Introduction There are few published empirical data on the effects of COVID‐19 on mental health, and until now, there is no large international study. Material and methods During the COVID-19 pandemic, an online questionnaire gathered data from 55,589 participants from 40 countries (64.85% females aged 35.80 ± 13.61; 34.05% males aged 34.90±13.29 and 1.10% other aged 31.64±13.15). Distress and probable depression were identified with the use of a previously developed cut-off and algorithm respectively. Statistical analysis Descriptive statistics were calculated. Chi-square tests, multiple forward stepwise linear regression analyses and Factorial Analysis of Variance (ANOVA) tested relations among variables. Results Probable depression was detected in 17.80% and distress in 16.71%. A significant percentage reported a deterioration in mental state, family dynamics and everyday lifestyle. Persons with a history of mental disorders had higher rates of current depression (31.82% vs. 13.07%). At least half of participants were accepting (at least to a moderate degree) a non-bizarre conspiracy. The highest Relative Risk (RR) to develop depression was associated with history of Bipolar disorder and self-harm/attempts (RR = 5.88). Suicidality was not increased in persons without a history of any mental disorder. Based on these results a model was developed. Conclusions The final model revealed multiple vulnerabilities and an interplay leading from simple anxiety to probable depression and suicidality through distress. This could be of practical utility since many of these factors are modifiable. Future research and interventions should specifically focus on them.

IntroductionA specific clinically relevant staging model for schizophrenia has not yet been developed. The aim of the current study was to evaluate the factor structure of the PANSS and develop such a staging method.MethodsTwenty-nine centers from 25 countries contributed 2358 patients aged 37.21 ± 11.87 years with schizophrenia. Analysis of covariance, Exploratory Factor Analysis, Discriminant Function Analysis, and inspection of resultant plots were performed.ResultsExploratory Factor Analysis returned 5 factors explaining 59% of the variance (positive, negative, excitement/hostility, depression/anxiety, and neurocognition). The staging model included 4 main stages with substages that were predominantly characterized by a single domain of symptoms (stage 1: positive; stages 2a and 2b: excitement/hostility; stage 3a and 3b: depression/anxiety; stage 4a and 4b: neurocognition). There were no differences between sexes. The Discriminant Function Analysis developed an algorithm that correctly classified >85% of patients.DiscussionThis study elaborates a 5-factor solution and a clinical staging method for patients with schizophrenia. It is the largest study to address these issues among patients who are more likely to remain affiliated with mental health services for prolonged periods of time.

Previously, we established an estimated exposome score for schizophrenia (ES-SCZ) as a cumulative measure of environmental liability for schizophrenia to use in gene–environment interaction studies and for risk stratification in population cohorts. Hereby, we examined the discriminative function of ES-SCZ for identifying individuals diagnosed with schizophrenia spectrum disorder in the general population by measuring the area under the receiver operating characteristic curve (AUC). Furthermore, we compared this ES-SCZ method to an environmental sum score (Esum-SCZ) and an aggregate environmental score weighted by the meta-analytical estimates (Emet-SCZ). We also estimated ORs and Nagelkerke’s R2 for ES-SCZ in association with psychiatric diagnoses and other medical outcomes. ES-SCZ showed a good discriminative function (AUC = 0.84) and statistically significantly performed better than both Esum-SCZ (AUC = 0.80) and Emet-SCZ (AUC = 0.80). At optimal cut point, ES-SCZ showed similar performance in ruling out (LR− = 0.20) and ruling in (LR+ = 3.86) schizophrenia. ES-SCZ at optimal cut point showed also a progressively greater magnitude of association with increasing psychosis risk strata. Among all clinical outcomes, ES-SCZ was associated with schizophrenia diagnosis with the highest OR (2.76, P < .001) and greatest explained variance (R2 = 14.03%), followed by bipolar disorder (OR = 2.61, P < .001, R2 = 13.01%) and suicide plan (OR = 2.44, P < .001, R2 = 12.44%). Our findings from an epidemiologically representative general population cohort demonstrate that an aggregate environmental exposure score for schizophrenia constructed using a predictive modeling approach—ES-SCZ—has the potential to improve risk prediction and stratification for research purposes and may help gain insight into the multicausal etiology of psychopathology.

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Background: A cumulative environmental exposure score for schizophrenia (exposome score for schizophrenia: ES-SCZ) may provide potential utility for risk stratification and outcome prediction. Here, we investigated whether ES-SCZ was associated with functioning in patients with schizophrenia spectrum disorder, unaffected siblings, and healthy controls. Methods: This cross-sectional sample consisted of 1261 patients, 1282 unaffected siblings, and 1525 healthy controls. The Global Assessment of Functioning (GAF) scale was used to assess functioning. ES-SCZ was calculated based on our previously validated method. The association between ES-SCZ and the GAF dimensions (symptom and disability) was analyzed by applying regression models in each group (patients, siblings, controls). Additional models included polygenic risk score for schizophrenia (PRS-SCZ) as a covariate.