Dynamics of Food and Nutrition Security Among College Students in High-Income Countries: A Multi-Method Computational Modeling Approach

Food and nutrition insecurity (FNI) remains a significant and growing public health challenge among college students in high-income countries, impacting academic performance, physical health, and mental well-being. Despite increasing recognition of this issue, a comprehensive understanding of its complex, dynamic determinants and the potential long-term effects of intervention strategies is often lacking. This dissertation addresses this gap by developing and evaluating a computational model to elucidate the intricate dynamics of FNI among college students.

Aim 1 involved conducting a systematic umbrella review to synthesize existing evidence on the significant determinants of food and nutrition security among college students. This was followed by a meta-meta-analysis to quantitatively assess the aggregated impact of these determinants, culminating in the construction of a comprehensive causal loop diagram that visually maps the interconnections and feedback loops within the FNI system.

Aim 2 focused on creating a robust synthetic college student population. This computationally derived population, informed by available empirical data, serves as a realistic and scalable platform for agent-based modeling, capturing diverse demographic, socioeconomic, and behavioral characteristics relevant to FNI.

Aim 3 entailed the development and rigorous evaluation of an agent-based model (ABM) designed to simulate college student academic and health behaviors in response to varying levels of food security. This ABM allows for the dynamic forecasting of the impact of implementing different prevention and intervention strategies on food security status and its related outcomes, such as academic success, mental health, and overall well-being.

By integrating systematic review, meta-analysis, and agent-based modeling, this dissertation provides a novel, systems-level perspective on college student FNI. The developed computational model offers a powerful tool for policymakers and university administrators to proactively design and evaluate evidence-informed strategies, ultimately contributing to improved food and nutrition security and holistic student success in higher education settings.