Peer Network Influence on HIV Risk

描述（由申请人提供）：制定有效的干预措施， 由于青少年的友谊网络而改变或缓冲风险， 研究以了解网络影响的机制和调节者。 这是一项研究友谊网络在艾滋病毒风险中的作用的建议 在青少年中传播。主要的重点是， 青少年同伴群体在行为和态度上起作用， 艾滋病毒感染风险。拟议的研究将应用研究中获得的知识， 与其他高风险行为相关的同伴网络，并从研究结果中得出 有关社交网络对其他人群艾滋病风险增加的帮助 了解青少年同伴网络的各个方面， 青少年艾滋病毒风险。这项建议的具体目标是：1）研究 与风险密切相关的同伴网络因素，包括行为 和信仰的同龄人，以及个人的立场，就他或 她的同伴网络，和2）研究缓冲或调节作用的家庭 如家庭关系和养育方式等因素。这些问题 将通过Add-Health数据集的二次分析来解决。 具体而言，Add-Health数据集的饱和学校子集（n=2654） 将被用来创建同龄人网络的措施，平均艾滋病毒风险行为， 同龄人（无保护措施的性行为，吸毒，酗酒），同龄人的信仰支持 无保护性行为、家庭凝聚力和两波数据的监测 集合，尽可能使用父源和子源。分析将 是纵向的，并将解释内的个人筑巢 网络和学校通过使用混合模式。

DESCRIPTION (provided by applicant): Developing effective interventions to change or buffer risk due to adolescent friendship networks will require basic research to understand the mechanisms and moderators of network influences. This is a proposal to study the role of friendship networks in risk for HIV transmission among adolescents. The primary focus is on the role that norms of adolescent peer groups play in behaviors and attitudes associated with elevated HIV infection risk. The proposed study will apply knowledge gained in studies of peer networks related to other high-risk behaviors and draw from findings about social networks related to HIV risk of other populations to help increase understanding of the aspects of adolescent peer networks that can mitigate adolescent HIV risk. The specific aims of this proposal are 1) to study the peer network factors that are closely related to risk, including the behavior and beliefs of peers, and the position of the individual with respect to his or her peer network, and 2) to study the buffering or moderating effects of family factors such as family relationships, and parenting practices. These questions will be addressed through secondary analysis of the Add-Health data set. Specifically, the saturated schools subset of the Add-Health data set (n=2654) will be used to create measures of peer networks, average HIV risk behaviors of peers (unprotected sex, drug use, alcohol use), peers' beliefs endorsing unprotected sex, family cohesion, and monitoring across two waves of data collection, using both parent and child sources where possible. Analyses will be longitudinal, and will account for the nesting of individuals within networks and schools through the use of mixed models.