Examining Social Capital's Role in Predicting Health Risk Behaviors and Depression Among Low-Income Mothers: A Latent Class Analysis

Background: Disparities in maternal behavioral health risk behaviors and depression have a long association with women’s socioeconomic statuses (SES; Wilmot & Dauner, 2016). Although the association between behavioral health and SES is clear, the causal mechanisms behind this association are largely debated. Social capital is one causal mechanism currently being debated within sociological and public health literature (Song, 2013; Wilmot & Dauner, 2016). Few longitudinal studies have examined how different aspects of social capital simultaneously influence low-income mother’s subsequent engagement in health risk behaviors and depression. Focusing on the protective factors present within vulnerable communities may be especially key to developing social interventions and policies with a capacity to positively impact mothers with mental health and substance use needs.

Purpose: The purpose of this study was to identify subgroups of low-income mother’s based on perceived levels of varying forms of social capital and whether group membership predicted depression and health risk behaviors. Understanding the etiology of maternal behavioral health issues can inform policies and practices that promote enhanced prevention. This, in turn, may help ensure the healthy development for all youth, one of the Grand Challenges in the social work profession.

Methods: A secondary data analysis was performed using the Fragile Families and Child Wellbeing (FFCW) data; a longitudinal study of families at an increased risk of experiencing poverty and its adverse effects. The sample for this study included 2,759 mothers of young children who completed self-report interviews at waves 5 and 9. First, a latent class analysis was conducted to identify classes of low-income mothers based on perceptions of social trust and support, cohesion and control in their neighborhoods. Next, a series of hierarchical logistic regressions were used to determine whether class membership (i.e., unique clusters of social capital) predicted depression, daily alcohol use, smoking and substance use controlling for sample demographics and socio-economic indicators.

Results: Four latent class were identified: (1) low trust, control, and cohesion (5.3%), (2) high control, low trust and cohesion (11.2%), (3) high trust, control, and cohesion (69.1%), and (4) low control and cohesion (14.5%). Compared to class three, the odds of meeting criteria for clinical depression were significantly higher for classes 1 (93%, p = .004, 95% BCa CI .17, 1.11) and 2 (54%, p = .016, 95% BCa CI .08, .79). Membership in class two increased the odds of smoking by 44% (p =.001, 95% BCa CI .09, .64). No differences were observed for substance use or alcohol use between classes.

Conclusions: Our results support that social capital experiences differ among low-income women and that the unique clustering of different indicators of social capital significantly predicts behavioral health outcomes that are important to overall family wellbeing. Further, we found evidence that, when combined with other measures of social capital, some indicators that have been hypothesized as protective factors may function to promote poor behavioral health outcomes. We will discuss the theoretical and practical implications relevant to guiding effective behavioral health intervention and prevention targeting low-income mothers.