Despite widespread burden across individual and systemic levels, food insecurity (FI) and eating disorders have only recently been linked empirically. Furthermore, medical professionals often ignore the role of non-weight variables (e.g., discrimination) in the etiology and maintenance of health disparities in larger-bodied populations. We aim to uncover cross-sectional relationships between discrimination, FI, mental health outcomes (MHO), and physical health consequences (PHC). Because our variables represent lived experiences, quantifying this data requires careful nuance. Nonetheless, we expect to model the following relationship using advanced statistical methods: via FI and MHO, discrimination predicts PHC. This work will lay the foundation for future longitudinal studies and, if our hypotheses are confirmed, call for fundamental changes to medical care across the weight spectrum.
We will use structural equation modeling (SEM) using the lavaan package in R and serial mediation models using the PROCESS macro in SPSS. These methods are often used in clinical research, as they allow for exploration of interrelationships among multiple variables with complex measurement methods. Self-report questionnaire data (N = 1733, 67% white) has been collected from a large, moderately diverse sample. Our insights so far suggest that our sample is indeed impacted by discrimination, FI, and MHO/PHC.