Expanding the Utility of Social Network Analysis for Multilevel Health Outcomes

描述（由申请人提供）：社会网络理论有潜力在多个层面上提高我们对人类健康问题的理解和处理，但我们目前缺乏关于网络的空间和数学关系如何与关系的内容和质量相关以及这种变化如何影响健康结果的基本信息。我们打算利用一种非人类灵长类动物，确定多个个体的内部因素（如个性和气质、遗传倾向）和外部因素（如环境和社会压力源）如何相互作用，影响网络结构和动态，以及这些因素如何反过来影响社会群体的健康结果。我们认为，非人灵长类动物模型为社会网络理论在人类健康方面的进步提供了几个优势，因为猴子为人类提供了认知和社会模拟，数据可以通过直接观察多个群体来收集（提供统计复制），并且所有个体的遗传和社会历史是完全已知的。我们有四个具体目标：(1)在与健康环境相关的多个层面上推进评估网络动态和稳健性的理论和方法；(2)描述作用于个人的内部和外部因素如何共同影响网络结构和稳健性；(3)

DESCRIPTION (provided by applicant): Social network theory has the potential to improve our understanding and treatment of human health issues on multiple levels, but we currently lack the basic information on how the spatial and mathematical relations of networks relate to the content and quality of relationships and how such variation influences health outcomes. We propose to determine, using a nonhuman primate, how internal (e.g., personality and temperament, genetic predispositions) and external factors (e.g., environmental and social stressors) in multiple individuals interact to affect network structure and dynamics and how these, in turn, influence health outcomes in social communities. We believe a nonhuman primate model offers several advantages to the advancement of social network theory with regard to human health because monkeys provide a cognitive and social analog for humans, data can be collected by direct observation of multiple communities (providing statistical replication), and the genetic and social history of all individuals is fully known.)We have four specific aims: (1) advance theory and methodologies assessing the network dynamics and robustness at multiple levels pertinent to the health context, (2) characterize how internal and external factors acting on individuals collectively influence network structure and robustness, (3) quantify the influence of network structure and robustness on metrics of stress as health outcomes and (4) assess the effects of experimental perturbation of network composition on network structure and robustness and health outcomes. Three main categories of data will be collected: (1) behavioral observation of affiliative and aggressive interactions, (2) assessment of individual internal factors including biobehavioral assessment of personality/ temperament and genotyping of the 5-HTTPLR and MAO-A genes, and (3) behavioral, physical and physiological measurement of health outcomes, including Rhadinovirus shedding, C-reactive protein levels, attitude, hydration, body condition, and trauma. Each of eight social groups will be observed 78 weeks across two years. Observers will record affiliate, aggressive, and submissive interactions among individuals using an event sampling design. Personality/temperament will be assessed by rating each animal on a list of 50 personality traits. Several health outcomes will be measured daily and during routine roundups. Behavioral data will be used to construct various social networks whose structure, dynamics, and robustness will be measured, and subsequently analyzed with respect to internal factors, health outcomes, and behavioral measures using multi-level generalized linear models.) PUBLIC HEALTH RELEVANCE: This study will advance the current understanding of social network structure and dynamics and develop new network measures and techniques to further understand how social network theory can be successfully applied to the understanding of health outcomes, and ultimately to the improvement of human health.