Schizophrenia is a severe and complex mental illness influenced by genetic and environmental factors. Across the globe, particularly in the Middle East, and North Africa region, structural barriers and disparities persist, hindering access of a substantial proportion of people with psychosis to mental health services. Thus, the mean duration of untreated psychosis is long. This leads to a worse prognosis in patients with schizophrenia. Consequently, establishing community-based models grounded on the identification of modifiable risk factors has emerged as a key research priority to inform targeted early interventions and ultimately improve patient outcomes. This study aims to determine the predictive factors of schizophrenia in southeastern and southern Morocco. A matched case-control study was conducted in the Moroccan regions of Drâa-Tafilalet and Guelmim Oued Noun. Cases were patients with schizophrenia diagnosed according to the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) criteria; whose clinical condition is stable and without acute symptoms, consulting the data collection services. Controls were healthy individuals without any mental illness who visited the same healthcare establishments. Cases and controls were matched using age and sex. A questionnaire was used to collect the data. Also, for each control, the Moroccan Arabic dialect version of the Mini International Neuropsychiatric Interview (MINI) was employed to ensure the absence of any likely mental illness. To identify predictive factors of schizophrenia, a multivariate logistic regression was performed. The study followed the ‘Strengthening the Reporting of OBservational studies in Epidemiology’ (STROBE) guidelines. 321 cases and 321 controls were included. Tobacco use, family history of psychosis marital status, educational attainment, socioeconomic conditions, and winter birth, are key predictive factors for schizophrenia in this population. Our findings support the neurodevelopmental-vulnerability-neurochemical model of schizophrenia. Identifying predictive factors can help target early detection and prevention, improving prognosis.
Boukhari et al. (Tue,) studied this question.