Comparing symptom network structures across multiple psychiatric disorders: Large scale evidence from Canada

Abstract: The network approach to psychopathology conceptualizes psychiatric disorders as complex dynamic systems of causally connected symptoms and has recently gained prominence as an alternative to the traditional disease model. However, existing empirical investigations are often limited to small samples, a specific psychiatric diagnosis (particularly depression), or a small number of symptoms taken directly from pre-existing scales. Robust evidence on how symptom networks differ between disorders remains scarce. In inpatient mental health settings across three Canadian provinces (Ontario, Newfoundland and Labrador, and Manitoba), patients are routinely assessed using the interRAI Mental Health assessment at admission, discharge, every 90 days, and when a clinically significant change occurs. The assessment captures over 30 mental state indicators that measure the frequency of symptoms observed in the past three days. Between 2005 and 2024, population-representative assessment data were collected from 428,469 individuals. In this talk, we present preliminary findings from a study that aims to generate large-scale evidence on symptom profiles and networks across multiple psychiatric disorders. We will discuss the implications of these findings for the diagnosis and treatment of psychiatric disorders, and suicide prevention.

About the Speaker:

Hao Luo is an Assistant Professor in the School of Public Health Sciences at the University of Waterloo, a Docent in Statistics at the Department of Statistics, Uppsala University, and an interRAI fellow. Her research leverages large-scale real-world data to address critical clinical and policy questions related to aging and mental health. Her recent projects use multinational data with decades-long records to identify patterns of multimorbidity in persons with mental health conditions and to trace their trajectories over time. Her goal is to enhance the knowledge of the complexity of aging and health by data-linkage across the continuum of care.