Title: Predicting Depressive Symptoms from Demographic, Social, Psychological, Behavioral, and Genetic Factors in Older Adults: A Trajectory Analysis of the Health and Retirement Study
Supervisor: Dr. Eli Puterman
Committee members: Dr. Mark Beauchamp, Dr. Guy Faulkner
Abstract: Depressive symptoms in older adults are common, persistent, and are associated with cardiovascular disease, cognitive impairment, as well as earlier mortality. Previous research has identified risk factors for depressive symptoms in older adulthood such as increasing age, female sex, race and ethnicity, major life stressors, lack of social connectedness, neuroticism, physical inactivity, and polygenic risk scores. However, these multidisciplinary predictors have not been compared to determine their relative contribution to depressive symptoms in older adulthood. Furthermore, previous research has only assessed depressive symptoms and their predictors cross-sectionally or prospectively (i.e., 2 time points), which misconstrues the dynamic temporal nature of depressive symptoms and time-varying predictors. Therefore, using data from the Health and Retirement Study, this proposed thesis has three aims: 1) assess trajectories of depressive symptoms in older adults using growth mixture modelling, 2) identify predictors of trajectory groups in order of importance using random forest analysis, and 3) compare how trajectory groups differ in relation to the most important predictors. The significance of this thesis is that it may extend the literature by identifying the top predictors of depressive symptom trajectories in community dwelling older adults, which may then serve as grounds for hypothesis generation and provide candidates for targeted interventions.