Conference Agenda

This study explores the impact of family interaction and family structure on self-rated health among Asian Americans from a social network perspective. Using data from the American Time Use Survey (ATUS), we analyze how different family structures shape individuals’ social connections and health outcomes. We further examine family interaction frequency and time as key mechanisms influencing health. We hypothesize that stronger family networks and more frequent interactions provide greater social support and emotional resources, leading to improved self-rated health. However, certain family structures or culture may introduce caregiving burdens or role strain, resulting in complex health effects. Employing an Ordered Probit Model, we control for socioeconomic status, age, gender, disability status, Asian subgroups, and cultural background. This study contributes new empirical evidence to the intersection of social networks and health research, highlighting the critical role of family networks in promoting well-being among Asian American communities.

Health emergencies are situational (Alonzo 1979). Social responses to bodily changes emerge from diverse social situations, including time, place, and people, influencing pathways to healthcare. Current network approaches, however, overlook real-time situations, focusing instead on stable personal networks that estimate responses during acute health emergencies. Consequently, specifying the social mechanisms affecting health inequality, particularly healthcare access, remains challenging. To increase specificity, we examine the momentary social situations of individuals who arrived at the hospital facing an acute health emergency—stroke. A survey of 246 individuals (egos) used novel situation-based name generators and identified two types of responders (alters) involved during responses: local (physically proximate alters) and remote (physically distant alters). There are four principal findings. First, 95.27% of individuals had responders involved, and among these, 48.09% reported a remote responder. Second, among the responders identified, remote responders were significantly more likely to be perceived as knowledgeable about stroke (49.71% versus 32.61%, p = 0.0034) and were medical experts (41.71% versus 17.39%, p < 0.0001) than local responders. Third, remote responders were significantly less likely first to notice symptoms than local responders (28.57% versus 52.90%, p < 0.0001), suggesting deliberate activation as a mechanism for gaining novel information during responses. Fourth, net of individual factors and social settings, remote responder involvement—situational structure—was significantly associated with an increased risk of delayed hospital arrival (>2 hours). A socio-situational approach reveals that the structural characteristics of situations are related to healthcare access, specifically the involvement of physically distant responders.

Past research has shown that chronic conditions tend to increase depressive symptoms. However, research on the moderating role of social networks has been largely asymmetric. Despite substantial interest in the positive aspects of relationships to provide social resources for coping with health changes and buffering depressive symptoms, less is known about how adverse aspects of social relationships and networks can be detrimental to coping efforts and amplify the mental health consequences of chronic conditions. To test whether and to what extent social costs may make chronic conditions more depressing, I use a national sample of adults aged 40 and above, the German Ageing Study (DEAS). Analyses evaluated the relationship between changes in chronic conditions and depressive symptoms and whether this association was exacerbated by social network costs. A structural equation modeling approach using dynamic panel models with fixed effects and full information maximum likelihood estimation adjusts for the effects of social resources, specific time-varying characteristics, and earlier depressive symptoms. This approach also improves confidence for causal inference into the moderating role of social network costs by accounting for missing data and confounding based on stable unobserved covariates. Results suggest that certain dimensions of social negativity amplify depressive symptoms following changes in chronic conditions, underscoring the detrimental impacts of social costs on mental health. With multimorbidity becoming increasingly prevalent across the adult life course, the double-edged nature of social relationships has the potential to substantially impact the mental health profile of aging populations.

Prenatal distress affects a significant proportion of pregnant individuals and can lead to adverse outcomes for both parent and child. While research has identified various risk factors, the role of family support and stress remains understudied, particularly regarding specific structural and qualitative dimensions of family networks. This mixed methods study examines the relationship between family dynamics and prenatal distress using data from the Family Matters Study (n=436). Pregnant participants were recruited from a North Carolina health system between 2023-2024 and completed virtual survey interviews using EgoWeb 2.0. A subset of the survey respondents (n=59) also participated in follow-up semi-structured interviews. In the survey, prenatal distress is measured using nine items from the Revised Prenatal Distress Questionnaire (NuPDQ). Using a social network approach, we examine whether structural features of family networks (size, density, transitivity) and qualitative aspects of family relationships (proportion of positive, negative, and ambivalent relationships) are associated with prenatal distress levels. We combine these results with an analysis of the qualitative data to better understand the ways that families induce or ameliorate stress during pregnancy. Unlike previous research that often focuses narrowly on partner support or uses broad measures of family support that hide the ambivalence common to family ties, our approach captures the complex nature of family networks during pregnancy. This research contributes to a more nuanced understanding of how specific family configurations and relationship qualities relate to psychosocial well-being during pregnancy.

Closures of obstetrics units in rural hospitals lead to maternity care deserts and a cascade of poor maternal health outcomes among underserved communities in the United States. Little is known about pregnant women’s social networks as drivers of care engagement under these dire maternal care circumstances.

This study uses social network analysis (SNA) to examine how social relationships influence health decisions among rural women during pregnancy and childbirth. Specifically, this study employs a mixed-methods approach, combining SNA with qualitative interviews, to assess the role of network size, density, centrality, and support quality in pre/postnatal care decision-making. Participants include pregnant and recently postpartum women from rural communities in Tennessee, recruited through local healthcare centers and community organizations. We investigate strong social ties as drivers of engagement with formal healthcare services, the extent to which prenatal misinformation exists within networks, and the role of network support in mitigating barriers to care. By mapping and analyzing the structure, strength, and composition of women’s social networks, we explore how family, friends, healthcare providers, and community members shape prenatal care utilization and birth choices.

Forthcoming findings will contribute to our understanding of how rural women's social networks shape health decisions and inform the design of interventions to enhance positive social influences, strengthen healthcare provider-patient relationships, correct misinformation, and improve maternal health outcomes in rural areas. Understanding these network dynamics can inform policies to address rural health disparities by leveraging community-driven, network-based solutions for maternal health.