Skip to main content Accessibility help
×
×
Home

Information:

  • Access
  • Open access
  • Cited by 2
  • Cited by
    This article has been cited by the following publications. This list is generated based on data provided by CrossRef.

    Carrà, Giuseppe Crocamo, Cristina Bartoli, Francesco Angermeyer, Matthias Brugha, Traolach Toumi, Mondher and Bebbington, Paul 2019. The mediating role of depression in pathways linking positive and negative symptoms in schizophrenia. A longitudinal analysis using latent variable structural equation modelling. Psychological Medicine, p. 1.

    Quattrone, D. Di Forti, M. Murray, R. Reininghaus, U. and Lewis, C. 2019. P.1.29 Are schizophrenia and bipolar polygenic risk scores associated with positive and manic symptoms at psychosis onset?. European Neuropsychopharmacology, Vol. 29, Issue. , p. S654.

    ×

Actions:

      • Send article to Kindle

        To send this article to your Kindle, first ensure no-reply@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about sending to your Kindle. Find out more about sending to your Kindle.

        Note you can select to send to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be sent to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

        Find out more about the Kindle Personal Document Service.

        Transdiagnostic dimensions of psychopathology at first episode psychosis: findings from the multinational EU-GEI study
        Available formats
        ×

        Send article to Dropbox

        To send this article to your Dropbox account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Dropbox.

        Transdiagnostic dimensions of psychopathology at first episode psychosis: findings from the multinational EU-GEI study
        Available formats
        ×

        Send article to Google Drive

        To send this article to your Google Drive account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Google Drive.

        Transdiagnostic dimensions of psychopathology at first episode psychosis: findings from the multinational EU-GEI study
        Available formats
        ×
Export citation

Abstract

Background

The value of the nosological distinction between non-affective and affective psychosis has frequently been challenged. We aimed to investigate the transdiagnostic dimensional structure and associated characteristics of psychopathology at First Episode Psychosis (FEP). Regardless of diagnostic categories, we expected that positive symptoms occurred more frequently in ethnic minority groups and in more densely populated environments, and that negative symptoms were associated with indices of neurodevelopmental impairment.

Method

This study included 2182 FEP individuals recruited across six countries, as part of the EUropean network of national schizophrenia networks studying Gene–Environment Interactions (EU-GEI) study. Symptom ratings were analysed using multidimensional item response modelling in Mplus to estimate five theory-based models of psychosis. We used multiple regression models to examine demographic and context factors associated with symptom dimensions.

Results

A bifactor model, composed of one general factor and five specific dimensions of positive, negative, disorganization, manic and depressive symptoms, best-represented associations among ratings of psychotic symptoms. Positive symptoms were more common in ethnic minority groups. Urbanicity was associated with a higher score on the general factor. Men presented with more negative and less depressive symptoms than women. Early age-at-first-contact with psychiatric services was associated with higher scores on negative, disorganized, and manic symptom dimensions.

Conclusions

Our results suggest that the bifactor model of psychopathology holds across diagnostic categories of non-affective and affective psychosis at FEP, and demographic and context determinants map onto general and specific symptom dimensions. These findings have implications for tailoring symptom-specific treatments and inform research into the mood-psychosis spectrum.

Footnotes

*

Joint first author

For a full list of collaborators, see Appendix

Introduction

Current nosology classifies the observed manifestations of psychosis into two main categories of non-affective (e.g. schizophrenia, schizoaffective disorder) and affective psychosis (e.g. bipolar and major depressive disorders with psychotic features) (World Health Organization, 1992; American Psychiatric Association, 2013). However, the scientific accessibility of discrete ‘natural disease entities’ in psychiatry has been questioned since Kraepelin's original distinction between dementia praecox and manic-depressive psychosis (Kraepelin, 1899; Murray et al., 2004; Craddock and Owen, 2005; Hoff, 2017). On this basis, it has been proposed, and is now widely accepted, that the categorical classification system alone is too reductionist to explain the complexity of psychotic phenomena (Van Os et al., 1999; Linscott and van Os, 2010). Various evidence-based perspectives might support a scheme incorporating symptom dimensions in psychotic disorders, as a possible approach to address the following limitations of categorical distinctions.

First, the dichotomous model of non-affective and affective psychosis does not fit the cases presenting with both prominent mood and psychotic symptoms. This is testified by the notion of a third category of schizoaffective disorder (Kasanin, 1933), which nevertheless implies further nosological challenges (Abrams et al., 2008).

In addition, if criteria-based classification systems could identify genuine disorders within the psychosis spectrum, the diagnostic overlap would be relevant to only a few patients. On the contrary, there is a large comorbidity index between schizophrenia, schizoaffective, bipolar, and major depressive disorders (Laursen et al., 2009; Upthegrove et al., 2017). Similarly, the 10-year outcomes of the Aetiology and Ethnicity in Schizophrenia and Other Psychoses (ÆSOP-10) study showed that diagnoses within psychosis other than schizophrenia at baseline tend to be unstable over time (Heslin et al., 2015).

Also, the dichotomous model is neither consistent with family studies showing familial co-aggregation of non-affective and affective psychosis (Cardno et al., 2002; Lichtenstein et al., 2009; Chou et al., 2017) nor with the accumulated evidence from genome-wide association studies that genetic risk is in part shared among schizophrenia, bipolar disorder, and major depressive disorder (International Schizophrenia Consortium et al., 2009; Demjaha et al., 2011; Cardno and Owen, 2014; O'Donovan and Owen, 2016; Power et al., 2017).

Last, several studies show the efficacy of agents which impact on dopamine signalling in the treatment of both non-affective and affective symptoms. For example, antipsychotics antagonise D2-receptor functioning and are used in bipolar disorder and schizophrenia (Post, 1999; Taylor et al., 2015), and clozapine is prescribed for both treatment-resistant bipolar disorder and schizophrenia (Li et al., 2015; Goodwin et al., 2016; Howes et al., 2016). These findings suggest that dopamine dysregulation may contribute to both positive and manic symptoms, as supported by recent positron emission tomographic findings (Jauhar et al., 2017).

Taken together, the above evidence challenges the binary categorization of non-affective and affective psychosis, enhancing research into non-categorical approaches. Pioneering studies using factor analysis examined associations among non-affective symptoms in schizophrenia and showed that these symptoms segregated in three groups (Liddle, 1987); however, these groups could not accommodate the whole symptom diversity in schizophrenia (Kay and Sevy, 1990). Thus, psychopathology models including also depressive and manic factors were proposed and replicated in schizophrenia (Lindenmayer et al., 1994; Salokangas, 1997; Wickham et al., 2001; Wallwork et al., 2012). This type of structure was likewise confirmed in psychotic disorders (Salokangas, 2003; Dikeos et al., 2006; Demjaha et al., 2009), and in a sample of bipolar patients (Lindenmayer et al., 2008). Hence, its validity across the spectrum of non-affective and affective psychosis has been consistently supported.

Recent findings suggest a more fundamental general, transdiagnostic dimension encompassing non-affective and affective symptoms, in addition to five specific symptom dimensions (Reininghaus et al., 2013; Reininghaus et al., 2016; Shevlin et al., 2017). This conceptualization statistically reflects a bifactor model, with one general factor representing shared variance among all symptoms, and a set of specific factors where the remainder of the variance is shared among subsets of symptoms (Reise et al., 2007). This is the first study set to investigate, in an incidence sample of First Episode Psychosis (FEP) patients: (1) whether the general psychosis dimension holds across diagnostic categories of non-affective psychosis (i.e. schizophrenia, schizoaffective disorder) and affective psychosis (i.e. bipolar and major depressive disorder with psychotic features); (2) whether formation of specific symptom dimensions is justified in addition to a general psychosis dimension; and (3) the association of demographic characteristics (i.e. age, gender, ethnicity), social context (i.e. urbanicity), and clinical factors (i.e. diagnosis) with general and specific psychosis dimensions.

The hypotheses underlying the third aim, based on the existing literature, were:

  1. (a) Positive symptoms would be more common in ethnic minority groups and in people living in more densely populated environments (van Os et al., 2001, Janssen et al., 2003).

  2. (b) Negative symptoms would be associated with indices suggestive of neurodevelopment impairment in psychosis (Limosin, 2014; Patel et al., 2015), such as being a man or having an early age at onset.

Methods

Sample design and procedures

Individuals suffering from their FEP were recruited between 2010 and 2015 as part of the large EUropean network of national schizophrenia networks studying Gene–Environment Interactions (EU-GEI) study (http://www.eu-gei.eu). Specifically, FEP individuals were recruited as part of the ‘Functional Enviromics’ work package, which consisted of an incidence and a case-sibling-control study conducted across six countries with the aim to investigate clinical, genetic, and environmental interaction in the development of psychotic disorders.

The study had 17 catchment areas, including urban and less urban populations: Southeast London, Cambridgeshire and Peterborough (England); central Amsterdam, Gouda and Voorhout (the Netherlands); part of the Veneto region, Bologna municipality, city of Palermo (Italy); 20th arrondissement of Paris, Val-de-Marne, Puy-de-Dôme (France); Madrid (Vallecas), Barcelona, Valencia, Oviedo, Santiago, Cuenca (Spain); and Ribeirão Preto (Brazil).

Participants

We screened all subjects who were referred to mental healthcare services with a suspicion of psychosis. The ascertainment period of cases ranged from 12 months in London to 48 months in Val-de-Marne and Bologna, with a median of 25 months. In each site, a psychiatrist experienced in epidemiology research oversaw the local team, which was centrally trained to minimize non-differential recruitment bias in the different healthcare systems. Written consent was obtained from the subjects who agreed to take part of the case-sibling-control study. For incidence-only cases, local research ethics committees approved the extraction of demographics and clinical information from patient records. More detailed information is available on the EU-GEI core paper on the incidence rates of schizophrenia and other psychotic disorders (Jongsma et al., 2018).

Patients were included in the current study if they met the following criteria during the recruitment period: (a) aged between 18 and 64 years; (b) presentation with a clinical diagnosis for an untreated FEP, even if longstanding [International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10) codes F20-F33]; (c) resident within the catchment area at FEP. Exclusion criteria were: (a) previous contact with psychiatric services for psychosis; (b) psychotic symptoms with any evidence of organic causation; and (c) transient psychotic symptoms resulting from acute intoxication (ICD-10: F1x.5).

Measures

Data on age, gender, and ethnicity was collected using a modified version of the Medical Research Council Sociodemographic Schedule (Mallett, 1997). Ethnicity was defined as self-reported. Country of heritage or birth was used as a proxy for ethnicity in people of a North African background. The OPerational CRITeria (OPCRIT) system (McGuffin et al., 1991; Williams et al., 1996) was used by centrally trained investigators, whose reliability was assessed throughout the study (κ = 0.7). The OPCRIT system allows to: (1) assess the pre-morbid history and current mental state; and (2) establish the diagnosis of psychotic disorders based on algorithms for several diagnostic classification systems. It consists of a checklist which can be filled using different sources, e.g. case records or clinical interviews. Fifty-nine items relate to the mental state examination. We used diagnoses based on Research Diagnostic Criteria (RDC) (Spitzer et al., 1978), since this classification system provides a better representation of schizoaffective disorder, which is a common presentation in clinical practice. OPCRIT RDC-based diagnoses have a good-to-excellent agreement with best-estimate consensus diagnostic procedures (Craddock et al., 1996). In each catchment area, population density was computed as a number of inhabitants per square kilometre, based on official population estimates.

Statistical analysis

Psychopathology items were dichotomized as 0 ‘absent’ or 1 ‘present’. In order to ensure sufficient covariance coverage for item response modelling, we used the items with a valid frequency of ‘present’ ⩾10% in our sample, which included individuals with ⩽20 missing values in the psychopathology rating. OPCRIT data used in the analysis contained missing values, which we assumed to be missing at random, allowing for the maximum likelihood estimator to provide unbiased estimates. We performed multidimensional item response modelling in Mplus, version 7.4 (Muthén and Muthén, 2012) to estimate unidimensional, multidimensional, bifactor, and second-order models of psychosis.

Extending previous analyses of OPCRIT data in individuals with enduring psychosis (Reininghaus et al., 2016), we estimated five alternative item-response models (online Supplementary Fig. S1): (a) a unidimensional model with one unique general factor (model A), which is consistent with the pre-Kraepelinian unitary concept of psychosis (Berrios and Beer, 1994); (b) a multidimensional model with five uncorrelated specific factors of positive, negative, disorganization, manic, and depressive symptoms (model B); (c) a multidimensional model with five correlated specific factors (model C), which, together with model B, is consistent with the pentagonal psychosis model (van Os and Kapur, 2009); (d) a bifactor model with one general latent factor along with five uncorrelated specific factors (model D) (Reininghaus et al., 2016); and (e) a hierarchical model with five first-order specific factors and one general second-order factor (model E), which, as model D, is consistent with the notion of a transdiagnotic spectrum of non-affective and affective psychosis (Craddock and Owen, 2005; Reininghaus et al., 2016). Some previous OPCRIT exploratory analysis showed a combined negative/disorganization dimension (Serretti et al., 2001; Fanous et al., 2005). We did not have a strong theoretical rationale for testing such a structure in a confirmatory analysis. By contrast, we considered specific negative symptoms as a clinically observable marker of neurodevelopmental impairment in psychosis (Limosin, 2014).

The five models were compared using Log-Likelihood (LL), Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and Sample-size Adjusted BIC (SABIC) as model fit statistics. For the model showing the best fit, we calculated reliability and strength indices, such as McDonald's omega (ω), omega hierarchical (ω H), and index H. Coefficient ω is an estimate of the proportion of common variance accounted by general and specific symptom dimensions. Coefficient ω H is an estimate of the proportion of reliable variance accounted by the general dimension, treating variability in scores due to specific dimensions as measurement error (Rodriguez et al., 2016b). Ωh formula can be extended to each specific factor, i.e. treating variability in scores due to the general factor as a measurement error, to compute omega hierarchical for subscales. Based on omega and omega hierarchical coefficients, which can vary from 0 to 1, we computed the ratios of ω/ω H, namely the relative omega, as the amount of reliable variance explained in the observed scores attributable to (1) the general factor independently from the specific symptom dimensions, and (2) each specific symptom dimension independently from the general factor. To estimate the extent to which symptom dimensions were represented by their own set of OPCRIT items and their replicability across studies, we computed the construct reliability index H (Hancock and Mueller, 2001). The index H ranges from 0 to 1, with values closer to 1 indicating better reliability and replicability (Rodriguez et al., 2016a). Quantitative scores for all symptom dimensions were calculated using the ‘FSCORES’ function in Mplus.

Further, we examined the diagnostic classification accuracy based on general and specific symptom dimension scores using multinomial receiver operating characteristic (ROC) analysis in STATA 14 (StataCorp, 2015). In addition, we performed a sensitivity analysis, examining subjects with item ratings based on face-to-face interview and based on clinical records separately.

We used multiple linear regression to examine the association between factor scores of general and/or specific psychosis dimensions as the outcome variable and demographic variables, including gender, age-at-first-contact with psychiatric services, ethnicity, and diagnosis as covariates. Country and assessment method were treated as a priori confounders.

To examine the individual-level effect of urbanicity on symptom dimension scores, standardized population density values were used as a continuous independent variable, while controlling the analysis for gender, age-at-first-contact, ethnicity, diagnosis, and assessment method. Sensitivity analysis included post-hoc multiple regressions within each country, where population density was dichotomized at its median as a dummy variable for urbanicity.

Results

Sample characteristics

We identified 2774 treated incidence cases of psychosis (Jongsma et al., 2018), of whom 2182 had (complete or missing at random) OPCRIT data available for analysis under the provision of local research ethics committees (Table 1). OPCRIT item ratings were completed based on face-to-face assessment for 51% (n = 1112) and based on clinical records for 49% (n = 1070) of the sample. The sample prevalence of psychotic symptoms is presented in Supplementary Table S1.

Table 1. Demographic and clinical characteristics of the sample included in the factor analysis

a Psychopathology assessment methods included face-to-face interview or review of clinical notes.

b Study countries were England, the Netherlands, France, Spain, Italy, and Brazil.

c 29 missing values excluded from tabulation and age analysis.

d Other and missing self-reported groups excluded from ethnicity analysis.

e Schizophrenia spectrum encompassed Broad Schizophrenia (N = 194) and Narrow Schizophrenia (N = 648); Schizoaffective disorder encompassed Schizoaffective/manic (N = 112); Schizoaffective/depressive (N = 566); Schizoaffective/bipolar (N = 86).

Fifty-seven per cent of FEP were men. Subjects were mostly people of a White ethnicity. Other main ethnic groups included Black African and Black Caribbean, North African, Mixed, and Asian. Mean age-at-first-contact with psychiatric services was 32.1 years; this was lower in men (M = 30.1) compared with women (M = 34.7; t = −9.6, p < 0.001). Age-at-first-contact differed across ethnic groups, with individuals of Black ethnicity (M = 29) being younger than individuals of White ethnicity (M = 32.7; F = 7.72, p < 0.001). The most common RDC-based diagnosis was broad or narrow schizophrenia (38.6%), followed by schizoaffective disorders (35%), unspecified non-organic psychotic disorder (16.3%), bipolar disorder (5.9%), and psychotic depression (4.2%).

Symptom dimensions in the EU-GEI sample

The bifactor model was the best fit for the OPCRIT symptom data compared with all other models, as consistently indicated by each of the model fit statistics (Table 2), and explained 54% of the total variance.

Table 2. Model fit statistics of unidimensional, multidimensional, bi-factor, and second-order models

LL, log-likelihood; AIC, Akaike Information Criterion; BIC, Bayesian Information Criterion; SABIC Sample-size Adjusted Bayesian Information Criterion.

a A difference of 10 in AIC, BIC and SABIC is considered important. Lower values indicate a statistically better model fit.

Figure 1 shows that, within the bifactor model, general and specific dimensions accounted for 93% of the common variance. Overall, statistical indices derived from the bifactor model suggest that its explained variance was due to individual differences in both general and specific symptom dimensions, which therefore might complement each other in reflecting the psychopathological structure at FEP. This is illustrated by the relative omega coefficients, which, for example, showed that 47% of the reliable variance was due to the general factor when partitioning out the variability in scores due to the specific factors (Fig. 1). High H values were consistently observed for all latent factors, indicating that they were well defined, and that the bifactor model had high reliability and replicability (Fig. 1). Sensitivity analysis showed that the bifactor model was the best fit for the OPCRIT data in both the assessment methods (online Supplementary Tables S2.1 and S2.2).

Fig. 1. Bifactor model. (□) Observed variables (No. of OPCRIT items); (○) Unobserved variables (latent factors); (→) standardized item loading estimation onto latent factors; G, general psychosis factor; specific symptom factors: DEP, depression; MAN, mania; DIS, disorganization; NEG, negative; POS, positive. Reliability and strength estimates: H = construct reliability index; ω = McDonald omega; ω H = hierarchical omega; ω/ω H = Relative omega. Explanatory note: McDonald's ω is an estimate of the proportion of the common variance accounted by general and specific symptom dimensions. Relative omega (ω/ω h) is the amount of reliable variance explained in the observed scores attributable to (1) the general factor independently from the specific symptom dimensions, and (2) each specific symptom dimension independently from the general factor. H is an index of the quality of the measurement model based on the set of OPCRIT items for each symptom dimension. Index H can range from 0 to 1, with values closer to 1 indicating a better construct reliability and replicability across studies.

Symptom dimensions and item factor loadings

Table 3 shows standardized factor loadings for the bifactor model. On the general dimension, a positive factor loading was observed for all OPCRIT items with statistically significant loadings. In addition, the magnitude of factor loadings of items on the general dimension was small, except for some manic/delusional items for which loadings of moderate magnitude were observed. On the specific dimensions, most of the items showed moderate to strong positive loadings. Finally, latent factor scores were strongly and positively associated with simplified weighted OPCRIT sum scores for use in clinical practice (online Supplementary Table S3).

Table 3. Standardized factor loadings in the bifactor model

General, general psychosis factor; specific symptom dimensions: DEP, depression; MAN, mania; DIS, disorganisation; NEG, negative; POS, positive. Only loadings ⩾0.2 for the general factor are shown for simplicity. Significance: *** = p < 0.001; ** = p < 0.01.

Symptom dimensions and categorical diagnoses

Findings from regression analyses are shown in Table 4 and predicted symptom dimension scores for each RDC-based diagnostic category are reported in Fig. 2. Compared with bipolar disorder, factor scores for the positive dimension were moderately higher in schizophrenia and schizoaffective disorder; factor scores for the negative dimension were moderately higher in schizophrenia, schizoaffective and psychotic depression; and factor scores for the depressive dimension were markedly higher in psychotic depression and schizoaffective disorder. Bipolar disorder showed the highest factor scores for the manic and the general dimensions. Dimension scores based on ICD diagnostic categories are presented in Supplementary Fig. S2 and Supplementary Table S4.

Fig. 2. Predicted symptom profiles by RDC-based diagnostic category, gender, and ethnicity. Explanatory note: After the estimation of the bifactor model, the continuous scores for general and specific symptom dimensions were computed using the function ‘FSCORES’ in Mplus (setting mean = 0 and standard deviation = 1), and used as the outcome variable in the regression analyses.

Table 4. Symptom dimension scores by sociodemographic, categorical diagnosis, and social context variables

B, unstandardised regression coefficient; CI, confidence interval.

a Covariates in multiple models were gender, age, ethnicity, diagnosis, study country, and type of assessment method (interview v. case records).

b Population density analysis was adjusted for gender, age, ethnicity, diagnosis, and type of assessment method (interview v. case records).

Finally, ROC analysis showed that classification accuracy into RDC categories based on general and specific symptom dimension scores was markedly higher for patients with psychopathology rating based either on face-to-face interview (95% CI 0.54–0.63) or case note review (95% CI 0.56–0.65), compared with a classification by chance (95% 0.32–0.41). Moreover, symptom dimensions showed similar diagnostic classification accuracy across countries (online Supplementary Figs S3.1 and S3.2).

Symptom dimensions by gender, age-at-first-contact, and ethnicity

Findings on factor scores by gender, age-at-first-contact, and ethnicity, are shown in Fig. 2 and Table 4. Early age-at-first-contact was associated with higher scores for the general, negative, disorganized, and manic symptom dimensions, and with lower scores for the depressive symptom dimension. Men showed fewer depressive symptoms and more negative symptoms than women, even after adjusting the analysis for several confounders. Table 4 further shows that participants of Black and North African ethnicity presented with higher scores on the positive symptom dimension compared with an individual of White ethnicity. Finally, higher scores for the disorganization dimension and lower scores for the depressive dimension were observed in Black compared with White ethnicity. Noteworthy, the magnitude of the effect was small for all the results.

Symptom dimensions by urbanicity

A moderate positive association was observed for more densely populated environments and the general dimension score. Table 4 further shows a weaker positive association between population density and specific negative, disorganization, and manic symptom dimensions. Post-hoc analysis of symptom dimensions within countries showed that positive symptoms were more common in urban study sites in the UK (i.e. London v. Cambridge), whereas a negative association was observed in Spain (online Supplementary Table S5).

Discussion

Principal findings

This is the first study on general and specific symptom dimensions in an incidence sample of psychosis. First, we found in our FEP sample that manic and delusional symptoms primarily underlie the identified general psychosis factor across diagnostic categories of non-affective and affective psychosis. Second, findings showed that specific dimensions of positive, negative, disorganized, manic and depressive symptoms are complementary to the general dimension. Third, general and specific symptom dimensions discriminated well between diagnoses of psychotic disorders. Forth, positive symptoms were more common among individuals of Black and North African ethnicity. Fifth, there was some evidence that early age-at-first-contact was associated with higher scores for several dimensions, such as of negative, disorganised and manic symptoms. Sixth, men presented with more negative and less depressive symptoms than women. Finally, higher scores for the general dimension were observed for individuals living in urban neighbourhoods.

Limitations

Before interpreting our findings, we must consider potential limitations. Symptoms were rated with a semi-structured face-to-face interview or from case note review. Still, study investigators underwent a specific and centrally organized training for OPCRIT and demonstrated good inter-rater reliability for individual item ratings; moreover, OPCRIT is a tool specifically designed to allow use with different sources (McGuffin et al., 1991; Cardno et al., 1996; Rucker et al., 2011). However, we found consistently lower symptom ratings using case note review compared with face-to-face interviews. It is possible that clinicians failed to record all symptoms; alternatively, patients presenting with less severe psychopathology had a shorter contact with services, and therefore less chances to be interviewed by researchers. Whether or not differences in ratings are genuine or a surrogate of different sources of item ratings, we treated this potential bias as artificial confounding of our findings and adjusted all analyses for the type of assessment method. On the other hand, the use of an incidence sample allowed the best possible approximation of the true distribution of psychosis symptoms at FEP, which may have reduced potentially inflated presence of positive and negative symptoms in previous studies conducted in hospital settings (Allardyce et al., 2007). Also, OPCRIT does not cover some relevant aspects of negative symptoms related to passive social withdrawal, lack of motivation, and difficulties in abstract/symbolic thinking. Consequently, we constructed a narrow negative symptom dimension with three items. Finally, some authors have argued that, in a bifactor model, the general factor may be difficult to interpret and in general may overfit the data (Bonifay et al., 2016). However, the bifactor model allows solutions to dimensionality issues that arise when the conceptual breadth of a construct cannot be fully determined (Reise et al., 2007), as is likely to be the case for the construct of psychosis, which, in the past, has been considered as unidimensional and multidimensional at the same time. For example, the bifactor model discerns each specific symptom dimension from the common item effect, which is captured by the general dimension, thus allowing an accurate evaluation of the unique contribution of each subset of symptoms. Last, this solution provides crucial information which cannot be determined from the other models, i.e. how much of the phenotypic variance that we aim to measure is due to a unidimensional construct v. a multidimensional construct of psychosis. Hence, it was a suitable model for addressing dimensionality issues for psychosis and generating reliable phenotypes.

Comparison with previous research

In our study, the bifactor model of psychopathology best explained the observed symptoms at FEP compared with unidimensional and multidimensional models. Our findings are consistent with, and extend, previous research on psychotic symptoms in people with enduring psychotic disorders (Reininghaus et al., 2013; Reininghaus et al., 2016) and the general population (Shevlin et al., 2017) to a multinational incidence sample of FEP. They provide further evidence that non-affective and affective psychotic disorders lie on a common mood-psychosis spectrum (Murray et al., 2004). In addition, we provided the first evidence in psychosis that a bifactor solution shows better model fit statistics compared with a second-order hierarchical solution. However, compared with findings in enduring psychosis (Reininghaus et al., 2016), we found a less specific general psychopathology factor with more general disturbances and affective features. As illnesses develop, the non-affective psychotic phenomena may become more and affective features less prominent.

We found some evidence of gender differences in symptom dimension scores. Men showed less depressive symptoms and more negative symptoms compared with women. This finding is consistent with other studies in stable schizophrenia (Shtasel et al., 1992; Roy et al., 2001; Galderisi et al., 2012), first episode psychotic disorder (Morgan et al., 2008), and the general population (Maric et al., 2003). In our sample, we also showed that early age-at-first-contact was associated with a higher level of general and specific psychopathology. Notably, it has been proposed that gender-related and symptom profiles differences in psychosis may be suggestive of different neurodevelopmental trajectories (Castle and Murray, 1991; Seeman, 1997; Riecher-Rössler and Häfner, 2000).

We further found that symptom dimensions vary in terms of ethnicity. Consistent with a previous report (Kirkbride et al., 2016), we provided evidence that people of Black ethnicity presented at FEP with more positive and disorganized symptoms and fewer depressive symptoms compared with people of White ethnicity. Moreover, in line with another study (Veling et al., 2007), we found in our sample that the North African group presented at FEP with more positive symptoms compared with people of White ethnicity. It has been debated whether similar findings reflect true differences in symptom presentation or instead result from raters being more likely to overrate symptoms in the context of ethno-cultural diversity (Mukherjee et al., 1983; Hutchinson et al., 1999; Barrio et al., 2003; Arnold et al., 2004; Vega and Lewis-Fernandez, 2008). Recent studies using standardized procedures for assessing symptomatology blind to ethnicity have suggested that misdiagnosis or rating bias cannot account for differences across ethnic groups (Morgan et al., 2010). However, we must remain cautious in interpreting these results.

We showed that high population density is positively associated with the general and specific disorganized, negative and manic dimensions. In our multinational sample, we were not able to replicate previous findings on the relationship between urbanicity and the positive dimension (Kirkbride et al., 2007). Nevertheless, stratified analysis by country was consistent with the previously reported association between urbanicity and positive symptoms in the UK. The relationship between urbanicity and a higher incidence of psychotic disorders is well-established (Vassos et al., 2012). However, it has been found to show non-linearity (Kirkbride et al., 2017), which implies that the effect of urbanicity may depend on exposure to additional socio-environmental factors associated with urban contexts, for example cannabis use (Kuepper et al., 2011) and childhood adversities (Frissen et al., 2015). Similarly, our findings support the hypothesis that urban environment does not have a dimension-specific effect and may act to confer risk for different psychopathological outcomes in psychosis (van Os et al., 2002). Noteworthy, similar findings have been reported in the general population (van Os et al., 2001), which may require future studies to consider the additive interaction between putative risk factors for psychosis and urbanicity.

Implications

In the context of a general effort to move away from DSM and ICD categories (Demjaha et al., 2009; Reininghaus et al., 2016; Kotov et al., 2017; Van Dam et al., 2017; Whalen, 2017; Zachar and Kendler, 2017), we found evidence that supports, and may inform, the use of dimensional measures in the field of psychosis. In our sample, the bifactor model was a valid platform for research into FEP. Nevertheless, the plausibility of our statistically-guided approach depends on the extent to which: (1) symptom dimensions represent coherent environmental and biological factors; and (2) meaningful clinical information or decisions may derive from the latent constructs.

From a research perspective, our findings suggest that the general dimension may reflect a phenotype for the study of general risk factors. For example, urbanicity may impact on the risk and profile of psychosis through the combination of other, more specific socio- or bio-environmental factors. In addition, we showed a substantial variation of sociodemographic determinants at the specific dimension level, which may support an integrated socio-developmental model of psychosis (Morgan et al., 2010).

We may further suggest using the general dimension as a quantitative measure of psychopathology for research into the genetic component shared across psychotic disorders. The evidence is required to establish the extent to which pathophysiology of schizophrenia, bipolar disorder, and psychotic depression is shared at the level of pathways and neuronal cell mechanisms (Forstner et al., 2017). Based on the data presented on specific symptom dimensions, it is intriguing to speculate whether the distribution of psychotic symptoms reflects a gradient of neurodevelopmental impairment or socio-environmental risk (Morgan et al., 2010; Howes and Murray, 2014) resulting in different patterns of functional abnormalities (Murray and Lewis, 1987; Murray et al., 1992; Demjaha et al., 2011; Owen and O'Donovan, 2017).

From a clinical perspective, although each patient presents with a specific pattern of psychopathology and response to treatment at FEP, attention has been traditionally focused on the positive dimension management. Mental health professionals may integrate observations of the whole range of symptoms and signs with a consideration of neurodevelopmental and socio-environmental risk factors. Such an approach should aim to plan and optimize pharmacological and non-pharmacological treatments (Murray et al., 2016), thus focusing further on treatment of negative, disorganized and affective dimensions (Wykes et al., 2011; Giacco et al., 2012; Carbon and Correll, 2014; Pelayo-Teran et al., 2014; Rosenbaum et al., 2014).

We may further suggest promoting mental health professionals to adopt treatment plans guided by dimensions, and increasing their confidence in dimensional classifications. Reconciling contradictory concerns of clinicians and researchers (Kendell and Jablensky, 2003) may represent the first milestone towards a gradual nosology refinement.

Supplementary material

The supplementary material for this article can be found at https://doi.org/10.1017/S0033291718002131

Acknowledgements

The EU-GEI Project was funded by the European Community's Seventh Framework Programme under grant agreement No. HEALTH-F2-2010-241909 (Project EU-GEI). The work was further funded by: Clinician Scientist Medical Research Council fellowship (project reference MR/M008436/1) to MDF; Veni grant from the Netherlands Organisation for Scientific Research (grant no. 451-13-022) to UR; Sir Henry Dale Fellowship, jointly funded by the Wellcome Trust and the Royal Society (grant no. 101272/Z/13/Z) to JBK; National Institute for Health Research (NIHR) Biomedical Research Centre for Mental Health at South London and Maudsley NHS Foundation Trust and King's College London. The Brazilian study was funded by the Säo Paulo Research Foundation under grant number 2012/0417-0. Funders were not involved in design and conduct of the study; collection, management, analysis and interpretation of the data; preparation, review or approval of the manuscript, and decision to submit the manuscript for publication.

Conflict of interest

The authors have no conflicts of interest to declare in relation to the work presented in this paper.

References

Abrams, DJ, Rojas, DC and Arciniegas, DB (2008) Is schizoaffective disorder a distinct categorical diagnosis? A critical review of the literature. Neuropsychiatric Disease and Treatment 4, 10891109.
Allardyce, J, Suppes, T and Van Os, J (2007) Dimensions and the psychosis phenotype. International Journal of Methods in Psychiatric Research 16(Suppl. 1), S34S40.
American Psychiatric Association (2013) Diagnostic and Statistical Manual of Mental Disorders (DSM-5®). Arlington, VA: American Psychiatric Publishing.
Arnold, LM, Keck, PE Jr., Collins, J, Wilson, R, Fleck, DE, Corey, KB, Amicone, J, Adebimpe, VR and Strakowski, SM (2004) Ethnicity and first-rank symptoms in patients with psychosis. Schizophrenia Research 67, 207212.
Barrio, C, Yamada, AM, Atuel, H, Hough, RL, Yee, S, Berthot, B and Russo, PA (2003) A tri-ethnic examination of symptom expression on the positive and negative syndrome scale in schizophrenia spectrum disorders. Schizophrenia Research 60, 259269.
Berrios, GE and Beer, D (1994) The notion of a unitary psychosis: a conceptual history. History of Psychiatry 5, 1336.
Bonifay, W, Lane, SP and Reise, SP (2016) Three concerns with applying a bifactor model as a structure of psychopathology. Clinical Psychological Science 5, 184186.
Carbon, M and Correll, CU (2014) Thinking and acting beyond the positive: the role of the cognitive and negative symptoms in schizophrenia. CNS Spectrums 19(Suppl. 1), 3553.
Cardno, AG and Owen, MJ (2014) Genetic relationships between schizophrenia, bipolar disorder, and schizoaffective disorder. Schizophrenia Bulletin 40, 504515.
Cardno, AG, Jones, LA, Murphy, KC, Asherson, P, Scott, LC, Williams, J, Owen, MJ and McGuffin, P (1996) Factor analysis of schizophrenic symptoms using the OPCRIT checklist. Schizophrenia Research 22, 233239.
Cardno, AG, Rijsdijk, FV, Sham, PC, Murray, RM and McGuffin, P (2002) A twin study of genetic relationships between psychotic symptoms. The American Journal of Psychiatry 159, 539545.
Castle, DJ and Murray, RM (1991) The neurodevelopmental basis of sex differences in schizophrenia. Psychological Medicine 21, 565575.
Chou, IJ, Kuo, CF, Huang, YS, Grainge, MJ, Valdes, AM, See, LC, Yu, KH, Luo, SF, Huang, LS, Tseng, WY, Zhang, W and Doherty, M (2017) Familial aggregation and heritability of schizophrenia and co-aggregation of psychiatric illnesses in affected families. Schizophrenia Bulletin 43, 10701078.
Craddock, N and Owen, MJ (2005) The beginning of the end for the Kraepelinian dichotomy. The British Journal of Psychiatry: The Journal of Mental Science 186, 364366.
Craddock, M, Asherson, P, Owen, MJ, Williams, J, McGuffin, P and Farmer, AE (1996) Concurrent validity of the OPCRIT diagnostic system. Comparison of OPCRIT diagnoses with consensus best-estimate lifetime diagnoses. The British Journal of Psychiatry: The Journal of Mental Science 169, 5863.
Demjaha, A, Morgan, K, Morgan, C, Landau, S, Dean, K, Reichenberg, A, Sham, P, Fearon, P, Hutchinson, G, Jones, PB, Murray, RM and Dazzan, P (2009) Combining dimensional and categorical representation of psychosis: the way forward for DSM-V and ICD-11? Psychological Medicine 39, 19431955.
Demjaha, A, MacCabe, JH and Murray, RM (2011) How genes and environmental factors determine the different neurodevelopmental trajectories of schizophrenia and bipolar disorder. Schizophrenia Bulletin 38, 209214.
Dikeos, DG, Wickham, H, McDonald, C, Walshe, M, Sigmundsson, T, Bramon, E, Grech, A, Toulopoulou, T, Murray, R and Sham, PC (2006) Distribution of symptom dimensions across Kraepelinian divisions. The British Journal of Psychiatry: The Journal of Mental Science 189, 346353.
Fanous, AH, van den Oord, EJ, Riley, BP, Aggen, SH, Neale, MC, O'Neill, FA, Walsh, D and Kendler, KS (2005) Relationship between a high-risk haplotype in the DTNBP1 (dysbindin) gene and clinical features of schizophrenia. The American Journal of Psychiatry 162, 18241832.
Forstner, AJ, Hecker, J, Hofmann, A, Maaser, A, Reinbold, CS, Muhleisen, TW, Leber, M, Strohmaier, J, Degenhardt, F, Treutlein, J, Mattheisen, M, Schumacher, J, Streit, F, Meier, S, Herms, S, Hoffmann, P, Lacour, A, Witt, SH, Reif, A, Muller-Myhsok, B, Lucae, S, Maier, W, Schwarz, M, Vedder, H, Kammerer-Ciernioch, J, Pfennig, A, Bauer, M, Hautzinger, M, Moebus, S, Schenk, LM, Fischer, SB, Sivalingam, S, Czerski, PM, Hauser, J, Lissowska, J, Szeszenia-Dabrowska, N, Brennan, P, McKay, JD, Wright, A, Mitchell, PB, Fullerton, JM, Schofield, PR, Montgomery, GW, Medland, SE, Gordon, SD, Martin, NG, Krasnov, V, Chuchalin, A, Babadjanova, G, Pantelejeva, G, Abramova, LI, Tiganov, AS, Polonikov, A, Khusnutdinova, E, Alda, M, Cruceanu, C, Rouleau, GA, Turecki, G, Laprise, C, Rivas, F, Mayoral, F, Kogevinas, M, Grigoroiu-Serbanescu, M, Becker, T, Schulze, TG, Rietschel, M, Cichon, S, Fier, H and Nothen, MM (2017) Identification of shared risk loci and pathways for bipolar disorder and schizophrenia. PLoS ONE 12, e0171595.
Frissen, A, Lieverse, R, Drukker, M, van Winkel, R, Delespaul, P and Investigators, G (2015) Childhood trauma and childhood urbanicity in relation to psychotic disorder. Social Psychiatry and Psychiatric Epidemiology 50, 14811488.
Galderisi, S, Bucci, P, Ucok, A and Peuskens, J (2012) No gender differences in social outcome in patients suffering from schizophrenia. European Psychiatry: The Journal of the Association of European Psychiatrists 27, 406408.
Giacco, D, McCabe, R, Kallert, T, Hansson, L, Fiorillo, A and Priebe, S (2012) Friends and symptom dimensions in patients with psychosis: a pooled analysis. PLoS ONE 7, e50119.
Goodwin, GM, Haddad, PM, Ferrier, IN, Aronson, JK, Barnes, T, Cipriani, A, Coghill, DR, Fazel, S, Geddes, JR, Grunze, H, Holmes, EA, Howes, O, Hudson, S, Hunt, N, Jones, I, Macmillan, IC, McAllister-Williams, H, Miklowitz, DR, Morriss, R, Munafo, M, Paton, C, Saharkian, BJ, Saunders, K, Sinclair, J, Taylor, D, Vieta, E and Young, AH (2016) Evidence-based guidelines for treating bipolar disorder: revised third edition recommendations from the British Association for psychopharmacology. Journal of Psychopharmacology (Oxford, England) 30, 495553.
Hancock, GR and Mueller, RO (2001) Rethinking construct reliability within latent variable systems. In Cudek, R, Du Toit, S and Sorbom, D (eds), Structural Equation Modeling: Present and Future: A Festschrift in Honor of Karl Jöreskog. Linconlnwood, IL: Scientific Software International, Inc., pp. 195216.
Heslin, M, Lomas, B, Lappin, JM, Donoghue, K, Reininghaus, U, Onyejiaka, A, Croudace, T, Jones, PB, Murray, RM, Fearon, P, Dazzan, P, Morgan, C and Doody, GA (2015) Diagnostic change 10 years after a first episode of psychosis. Psychological Medicine 45, 27572769.
Hoff, P (2017) On reification of mental illness: historical and conceptual issues from Emil Kraepelin and Eugen Bleuler to DSM-5. In Kendler, KS and Parnas, J (eds), Philosophical Issues in Psychiatry IV: Psychiatric Nosology. New York, NY: Oxford University Press, pp. 107117.
Howes, OD and Murray, RM (2014) Schizophrenia: an integrated sociodevelopmental-cognitive model. The Lancet 383, 16771687.
Howes, OD, McCutcheon, R, Agid, O, de Bartolomeis, A, van Beveren, NJ, Birnbaum, ML, Bloomfield, MA, Bressan, RA, Buchanan, RW and Carpenter, WT (2016) Treatment-resistant schizophrenia: treatment response and resistance in psychosis (TRRIP) working group consensus guidelines on diagnosis and terminology. American Journal of Psychiatry 174, 216229.
Hutchinson, G, Takei, N, Sham, P, Harvey, I and Murray, RM (1999) Factor analysis of symptoms in schizophrenia: differences between White and Caribbean patients in Camberwell. Psychological Medicine 29, 607612.
International Schizophrenia Consortium, Purcell, SM, Wray, NR, Stone, JL, Visscher, PM, O'Donovan, MC, Sullivan, PF and Sklar, P (2009) Common polygenic variation contributes to risk of schizophrenia and bipolar disorder. Nature 460, 748752.
Janssen, I, Hanssen, M, Bak, M, Bijl, RV, de Graaf, R, Vollebergh, W, McKenzie, K and van Os, J (2003) Discrimination and delusional ideation. The British Journal of Psychiatry: The Journal of Mental Science 182, 7176.
Jauhar, S, Nour, MM, Veronese, M, Rogdaki, M, Bonoldi, I, Azis, M, Turkheimer, F, McGuire, P, Young, AH and Howes, OD (2017) A test of the transdiagnostic dopamine hypothesis of psychosis using positron emission tomographic imaging in bipolar affective disorder and schizophrenia. JAMA Psychiatry 74, 12061213.
Jongsma, HE, Gayer-Anderson, C, Lasalvia, A, Quattrone, D, Mule, A, Szoke, A, Selten, JP, Turner, C, Arango, C, Tarricone, I, Berardi, D, Tortelli, A, Llorca, PM, de Haan, L, Bobes, J, Bernardo, M, Sanjuan, J, Santos, JL, Arrojo, M, Del-Ben, CM, Menezes, PR, Velthorst, E, Murray, RM, Rutten, BP, Jones, PB, van Os, J, Morgan, C and Kirkbride, JB & European Network of National Schizophrenia Networks Studying Gene-Environment Interactions Work Package, G (2018) Treated incidence of psychotic disorders in the multinational EU-GEI study. JAMA Psychiatry 75, 3646.
Kasanin, J (1933) The acute schizoaffective psychoses. American Journal of Psychiatry 13, 97126.
Kay, SR and Sevy, S (1990) Pyramidical model of schizophrenia. Schizophrenia Bulletin 16, 537545.
Kendell, R and Jablensky, A (2003) Distinguishing between the validity and utility of psychiatric diagnoses. The American Journal of Psychiatry 160, 412.
Kirkbride, J, Boydell, J, Ploubidis, G, Morgan, C, Fearon, P, Dazzan, P, Morgan, K, Murray, R and Jones, P (2007) The relationship between schizophrenia and neighbourhood-level social capital in an urban area: findings from the AESOP study. Schizophrenia Bulletin 33, 237238.
Kirkbride, JB, Hindocha, C, Hameed, Y, Perez, J and Jones, PB (2016) Talk 3. Do symptom dimensions vary between ethnic groups at first presentation to early intervention in psychosis services? Evidence from the SEPEA study. Early Intervention in Psychiatry 10(Suppl. 1), 1616.
Kirkbride, JB, Hameed, Y, Ankireddypalli, G, Ioannidis, K, Crane, CM, Nasir, M, Kabacs, N, Metastasio, A, Jenkins, O, Espandian, A, Spyridi, S, Ralevic, D, Siddabattuni, S, Walden, B, Adeoye, A, Perez, J and Jones, PB (2017) The epidemiology of first-episode psychosis in early intervention in psychosis services: findings from the social epidemiology of psychoses in east Anglia [SEPEA] study. American Journal of Psychiatry 174, 143153.
Kotov, R, Krueger, RF, Watson, D, Achenbach, TM, Althoff, RR, Bagby, RM, Brown, TA, Carpenter, WT, Caspi, A and Clark, LA (2017) The hierarchical taxonomy of psychopathology (HiTOP): a dimensional alternative to traditional nosologies. Journal of Abnormal Psychology 126, 454.
Kraepelin, E (1899) Psychiatrie: Ein Lehrbuch für Studirende und Aerzte. Leipzig: JA Barth.
Kuepper, R, van Os, J, Lieb, R, Wittchen, HU and Henquet, C (2011) Do cannabis and urbanicity co-participate in causing psychosis? Evidence from a 10-year follow-up cohort study. Psychological Medicine 41, 21212129.
Laursen, TM, Agerbo, E and Pedersen, CB (2009) Bipolar disorder, schizoaffective disorder, and schizophrenia overlap: a new comorbidity index. The Journal of Clinical Psychiatry 70, 14321438.
Li, XB, Tang, YL, Wang, CY and de Leon, J (2015) Clozapine for treatment-resistant bipolar disorder: a systematic review. Bipolar Disorders 17, 235247.
Lichtenstein, P, Yip, BH, Björk, C, Pawitan, Y, Cannon, TD, Sullivan, PF and Hultman, CM (2009) Common genetic determinants of schizophrenia and bipolar disorder in Swedish families: a population-based study. The Lancet 373, 234239.
Liddle, PF (1987) The symptoms of chronic schizophrenia. A re-examination of the positive-negative dichotomy. The British Journal of Psychiatry: The Journal of Mental Science 151, 145151.
Limosin, F (2014) Neurodevelopmental and environmental hypotheses of negative symptoms of schizophrenia. BMC Psychiatry 14, 88.
Lindenmayer, J-P, Bernstein-Hyman, R and Grochowski, S (1994) Five-Factor model of schizophrenia initial validation. The Journal of Nervous and Mental Disease 182, 631638.
Lindenmayer, JP, Bossie, CA, Kujawa, M, Zhu, Y and Canuso, CM (2008) Dimensions of psychosis in patients with bipolar mania as measured by the positive and negative syndrome scale. Psychopathology 41, 264270.
Linscott, RJ and van Os, J (2010) Systematic reviews of categorical versus continuum models in psychosis: evidence for discontinuous subpopulations underlying a psychometric continuum. Implications for DSM-V, DSM-VI, and DSM-VII. Annual Review of Clinical Psychology 6, 391419.
Mallett, R (1997) Sociodemographic Schedule. London: Section of Social Psychiatry, Institute of Psychiatry.
Maric, N, Krabbendam, L, Vollebergh, W, de Graaf, R and van Os, J (2003) Sex differences in symptoms of psychosis in a non-selected, general population sample. Schizophrenia Research 63, 8995.
McGuffin, P, Farmer, A and Harvey, I (1991) A polydiagnostic application of operational criteria in studies of psychotic illness. Development and reliability of the OPCRIT system. Archives of General Psychiatry 48, 764770.
Morgan, VA, Castle, DJ and Jablensky, AV (2008) Do women express and experience psychosis differently from men? Epidemiological evidence from the Australian National Study of Low Prevalence (Psychotic) Disorders. Australian & New Zealand Journal of Psychiatry 42, 7482.
Morgan, C, Charalambides, M, Hutchinson, G and Murray, RM (2010) Migration, ethnicity, and psychosis: toward a sociodevelopmental model. Schizophrenia Bulletin 36, 655664.
Mukherjee, S, Shukla, S, Woodle, J, Rosen, AM and Olarte, S (1983) Misdiagnosis of schizophrenia in bipolar patients: a multiethnic comparison. American Journal of Psychiatry 140, 15711574.
Murray, RM and Lewis, SW (1987) Is schizophrenia a neurodevelopmental disorder? British Medical Journal (Clinical Research ed.) 295, 681682.
Murray, RM, O'Callaghan, E, Castle, DJ and Lewis, SW (1992) A neurodevelopmental approach to the classification of schizophrenia. Schizophrenia Bulletin 18, 319332.
Murray, RM, Sham, P, Van Os, J, Zanelli, J, Cannon, M and McDonald, C (2004) A developmental model for similarities and dissimilarities between schizophrenia and bipolar disorder. Schizophrenia Research 71, 405416.
Murray, RM, Quattrone, D, Natesan, S, van Os, J, Nordentoft, M, Howes, O, Di Forti, M and Taylor, D (2016) Should psychiatrists be more cautious about the long-term prophylactic use of antipsychotics? The British Journal of Psychiatry: The Journal of Mental Science 209, 361365.
Muthén, L and Muthén, B (2012) Mplus User's Guide, 7th Edn. Los Angeles, CA: Muthén & Muthén.
O'Donovan, MC and Owen, MJ (2016) The implications of the shared genetics of psychiatric disorders. Nature Medicine 22, 12141219.
Owen, MJ and O'Donovan, MC (2017) Schizophrenia and the neurodevelopmental continuum: evidence from genomics. World Psychiatry 16, 227235.
Patel, R, Jayatilleke, N, Broadbent, M, Chang, CK, Foskett, N, Gorrell, G, Hayes, RD, Jackson, R, Johnston, C, Shetty, H, Roberts, A, McGuire, P and Stewart, R (2015) Negative symptoms in schizophrenia: a study in a large clinical sample of patients using a novel automated method. BMJ Open 5, e007619.
Pelayo-Teran, JM, Diaz, FJ, Perez-Iglesias, R, Suarez-Pinilla, P, Tabares-Seisdedos, R, de Leon, J and Crespo-Facorro, B (2014) Trajectories of symptom dimensions in short-term response to antipsychotic treatment in patients with a first episode of non-affective psychosis. Psychological Medicine 44, 3750.
Post, RM (1999) Comparative pharmacology of bipolar disorder and schizophrenia. Schizophrenia Research 39, 153158.
Power, RA, Tansey, KE, Buttenschon, HN, Cohen-Woods, S, Bigdeli, T, Hall, LS, Kutalik, Z, Lee, SH, Ripke, S, Steinberg, S, Teumer, A, Viktorin, A, Wray, NR, Arolt, V, Baune, BT, Boomsma, DI, Borglum, AD, Byrne, EM, Castelao, E, Craddock, N, Craig, IW, Dannlowski, U, Deary, IJ, Degenhardt, F, Forstner, AJ, Gordon, SD, Grabe, HJ, Grove, J, Hamilton, SP, Hayward, C, Heath, AC, Hocking, LJ, Homuth, G, Hottenga, JJ, Kloiber, S, Krogh, J, Landen, M, Lang, M, Levinson, DF, Lichtenstein, P, Lucae, S, MacIntyre, DJ, Madden, P, Magnusson, PK, Martin, NG, McIntosh, AM, Middeldorp, CM, Milaneschi, Y, Montgomery, GW, Mors, O, Muller-Myhsok, B, Nyholt, DR, Oskarsson, H, Owen, MJ, Padmanabhan, S, Penninx, BW, Pergadia, ML, Porteous, DJ, Potash, JB, Preisig, M, Rivera, M, Shi, J, Shyn, SI, Sigurdsson, E, Smit, JH, Smith, BH, Stefansson, H, Stefansson, K, Strohmaier, J, Sullivan, PF, Thomson, P, Thorgeirsson, TE, Van der Auwera, S, Weissman, MM, Converge Consortium, CCGC, Breen, G and Lewis, CM (2017) Genome-wide association for major depression through age at onset stratification: major depressive disorder working group of the psychiatric genomics consortium. Biological Psychiatry 81, 325335.
Reininghaus, U, Priebe, S and Bentall, RP (2013) Testing the psychopathology of psychosis: evidence for a general psychosis dimension. Schizophrenia Bulletin 39, 884895.
Reininghaus, U, Bohnke, JR, Hosang, G, Farmer, A, Burns, T, McGuffin, P and Bentall, RP (2016) Evaluation of the validity and utility of a transdiagnostic psychosis dimension encompassing schizophrenia and bipolar disorder. The British Journal of Psychiatry: The Journal of Mental Science 209, 107113.
Reise, SP, Morizot, J and Hays, RD (2007) The role of the bifactor model in resolving dimensionality issues in health outcomes measures. Quality of Life Research 16, 1931.
Riecher-Rössler, A and Häfner, H (2000) Gender aspects in schizophrenia: bridging the border between social and biological psychiatry. Acta Psychiatrica Scandinavica 102, 5862.
Rodriguez, A, Reise, SP and Haviland, MG (2016 a) Applying bifactor statistical indices in the evaluation of psychological measures. Journal of Personality Assessment 98, 223237.
Rodriguez, A, Reise, SP and Haviland, MG (2016 b) Evaluating bifactor models: calculating and interpreting statistical indices. Psychological Methods 21, 137150.
Rosenbaum, S, Tiedemann, A, Sherrington, C, Curtis, J and Ward, PB (2014) Physical activity interventions for people with mental illness: a systematic review and meta-analysis. The Journal of Clinical Psychiatry 75, 964974.
Roy, M-A, Maziade, M, Labbé, A and Mérette, C (2001) Male gender is associated with deficit schizophrenia: a meta-analysis. Schizophrenia Research 47, 141147.
Rucker, J, Newman, S, Gray, J, Gunasinghe, C, Broadbent, M, Brittain, P, Baggaley, M, Denis, M, Turp, J, Stewart, R, Lovestone, S, Schumann, G, Farmer, A and McGuffin, P (2011) OPCRIT+: an electronic system for psychiatric diagnosis and data collection in clinical and research settings. The British Journal of Psychiatry: The Journal of Mental Science 199, 151155.
Salokangas, RKR (1997) Structure of schizophrenic symptomatology and its changes over time: prospective factor-analytical study. Acta Psychiatrica Scandinavica 95, 3239.
Salokangas, RK (2003) Symptom dimensions and outcome in schizophrenia. World Psychiatry 2, 172178.
Seeman, MV (1997) Psychopathology in women and men: focus on female hormones. American Journal of Psychiatry 154, 16411647.
Serretti, A, Rietschel, M, Lattuada, E, Krauss, H, Schulze, TG, Muller, DJ, Maier, W and Smeraldi, E (2001) Major psychoses symptomatology: factor analysis of 2241 psychotic subjects. European Archives of Psychiatry and Clinical Neuroscience 251, 193198.
Shevlin, M, McElroy, E, Bentall, RP, Reininghaus, U and Murphy, J (2017) The psychosis Continuum: testing a bifactor model of psychosis in a general population sample. Schizophrenia Bulletin 43, 133141.
Shtasel, DL, Gur, RE, Gallacher, F, Heimberg, C and Gur, RC (1992) Gender differences in the clinical expression of schizophrenia. Schizophrenia Research 7, 225231.
Spitzer, RL, Endicott, J and Robins, E (1978) Research diagnostic criteria: rationale and reliability. Archives of General Psychiatry 35, 773782.
StataCorp, L (2015) Stata Statistical Software: Release 14.[computer program]. StataCorp LP.
Taylor, D, Paton, C and Kapur, S (2015) The Maudsley Prescribing Guidelines in Psychiatry, 12th Edn. Chichester, UK: John Wiley & Sons.
Upthegrove, R, Marwaha, S and Birchwood, M (2017) Depression and schizophrenia: cause, consequence, or trans-diagnostic issue? Schizophrenia Bulletin 43, 240244.
Van Dam, NT, O'Connor, D, Marcelle, ET, Ho, EJ, Cameron Craddock, R, Tobe, RH, Gabbay, V, Hudziak, JJ, Xavier Castellanos, F, Leventhal, BL and Milham, MP (2017) Data-driven phenotypic categorization for neurobiological analyses: beyond DSM-5 labels. Biological Psychiatry 81, 484494.
van Os, J and Kapur, S (2009) Schizophrenia. The Lancet 374, 635645.
Van Os, J, Gilvarry, C, Bale, R, Van Horn, E, Tattan, T, White, I and Murray, R (1999) A comparison of the utility of dimensional and categorical representations of psychosis. UK700 group. Psychological Medicine 29, 595606.
van Os, J, Hanssen, M, Bijl, RV and Vollebergh, W (2001) Prevalence of psychotic disorder and community level of psychotic symptoms: an urban-rural comparison. Archives of General Psychiatry 58, 663668.
van Os, J, Hanssen, M, de Graaf, R and Vollebergh, W (2002) Does the urban environment independently increase the risk for both negative and positive features of psychosis? Social Psychiatry and Psychiatric Epidemiology 37, 460464.
Vassos, E, Pedersen, CB, Murray, RM, Collier, DA and Lewis, CM (2012) Meta-analysis of the association of urbanicity with schizophrenia. Schizophrenia Bulletin 38, 11181123.
Vega, WA and Lewis-Fernandez, R (2008) Ethnicity and variability of psychotic symptoms. Current Psychiatry Reports 10, 223228.
Veling, W, Selten, J-P, Mackenbach, JP and Hoek, HW (2007) Symptoms at first contact for psychotic disorder: comparison between native Dutch and ethnic minorities. Schizophrenia Research 95, 3038.
Wallwork, RS, Fortgang, R, Hashimoto, R, Weinberger, DR and Dickinson, D (2012) Searching for a consensus five-factor model of the positive and negative syndrome scale for schizophrenia. Schizophrenia Research 137, 246250.
Whalen, DJ (2017) Using hybrid modeling to determine the latent structure of psychopathology. Biological Psychiatry 81, e41e42.
Wickham, H, Walsh, C, Asherson, P, Taylor, C, Sigmundson, T, Gill, M, Owen, MJ, McGuffin, P, Murray, R and Sham, P (2001) Familiality of symptom dimensions in schizophrenia. Schizophrenia Research 47, 223232.
Williams, J, Farmer, AE, Ackenheil, M, Kaufmann, CA and McGuffin, P (1996) A multicentre inter-rater reliability study using the OPCRIT computerized diagnostic system. Psychological Medicine 26, 775783.
World Health Organization (1992) The ICD-10 Classification of Mental and Behavioural Disorders: Clinical Descriptions and Diagnostic Guidelines. Geneva: World Health Organization.
Wykes, T, Huddy, V, Cellard, C, McGurk, SR and Czobor, P (2011) A meta-analysis of cognitive remediation for schizophrenia: methodology and effect sizes. The American Journal of Psychiatry 168, 472485.
Zachar, P and Kendler, KS (2017) The philosophy of nosology. Annual Review of Clinical Psychology 13, 4971.

Appendix

Non-author EU-GEI collaborators

Kathryn Hubbard1, Stephanie Beards1, Simona A. Stilo2, Mara Parellada3, Pedro Cuadrado4, José Juan Rodríguez Solano5, Angel Carracedo6, Enrique García Bernardo7, Laura Roldán3, Gonzalo López3, Bibiana Cabrera8, Esther Lorente-Rovira9, Paz Garcia-Portilla10, Javier Costas6, Estela Jiménez-López11, Mario Matteis3, Marta Rapado3, Emiliano González3, Covadonga Martínez3, Emilio Sánchez7, Ma Soledad Olmeda7, Nathalie Franke12, Fabian Termorshuizen13,14, Daniella van Dam12, Elsje van der Ven13,14, Elles Messchaart14, Marion Leboyer15–18, Franck Schürhoff15–18, Stéphane Jamain16–18, Grégoire Baudin15,16, Aziz Ferchiou15,16, Baptiste Pignon15,16,18, Jean-Romain Richard16,18, Thomas Charpeaud18,19,21, Anne-Marie Tronche18,19,21, Flora Frijda22, Giovanna Marrazzo23, Lucia Sideli22, Crocettarachele Sartorio22,23, Fabio Seminerio22, Camila Marcelino Loureiro24,25, Rosana Shuhama24,25, Mirella Ruggeri26, Sarah Tosato26, Chiara Bonetto26 and Doriana Cristofalo26.

Affiliations

1Department of Health Service and Population Research, Institute of Psychiatry, King's College London, De Crespigny Park, Denmark Hill, London SE5 8AF, UK

2Department of Psychosis Studies, Institute of Psychiatry, King's College London, De Crespigny Park, Denmark Hill, London SE5 8AF, UK

3Department of Child and Adolescent Psychiatry, Hospital General Universitario Gregorio Marañón, School of Medicine, Universidad Complutense, IiSGM (CIBERSAM), C/Doctor Esquerdo 46, 28007 Madrid, Spain

4Villa de Vallecas Mental Health Department, Villa de Vallecas Mental Health Centre, Hospital Universitario Infanta Leonor / Hospital Virgen de la Torre, C/San Claudio 154, 28038 Madrid, Spain

5Puente de Vallecas Mental 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

6Fundación Pública Galega de Medicina Xenómica, Hospital Clínico Universitario, Choupana s/n, 15782 Santiago de Compostela, Spain

7Department of Psychiatry, Hospital General Universitario Gregorio Marañón, School of Medicine, Universidad Complutense, IiSGM (CIBERSAM), C/Doctor Esquerdo 46, 28007 Madrid, Spain

8Department of Psychiatry, Hospital Clinic, IDIBAPS, Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Universidad de Barcelona, C/Villarroel 170, escalera 9, planta 6, 08036 Barcelona, Spain

9Department of Psychiatry, School of Medicine, Universidad de Valencia, Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), C/Avda. Blasco Ibáñez 15, 46010 Valencia, Spain

10Department of Medicine, Psychiatry Area, School of Medicine, Universidad de Oviedo, Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), C/Julián Clavería s/n, 33006 Oviedo, Spain

11Department of Psychiatry, Servicio de Psiquiatría Hospital “Virgen de la Luz”, C/Hermandad de Donantes de Sangre, 16002 Cuenca, Spain

12Department of Psychiatry, Early Psychosis Section, Academic Medical Centre, University of Amsterdam, Meibergdreef 5, 1105 AZ Amsterdam, The Netherlands

13Rivierduinen Centre for Mental Health, Leiden, Sandifortdreef 19, 2333 ZZ Leiden, The Netherlands

14Department of Psychiatry and Neuropsychology, 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

15AP-HP, Groupe Hospitalier “Mondor”, Pôle de Psychiatrie, 51 Avenue de Maréchal de Lattre de Tassigny, 94010 Créteil, France

16INSERM, U955, Equipe 15, 51 Avenue de Maréchal de Lattre de Tassigny, 94010 Créteil, France

17Faculté de Médecine, Université Paris-Est, 51 Avenue de Maréchal de Lattre de Tassigny, 94010 Créteil, France

18Fondation Fondamental, 40 Rue de Mesly, 94000 Créteil, France

19CMP B CHU, BP 69, 63003 Clermont Ferrand, Cedex 1, France

20EPS Maison Blanche, Paris 75020, France

21Université Clermont Auvergne, EA 7280, Clermont-Ferrand 63000, France

22Department of Experimental Biomedicine and Clinical Neuroscience, Section of Psychiatry, University of Palermo, Via G. La Loggia n.1, 90129 Palermo, Italy

23Unit of Psychiatry, “P. Giaccone” General Hospital, Via G. La Loggia n.1, 90129 Palermo, Italy

24Departamento 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

25Núcleo de Pesquina em Saúde Mental Populacional, Universidade de São Paulo, Avenida Doutor Arnaldo 455, CEP 01246-903, SP, Brasil

26Section of Psychiatry, Department of Neuroscience, Biomedicine and Movement, University of Verona, Piazzale L.A. Scuro 10, 37134 Verona, Italy