Evaluating Effective Combinations of Modifiable Factors for Preventing Youth Depression Risk

Key Words: Depression, Mental Health, Prevention, Risk Factors, Protective Factors, Data Science

This project tackles the challenge of preventing depression in children, which represents a major growing source of distress and impairment. Depression in children is influenced by many different risk and protective factors, but the optimal combinations of these factors for effective prevention are not well understood. This project seeks to use cutting-edge methods to analyze how different modifiable risk and protective factors interact, and to identify the most effective combinations for reducing depression symptoms in children. The goal of this research is to determine which factors—and in what combinations—would yield the greatest impacts on child depression risk. By uncovering specific combinations of factors that can be best targeted together in prevention packages, this work can inform more precise and effective approaches for preventing depression in children, and lead to more efficient use of resources and better outcomes for children’s mental health.