Dissertation Defense: Chen Hu

"Determinants and optimization of disability outcomes in multiple sclerosis: Applications of markov models and casual inference framework", Department of Epidemiology, School of Public Health. Committee: Caterina Rosano, EPI (committee chair)Zongqi Xia, Neurology (advisor)Sonja Swanson, EPIChung-Chou Chang, BiostatisticsKangho Suh, Pharmacy and TherapeuticsAbstract: Multiple sclerosis (MS) is a chronic neuroinflammatory and neurodegenerative disease that leads to disability. Improving long-term disability outcomes is a major goal of MS care. In real-world clinical settings, disability trajectories vary across patients and are shaped by both risk profiles and treatment decisions. Important gaps remain in understanding how patient characteristics influence disability trajectories and how treatment strategies can be optimized across clinical contexts. The overall goal of this dissertation is to improve understanding of determinants of disability in MS and to generate evidence for optimizing disability outcomes using longitudinal and causal inference approaches applied to real-world data. In Aim 1, I applied multi-state Markov models to EHR-linked MS registries to evaluate how comorbidity burden influences disability transitions and trajectories. I found higher psychiatric and cardiometabolic comorbidity burden was associated with greater transition intensity toward worse disability states, lower transition intensity toward improvement, higher 5-year probability of reaching severe disability, and fewer years spent in low disability states. These findings supported a more integrated approach to MS care in which improving long-term outcomes requires attention to mental and vascular health in addition to MS itself. In Aim 2, using the same modeling framework, I assessed the association between treatment use and disability outcomes and whether these associations varied by age. I found higher-efficacy treatment use was associated with more favorable disability outc