Major depressive disorder (MDD) is a serious and chronic public health problem with considerable variation in treatment response. Inconsistent responses to treatment are likely a result of the heterogeneity of the illness. Variation in patient profiles exists in depressive symptoms and in the interactions or feedback loops among MDD and aspects of one's relationships, general health, and personal economics. These feedback processes, which we identify as the systemic complexity of MDD, often contribute to the persistence of the illness. This project aims to improve our understanding of the complex feedback mechanisms in which major depressive disorder (MDD) is embedded and to use that knowledge towards personalized decision making in the treatment of MDD. We will use system dynamics methods to develop an individual-level model of MDD dynamics and the interactions between MDD and an individual's relationships, physical health, and economics. After calibration with four datasets, the model will be empirically validated against two benchmark MDD clinical trials to assure that the model is representative of well-targeted patient profiles. Finally, an exploration of the feasibility of usig systems modeling to test personalized approaches to the treatment of MDD will ensue and pharmacological and behavioral treatments will be preliminarily examined at an aggregate level. The use of simulated environments has benefited prior research on other complex public health problems, such as obesity, cardiovascular diseases, epidemic response, and diabetes and has the potential for a more rapid and cost effective test of personalized treatments for MDD. With promising results, our validated model will permit future investigations of treatments, including beneficial and adverse effects, treatment timing and long-term prognosis related to differing dosages of monotherapies and combined treatments for unique population subgroups.
Major depressive disorder (MDD) is a prevalent and chronic illness with significant heterogeneity that complicates its treatment. Using system dynamics methods and secondary data, this project will model these complex MDD dynamics, empirically validate the model, and use the model of MDD to explore the feasibility of testing beneficial and adverse effects of existing treatments on well-targeted patient profiles. This model will contribute to personalized decision making in the treatment of MDD.