Complex Systems Science: Applications to Health Behavior.

The proposed research combines perspectives from bioecological models of development with principles from the realist approach to evaluation. Specifically, the research will: (1) use complex social network methods (e.g., multilevel network analysis) to evaluate the dynamics between individual, social, and environmental determinants of health (e.g., substance use, risky sexual behavior, mental wellbeing); and (2) investigate complexities in social networks as they relate to intervention design in order to answer critical questions regarding the effective leverage of social networks to improve health outcomes. A well-established literature demonstrates that social networks and health are interrelated, such that networks impact the development of health behavior, and simultaneously, health behavior impacts the formation of social connections. Further, growing evidence indicates that peers can be effectively engaged in behavioral interventions. However, in order to fully harness the potential of social network methods to improve health outcomes, it is critically important to look beyond simple associations between networks and health, and network interventions and treatment outcomes, and begin explicitly investigating how these associations transfer across contexts, and through which underlying mechanisms these processes occur.

As a result, the aim of the proposed research is two-fold. First, by integrating multilevel modeling techniques with social network methods, the research will answer complex questions regarding variation in health outcomes dependent on individual, social, and environmental factors. Specifically, the proposed research will apply and expand upon existing multilevel techniques for social network analysis in order to answer critical questions regarding when, for whom, to what extent, and under what circumstances social networks impact health. For example, the research will examine variation at the individual, peer, and school level to determine the ways through which these interdependent factors contribute to health inequities (e.g., substance use) among adolescents. Second, the proposed research aims to advance our understanding of network-based interventions by moving beyond simply investigating efficacy of these approaches, and instead focuses on parsing apart the underlying mechanisms through which network-based interventions impact health outcomes. For example, the research will apply and expand upon diffusion of innovation methods in order to determine for whom, in what way, and how these interventions impact health behavior change.

In summary, the proposed research is nested within two complimentary approaches to behavioral health and intervention design; the bioecological theory of development, and the realist approach to evaluation. Through this framework, the research aims to uncover important variations in the relationship between social networks and health, while identifying novel intervention targets, and expanding our understanding of the mechanisms through which network interventions impact behavioral change. Lastly, the research uses a variety of existing datasets to accomplish the aforementioned goals.

Aims. The proposed research encompasses two primary objectives. First, the research will integrate multilevel modeling techniques with social network methods in order to answer complex questions regarding variation in health outcomes dependent on individual, social, and environmental factors. In particular, the research will examine variation at the individual, peer, and school level to determine the ways through which these interdependent factors contribute to health inequities (e.g., substance use) among adolescents. Secondly, the research aims to advance our understanding of network-based interventions by applying diffusion of innovation methods in order to determine for whom, in what way, and how these interventions impact health behavior change (e.g., smoking cessation, engagement in healthy sexual practices).

Methodology. Multiple longitudinal extant datasets specific to social networks and health behavior will be used. A variety of rigorous social network methodologies, including Multiple Membership Multiple Classification models, stochastic actor-based models, agent based models, and models of behavioral diffusion, will be employed.

Scientific and Medical Impact. The proposed research impacts science by disentangling critical interconnections between social networks and health across contexts. Findings will highlight the complex interdependencies between individual, social, and environmental determinants, and thus, inform the effective design of intervention efforts. Moreover, the proposed research will benefit public health and policy through the identification of specific targets for sociocontexual interventions, in addition to advancing our understanding of how and for whom network interventions exert influence on health behavior change.

Justification: A well-established literature demonstrates that social networks and health are interrelated, such that networks impact the development of health behavior, and simultaneously, health behavior impacts the formation of social connections. Further, growing evidence indicates that peers can be effectively engaged in behavioral interventions. However, in order to fully harness the potential of social network methods to improve health outcomes, it is critically important to look beyond simple associations between networks and health, and network interventions and treatment outcomes, and begin explicitly investigating how these associations transfer across contexts, and through which underlying mechanisms these processes occur.

Summary of aims: As a result, the aim of this research is two-fold. First, by integrating multilevel modeling techniques with social network methods, the research will answer complex questions regarding variation in health outcomes dependent on individual, social, and environmental factors. Second, the research aims to advance our understanding of network-based interventions by moving beyond simply investigating efficacy of these approaches, and will instead focus on parsing apart the underlying mechanisms through which network-based interventions impact health outcomes.

Specifically, the impact objectives of the project are as follows.

1. Uncover important variations in the relationship between social networks and health.
Advanced quantitative methods from social network analysis will be used to highlight the complex interdependencies between individual, social, and environmental determinants of health. As a result, the research will answer critical questions regarding when, for whom, to what extent, and under what circumstances social networks impact health.

2. Identify novel intervention targets.
Through the identification of important differences in the relationship between social networks and health, the research will inform the effective design of intervention efforts. In particular, practitioners, health program developers, and policy makers will be better able to design intervention strategies for varying populations, thereby impacting health outcomes.

3. Highlight intervention strategies to reduce health inequities.
Given that the research will examine health outcomes based on individual, social, and environmental factors, effective avenues to reduce health inequities among vulnerable populations will be highlighted. Consequently, the results will assist policy makers and program developers in developing intervention efforts that target risk factors related to health disparities.

4. Advance understanding of peer-led interventions.
The research will explicitly examine the mechanisms through which network-based interventions exert influence on health behavior, thereby allowing for improvements in the way peers are incorporated into behavior change programs. The findings will benefit public health and policy by providing stakeholders with an advanced understanding of how and for whom network interventions exert influence on health behavior change.

In summary, the project will uncover important variations in the relationship between social networks and health, while identifying novel intervention targets, and expanding our understanding of the mechanisms through which network interventions impact behavioral change. In doing so, the research promotes improvements in population health across diverse communities.