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This study investigated sociodemographic and clinical differences between the sexes in individuals affected by schizophrenia-spectrum disorders (SSD) who accessed outpatient mental health services. Within a retrospective cohort of 45,361 outpatients receiving care in Ferrara (Italy) from 1991 to 2021, those with a SSD diagnosis were compared between the sexes for sociodemographic and clinical characteristics before and after the index date (when the ICD-9: 295.*diagnosis was first recorded) to assess early trajectory, age and type of diagnosis, and severity of illness indicated by medication use, hospitalization, and duration of psychiatric care. Predictors of discharge were also investigated. Among 2439 patients, 1191 were women (48.8%). Compared to men, women were significantly older at first visit (43.7 vs. 36.8 years) and at index date (47.8 vs. 40.6) with peak frequency at age 48 (vs. 30). The most frequent last diagnosis recorded before the index date was delusional disorder (27.7%) or personality disorder (24.3%) in men and depression (24%) and delusional disorder (30.1%) in women. After the index date, long-acting antipsychotics and clozapine were more frequently prescribed to men (46.5% vs. 36.3%; 13.2% vs. 9.4%, p < 0.05) and mood stabilizers and antidepressants to women (24.3% vs. 21.1%; 50.1% vs. 35.5%; p < 0.05). Women had fewer involuntary admissions (10.1% vs. 13.6%) and were more likely to be discharged as the time under care increased (p = 0.009). After adjusting for covariates, sex was not a significant predictor of discharge. Our study confirmed that sex differences exist in clinical and sociodemographic characteristics of outpatients with SSD and that gender considerations might influence the rapidity of diagnosis and medications prescribed. These findings highlight the need to implement a women-tailored approach in specialist care programs for psychoses., (© 2023. The Author(s).)

Background: The immediate use of data exported from electronic health records (EHRs) for research is often limited by the necessity to transform data elements into an actual data set., Objective: This paper describes the methodology for establishing a data set that originated from an EHR registry that included clinical, health service, and sociodemographic information., Methods: The Extract, Transform, Load process was applied to raw data collected at the Integrated Department of Mental Health and Pathological Addictions in Ferrara, Italy, from 1925 to February 18, 2021, to build the new, anonymized Ferrara-Psychiatry (FEPSY) database. Information collected before the first EHR was implemented (ie, in 1991) was excluded. An unsupervised cluster analysis was performed to identify patient subgroups to support the proof of concept., Results: The FEPSY database included 3,861,432 records on 46,222 patients. Since 1991, each year, a median of 1404 (IQR 1117.5-1757.7) patients had newly accessed care, and a median of 7300 (IQR 6109.5-9397.5) patients were actively receiving care. Among 38,022 patients with a mental disorder, 2 clusters were identified; the first predominantly included male patients who were aged 25 to 34 years at first presentation and were living with their parents, and the second predominantly included female patients who were aged 35 to 44 years and were living with their own families., Conclusions: The process for building the FEPSY database proved to be robust and replicable with similar health care data, even when they were not originally conceived for research purposes. The FEPSY database will enable future in-depth analyses regarding the epidemiology and social determinants of mental disorders, access to mental health care, and resource utilization., (© Maria Ferrara, Elisabetta Gentili, Martino Belvederi Murri, Riccardo Zese, Marco Alberti, Giorgia Franchini, Ilaria Domenicano, Federica Folesani, Cristina Sorio, Lorenzo Benini, Paola Carozza, Julian Little, Luigi Grassi. Originally published in JMIR Medical Informatics (https://medinform.jmir.org).)