40-fold over 52 countries. This study did not formally test for variation by country, and the broad heterogeneity of culture background and socioeconomic characteristics may have contributed to such divergent cross-national estimates. Two more studies(Jaya et al., Reference Jaya, Wüsten, Alizadeh, Van Amelsvoort, Bartels-Velthuis, Van Beveren and Lincoln2022; McGrath et al., Reference McGrath, Saha, Al-Hamzawi, Alonso, Bromet, Bruffaerts and Kessler2015) compared countries by income, following the World Bank classification(Fantom & Serajuddin, Reference Fantom and Serajuddin2016), with contrasting results. Our study included only countries classified as upper-middle (Brazil) or high-income countries (the rest).

In our sample, women presented higher levels of PLEs. Previous findings are mixed (Kelleher et al., Reference Kelleher, Connor, Clarke, Devlin, Harley and Cannon2012; Linscott & Van Os, Reference Linscott and Van Os2013; Nuevo et al., Reference Nuevo, Chatterji, Verdes, Naidoo, Arango and Ayuso-Mateos2012; Zammit et al., Reference Zammit, Kounali, Cannon, David, Gunnell, Heron and Lewis2013). Current cannabis use was also associated with increased PLEs which is consistent with previous literature (Linscott & Van Os, Reference Linscott and Van Os2013). A recent EU-GEI study found that cannabis contributes to the emergence of PLEs independently of underlying genetic proneness to schizophrenia (Quattrone et al., Reference Quattrone, Reininghaus, Richards, Tripoli, Ferraro, Quattrone and D'Andrea2021). Being single was also significantly associated with PLEs, consistent with previous findings (Linscott & Van Os, Reference Linscott and Van Os2013; Pignon et al., Reference Pignon, Schürhoff, Szöke, Geoffroy, Jardri, Roelandt and Amad2018; Saha, Scott, Varghese, & McGrath, Reference Saha, Scott, Varghese and McGrath2013). In our study, unemployment predicted both PLEs and schizotypy. Rates of employment among individuals suffering from schizophrenia are very low (Evensen et al., Reference Evensen, Wisløff, Lystad, Bull, Ueland and Falkum2016; Holm, Taipale, Tanskanen, Tiihonen, & Mitterdorfer-Rutz, Reference Holm, Taipale, Tanskanen, Tiihonen and Mitterdorfer-Rutz2021), while previous studies have failed to demonstrate such an association with SP phenotypes (DeVylder, Lehmann, & Chen, Reference DeVylder, Lehmann and Chen2015; Linscott & Van Os, Reference Linscott and Van Os2013; Saha et al., Reference Saha, Scott, Varghese and McGrath2013). Childhood adversities were associated with both phenotypes, coherently with prior reports(McGrath et al., Reference McGrath, McLaughlin, Saha, Aguilar-Gaxiola, Al-Hamzawi, Alonso and Kessler2017; Pignon et al., Reference Pignon, Lajnef, Kirkbride, Peyre, Ferchiou, Richard and Schürhoff2021; Velikonja, Fisher, Mason, & Johnson, Reference Velikonja, Fisher, Mason and Johnson2015) and bullying had the greatest effect size, consistent with previous research (Campbell & Morrison, Reference Campbell and Morrison2007; Horrevorts, Monshouwer, Wigman, & Vollebergh, Reference Horrevorts, Monshouwer, Wigman and Vollebergh2014; Wolke, Lereya, Fisher, Lewis, & Zammit, Reference Wolke, Lereya, Fisher, Lewis and Zammit2014; Wong & Raine, Reference Wong and Raine2018). Migrant status and lower level of education were associated with schizotypy but not with PLEs. It is known that migrants have higher rates of psychotic disorders(Selten et al., Reference Selten, Van Der Ven and Termorshuizen2019; Tarricone et al., Reference Tarricone, D'Andrea, Jongsma, Tosato, Gayer-Anderson, Stilo and Morgan2021), but there is still not sufficient evidence to establish whether they are also at higher risk of SP (Leaune et al., Reference Leaune, Dealberto, Luck, Grot, Zeroug-Vial, Poulet and Brunelin2019; Tortelli et al., Reference Tortelli, Nakamura, Suprani, Schürhoff, Van der Waerden, Szöke and Pignon2018). Interestingly, rates of psychosis among migrants also present a considerable between-sites variation, being higher in those sites where the native-born population presents higher rates (Termorshuizen et al., Reference Termorshuizen, Van Der Ven, Tarricone, Jongsma, Gayer-Anderson, Lasalvia and Selten2020).

We found an association between genetic proneness to schizophrenia and both SP phenotypes. While previous research has consistently replicated the finding regarding PLEs (Legge et al., Reference Legge, Jones, Kendall, Pardiñas, Menzies, Bracher-Smith and Walters2019; Quattrone et al., Reference Quattrone, Reininghaus, Richards, Tripoli, Ferraro, Quattrone and D'Andrea2021; Ronald & Pain, Reference Ronald and Pain2018), the association between genetic liability and schizotypy is less evident. Previous studies reported no association (Nenadić et al., Reference Nenadić, Meller, Schmitt, Stein, Brosch, Mosebach and Kircher2022) or even a counterintuitive inverse relationship (Hatzimanolis et al., Reference Hatzimanolis, Avramopoulos, Arking, Moes, Bhatnagar, Lencz and Stefanis2018; Van Os et al., Reference Van Os, Pries, Delespaul, Kenis, Luykx, Lin and Guloksuz2020). However, it is likely that, as is the case with schizophrenia, the expression of schizotypal traits is influenced by both genetic proneness and exposure to environmental risk factors (Barrantes-Vidal, Grant, & Kwapil, Reference Barrantes-Vidal, Grant and Kwapil2015; Pries et al., Reference Pries, Klingenberg, Menne-Lothmann, Decoster, van Winkel, Collip and Guloksuz2020a; Pries et al., Reference Pries, Dal Ferro, Van Os, Delespaul, Kenis, Lin and Guloksuz2020b).

Schizotypy showed a significant between-sites variation and was strongly intertwined with psychosis incidence within the same catchment area. These findings support the ‘psychosis continuum’ model (Van Os et al., Reference Van Os, Linscott, Myin-Germeys, Delespaul and Krabbendam2009) and the hypothesis that there are common causes of threshold disorders and subclinical expression of psychosis. Future studies need to clarify the mechanisms underpinning such variation of psychosis spectrum phenotypes by examining variability in the factors that putatively contribute to this discrepancy. The epidemiological continuity we observed adds value to the role of schizotypy in the research of psychosis etiology. Furthermore, by encompassing a wide array of psychopathological manifestations along the psychosis continuum, schizotypy allows to assess a broad range of phenotypes increasing the power to capture relevant factors associated with the etiogenic pathways of psychoses (Barrantes-Vidal et al., Reference Barrantes-Vidal, Grant and Kwapil2015). With regards to the latter, this highlights the role of contextual factors implicated in the risk of psychosis. Further research is thus needed to test the differential effect across diverse sites of candidate area-level factors. In this context, the EU-GEI incidence study (Jongsma et al., Reference Jongsma, Gayer-Anderson, Lasalvia, Quattrone, Mulè, Szöke and Cristofalo2018) has underscored the relevance of social deprivation indicators, such as the proportion of owner-occupied houses, of single households or unemployment across the catchment areas. On the other hand, in our analyses PLEs were ubiquitous and their distribution was not related to the incidence of psychotic disorders. This suggests that factors associated with the development of schizotypy and threshold psychotic disorders might be less relevant for PLEs. Furthermore, previous studies suggest that schizotypy might be a better predictor of psychosis compared with PLEs (Flückiger et al., Reference Flückiger, Ruhrmann, Debbané, Michel, Hubl, Schimmelmann and Schultze-Lutter2016; Salokangas et al., Reference Salokangas, Dingemans, Heinimaa, Svirskis, Luutonen, Hietala and Klosterkötter2013; Shah et al., Reference Shah, Eack, Montrose, Tandon, Miewald, Prasad and Keshavan2012). Previous literature on PLEs shows that they are more prevalent in early life-stages, such childhood and adolescence (Healy et al., Reference Healy, Brannigan, Dooley, Coughlan, Clarke, Kelleher and Cannon2019; Kelleher et al., Reference Kelleher, Connor, Clarke, Devlin, Harley and Cannon2012; Maijer, Begemann, Palmen, Leucht, & Sommer, Reference Maijer, Begemann, Palmen, Leucht and Sommer2018) to decrease thereafter (Calkins et al., Reference Calkins, Moore, Merikangas, Burstein, Satterthwaite, Bilker and Gur2014; Peters, Joseph, & Garety, Reference Peters, Joseph and Garety1999), being mostly transient in nature(Linscott & Van Os, Reference Linscott and Van Os2013). Nevertheless, recent reports from the Adolescent Brain Cognitive Development (ABCD) (Karcher et al., Reference Karcher, Loewy, Savill, Avenevoli, Huber, Makowski and Barch2022a., Reference Karcher, Paul, Johnson, Hatoum, Baranger, Agrawal and Bogdan2022b) study showed that distressing and persisting PLEs were associated with a broad range of negative outcomes in terms of mental health (not limited to psychosis) and general functioning, aligning with previous reports (Van Der Steen et al., Reference Van Der Steen, Myin-Germeys, Van Nierop, Ten Have, De Graaf, Van Dorsselaer and Van Winkel2019; Van Nierop et al., Reference Van Nierop, Van Os, Gunther, Myin-Germeys, De Graaf, Ten Have and Van Winkel2012).

Strengths and limitations

To the best of our knowledge, this is the first study that used a multi-level regression analysis approach to examine the variation of SP phenotypes across a mixture of urban and rural sites, considering FEP incidence over the same catchment areas and timespan. The control recruitment strategy was specifically designed to obtain a sample broadly representative of the population at-risk by age, sex, and ethnicity (Gayer-Anderson et al., Reference Gayer-Anderson, Jongsma, Di Forti, Quattrone, Velthorst, de Haan and Morgan2020). In some sites, controls were significantly shifted towards a younger age compared with the local population(Jongsma et al., Reference Jongsma, Gayer-Anderson, Tarricone, Velthorst, van der Ven, Quattrone and Kirkbride2020). Nevertheless, the uniform strategy for recruitment and ascertainment of participants increases the reliability of between-sites comparisons. Differently, from previous research on SP variation (Fonseca-Pedrero et al., Reference Fonseca-Pedrero, Ortuño-Sierra, Sierro, Daniel, Cella, Preti and Mason2015; Fonseca-Pedrero et al., Reference Fonseca-Pedrero, Cohen, Ortuño-Sierra, de Álbeniz and Muñiz2017, Reference Fonseca-Pedrero, Chan, Debbané, Cicero, Zhang, Brenner and Ortuño-Sierra2018; Jaya et al., Reference Jaya, Wüsten, Alizadeh, Van Amelsvoort, Bartels-Velthuis, Van Beveren and Lincoln2022; Kwapil et al., Reference Kwapil, Ros-Morente, Silvia and Barrantes-Vidal2012; McGrath et al., Reference McGrath, Saha, Al-Hamzawi, Alonso, Bromet, Bruffaerts and Kessler2015; Ortuño-Sierra et al., Reference Ortuño-Sierra, Badoud, Knecht, Paino, Eliez, Fonseca-Pedrero and Debbané2013; Sierro et al., Reference Sierro, Rossier, Mason and Mohr2015), we did not analyse data aggregated by countries, allowing for direct comparisons between different sites within a single country, which may differ for core features such as urbanicity, ethnic composition, or poverty. While previous studies on the cross-national variation of schizotypy relied on self-reported questionnaires (Fonseca-Pedrero et al., Reference Fonseca-Pedrero, Ortuño-Sierra, Sierro, Daniel, Cella, Preti and Mason2015; Fonseca-Pedrero et al., Reference Fonseca-Pedrero, Cohen, Ortuño-Sierra, de Álbeniz and Muñiz2017, Reference Fonseca-Pedrero, Chan, Debbané, Cicero, Zhang, Brenner and Ortuño-Sierra2018; Ortuño-Sierra et al., Reference Ortuño-Sierra, Badoud, Knecht, Paino, Eliez, Fonseca-Pedrero and Debbané2013), we used a clinician-administered interview (SIS-R). Regarding PLEs, a previous EU-GEI paper demonstrated that CAPE presented equivalent factorial structure, factor loadings, and thresholds across the study countries(Pignon et al., Reference Pignon, Peyre, Ferchiou, van Os, Rutten, Murray and Author2019). Interestingly, we did not observe any correlation between SIS-R and CAPE dimensions. Of note, sites with a higher self-reported score on positive PLEs did not show as much comparably high scores on the clinician-rated positive schizotypy. This could be explained by cultural differences that could increase the likelihood of reporting PLEs, such as culturally shaped different levels of PLEs acceptance and experience-related different degrees of distress. However, we have shown that variation in CAPE dimensions across sites was not relevant. Thus, cultural factors are unlikely to have introduced biases in our analyses.

Several limitations need to be acknowledged. First of all, we only included sites located in upper-middle or high-income countries (Fantom & Serajuddin, Reference Fantom and Serajuddin2016). This could have limited our ability to a detect more significant variation of PLEs, given previous evidence (though contrasting) (Jaya et al., Reference Jaya, Wüsten, Alizadeh, Van Amelsvoort, Bartels-Velthuis, Van Beveren and Lincoln2022; McGrath et al., Reference McGrath, Saha, Al-Hamzawi, Alonso, Bromet, Bruffaerts and Kessler2015). Nevertheless, heterogeneity of countries by economy could have introduced biases in the assessment of variation due to existing differences in prevalence and incidence of psychotic disorders between higher and lower income countries (McGrath, Saha, Chant, & Welham, Reference McGrath, Saha, Chant and Welham2008), which probably reflect considerable divergences in the societal context. Our sample did not comprise individuals aged under 18 years, thus excluding the age groups with high prevalence of PLEs and schizotypal traits (Fonseca-Pedrero, Lemos-Giráldez, Paino, Sierra-Baigrie, & Muñiz, Reference Fonseca-Pedrero, Lemos-Giráldez, Paino, Sierra-Baigrie and Muñiz2012; Healy et al., Reference Healy, Brannigan, Dooley, Coughlan, Clarke, Kelleher and Cannon2019; Kelleher et al., Reference Kelleher, Connor, Clarke, Devlin, Harley and Cannon2012; Maijer et al., Reference Maijer, Begemann, Palmen, Leucht and Sommer2018). However, CAPE investigates lifetime PLEs, while, for schizotypy, the persistence of traits in later life stages could be more insightful. All SIS-R items related to the disorganization domain were excluded due to lack of valid frequency. Recent network analyses (Christensen, Gross, Golino, Silvia, & Kwapil, Reference Christensen, Gross, Golino, Silvia and Kwapil2019; Polner et al., Reference Polner, Faiola, Urquijo, Meyhöfer, Steffens, Rónai and Ettinger2019) have shown that disorganized schizotypy could be seen as a higher-order factor mediating the presentation of negative and positive symptoms. Furthermore, only about 13% of the individual unexplained variance of SIS-R and CAPE general factors was accounted for by our explanatory variables in the fully adjusted models. We have not included measures of general or social cognition, which are likely to contribute. Finally, analyses of genetic liability were performed only in people of European ancestry, limiting the generalisability of the findings to Caucasian European populations.

Relevance and implications

Both phenotypes of SP are potentially associated with poorer mental health and lower functioning at multiple levels. The differential patterns of variation between the two have several implications. We showed that PLEs present a low degree of variance between sites with their presentation relying almost uniquely on individual factors, including genetic liability. Cannabis use can be a target for primary and secondary prevention programmes and effective interventions can be put in place to treat trauma and to support individuals in finding an occupation. Conversely, schizotypy presents a substantial between-sites variation, being more pronounced where FEP incidence peaks. This supports the hypothesis that shared contextual factors influence the local expression of psychosis across the spectrum. High FEP incidence can be considered as a proxy of site-level threats to mental health. Our findings emphasize the need for further research on contextual factors associated with schizotypy in order to increase our understanding of etiology of psychotic disorders.

Supplementary material

The supplementary material for this article can be found at https://doi.org/10.1017/S0033291723003781.

Funding statement

The European Network of National Schizophrenia Networks Studying Gene-Environment Interactions (EU-GEI) Project is funded by grant agreement HEALTH-F2-2010–241909 (Project EU-GEI) from the Seventh Framework Programme. The Brazilian study was funded by grant 2012/0417-0 from the São Paulo Research Foundation.

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Figure 0

Figure 1. Crude incidence rates for all psychosis across the EU-GEI sites plotted against the mean z-scores of dimensions of schizotypy (A) and PLEs (B).

Figure 1

Table 1. Multilevel regression analysis of SIS-R general factor score from 15 catchment areas in Europe and Brazil

Figure 2

Table 2. Multilevel regression analysis of CAPE General factor score from 16 catchment areas in Europe and Brazil

Figure 3

Figure 2. Caterpillar plot of shrunken residuals from the SIS-RGEN (A) and CAPEGEN (B) multilevel regression models.

Figure 4

Table 3. SP by SCZ-PRS

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