Background: Social circumstances may contribute to health disparities in patients with stroke through indirect and interdependent mechanisms that are not well understood. Objective: This study used complexity science methodology to examine the interplay between county- and individual-level social and clinical factors influencing stroke functional outcomes. Methods: As part of the Get With the Guidelines-Stroke (GWTG-S) Data Challenge, the GWTG-S Registry was merged with county-level Institute for Health Metrics and Evaluation data. Patients diagnosed with stroke (ischemic, subarachnoid, or intracerebral hemorrhage) were included. Multilayer networks were constructed by estimating mixed graphical models of 32 nodes across four layers, including social (county- and patient-level) and clinical (comorbidities and encounter) factors. Social determinant networks were estimated for patients with less favorable (ie., functional dependence or death, modified Rankin Score [mRS] 3-6) versus favorable outcomes (ie., functional independence, mRS 0-2). Network structure and node centrality were compared between mRS groups using bootstrap permutation analyses. Hub nodes were defined by betweenness centrality and facilitate effects across the network. Results: From 2013-2019, 990 721 (62.3%) stroke patients had mRS of 3-6 and 597 477 (37.6%) had mRS 0-2 at discharge. Compared to the mRS 0-2 group, the mRS 3-6 group’s social determinants network had greater global connectivity (p<0.001), and homelessness (p<0.001) and Black race (p<0.001) were hub nodes. Unique to the mRS 3-6 group, younger patients were more likely to identify as homeless (p=0.031), uninsured (p=0.001), and live in a county with lower per capita income (p<0.001). Conclusions: The effects of social determinants were significantly greater in patients with less favorable functional outcomes. Homelessness and race play critical roles in mediating the impacts of county- and individual-level social determinants on downstream disparities in stroke outcomes. Particularly among younger patients, housing, insurance status, and income may serve as a critical leverage point for county-level interventions to improve function after stroke.