Global trajectories of common mental disorders during the COVID-19 pandemic: An analysis using multiple cohorts

• Grant number: 24/17532-0
• Support Opportunities: Scholarships in Brazil - Post-Doctoral
• Start date: July 01, 2025
• End date: June 30, 2026
• Field of knowledge: Health Sciences - Medicine - Psychiatry
• Principal Investigator: Isabela Judith Martins Bensenor
• Grantee: Pedro Fonseca Zuccolo
• Host Institution: Faculdade de Medicina (FM). Universidade de São Paulo (USP). São Paulo , SP, Brazil

Abstract

Evidence suggests that the mental health impacts of the COVID-19 pandemic was imbalanced across populations due to complex interactions between individual characteristics and contextual factors, including the timing and intensity of the pandemic and the restrictions imposed by governments to contain the spread of the virus. However, there is significant heterogeneity among the available research in the literature, making global analyses of the association between mental health and the pandemic challenging. This project is part of the COVID Global Mental Health Consortium (CGMHC), an international consortium funded by the National Institute of Health (NIH) created to gather longitudinal data from cohorts in different countries and conduct analyses of mental health outcomes associated with the pandemic. This proposal is aligned with one of CGMHC's analytical aims: to characterize trajectories of depression and anxiety symptoms during the pandemic across multiple cohorts and to analyze risk and resilience factors associated with these trajectories. Currently, the CGMHC has made available data from four cohorts with pre- and post-pandemic data (Brains for Dementia Research, ELSA-Brasil COVID-19, ELSA UK, and UK Biobank), and there is potential to include other cohorts. Individual-level variables will be harmonized across datasets to form a single dataset. We will use Group Based Trajectory Modeling (GBTM) to identify mental health trajectory groups in the overall sample. Next, we will use multinomial logistic regression models to identify predictors of these trajectories, considering contextual variables such as the severity of the pandemic, the stringency of social distancing measures, and individual factors. (AU)