Garstka, Teri A.Bonnett, MichaelaKennedy, MeaghanFernandez, JasmineHarms, Randi2024-06-262024-06-262024-06-25Garstka, T.A, Bonnett, M., Kennedy, M., Fernandez, J., & Harms, R. (2024, June 25). The Relationship Between Community Networks and Population-Level Outcomes [Conference Talk]. Sunbelt 2024: Networks and Resilience, Edinburgh, Scotland.https://hdl.handle.net/1808/35216This presentation was given June 25th, 2024 at the International Network of Social Network Analysis conference, Sunbelt 2024: Networks and Resilience, Edinburgh, Scotland.Social care networks are partnerships of organizations that coordinate, share resources, and refer individuals to services. Resilient communities often have strong provider networks dedicated to solving issues related to equitable access to services and outcomes (Corbie-Smith et al, 2019; DeFosset et al, 2023; Gundacker et al, 2020; Hamer & Mays, 2020; Hardin et al 2020). However, little evidence that collaboration between local health care and non-health care organizations improves health outcomes (Alderwick, et al, 2021). This study utilized a large population of social care to quantify the strength and cohesion of the network as a complex adaptive system and as a key driver of community-level change. We conducted network analysis using service referral interaction data between cross-sector organizations in each network to derive network cohesion metrics. We used a Difference-in-Difference (DiD) method to test the hypothesis: Stronger social care networks positively affect health-related factors compared to weaker networks. We conducted four Generalized Estimating Equations linear regressions using the DiD interaction to test network group as a predictor of changes on community-level Health Factors, Health Behaviors, Clinical Care, and Social/Economic Factors. Results showed a strong COVID Effect and as anticipated, every health factor significantly declined over time. However, network cohesion mattered. The Difference-in-Difference (Group X Time) test was significant for every dependent variable. Highly cohesive networks mitigated the negative effects of COVID. Communities with cohesive networks were protected against steep declines experienced by those with less cohesive social care networks. We conclude that network analysis is better suited to quantifying resilience in a community ecosystem and allows for a better understanding of what influences outcomes at the community-level. This work supports the notion that community resilience is a dynamic process that describes a network of adaptive capacities that impact human society and allow it to adapt after adversity and take advantage of opportunities (Garstka & Kennedy, 2023; Norris et al, 2007). From here, we can identify and test interventions to more effectively enhance community resilience and optimize impact by using a structured ecosystem approach. This is Tech-Enabled Community Resilience.Copyright 2024, The AuthorsSocial Care NetworksPopulation HealthNetwork AnalysisCommunity ResiliencePresentationhttps://orcid.org/0000-0001-7229-099Xhttps://orcid.org/0000-0001-8211-1326https://orcid.org/0009-0000-0672-3654openAccess