Targeting social networks to maximize alcohol use disorder treatment & prevention

个人饮酒与社交网络饮酒呈一致的正相关关系(Beattie，2001；Longabaugh，Wirtz，Zywiak，&O‘Malley，2010；Project Match Research Group，1997,1998)。这种联系被认为是由两个同步的相互作用过程引起的，其中大量饮酒的社交网络影响个人的饮酒(即社会影响)，而个人的饮酒影响他或他对重度饮酒网络成员的选择(即社会选择；Krull，Sher，&Jackson，2007；Schulenberg 1999)。这些同时的、互惠的过程创造了一个正反馈循环，在社会关系网络中导致饮酒行为的非线性动态影响。当只对系统的个别组件进行采样和分析时，可以适当地理解这种影响；然而，必须对整个相互作用的网络进行整体研究，以考虑到所观察到的许多复杂性。收集完整的社交网络数据可能既困难又昂贵，而且随着时间的推移包括多个观察数据会进一步增加复杂性。由于采样的困难和非线性动态效应的存在，社会网络的计算机模拟经常被用来理解这些系统。对艾滋病毒、传染病和肥胖症传播的模拟为有针对性的预防和治疗提供了有用的策略，并产生了可以在未来的模拟或真实世界网络中探索的额外的、具体的假设(巴尔、布朗宁、怀亚特和希尔，2009；KosiDski和Grabowski，2007；Kretzschmar和Weissing，1998)。一项研究对社交网络中的酒精依赖进行了初步模拟(Braun，Wilson，Pelesko，Buchanan，&Gleeson，2006)，发现随机治疗8%的酒精依赖者会导致系统中酒精依赖率的指数下降，但治疗4%或6%的人不会。然而，这项研究没有解决其他可能由模拟研究指导的相关假设，而且几个方法学因素限制了这项研究结果的概括。本研究将生成社交网络中饮酒的计算机模拟，以了解饮酒如何在社交网络中传播。计算机模拟将生成各种类型的基于随机行为者的网络(Snijders，van de But，&Steglich，2010；Watts，1999)，并将同时模拟作为社交网络饮酒的函数的个人饮酒随时间的变化(即，社会影响)，以及作为个人饮酒的函数的社交网络构成的变化(即，社会选择)。模型将包括个人和系统层面的协变量，如性别、个人层面对酒精问题的易感性，以及系统层面增加或减少酒精消费的努力。模拟的网络将被操纵，以测试当目标为治疗或预防时，哪些成分在减少较大网络的酒精问题方面产生最大效果。研究结果还将为未来的研究提供假设，这些研究可能会使用对社交网络的真实观察。

Individual drinking shows a consistent, positive correlation with social network drinking (Beattie, 2001; Longabaugh, Wirtz, Zywiak, & O'Malley, 2010; Project MATCH Research Group, 1997, 1998). This association is thought to be caused by two simultaneous interacting processes, where a heavy drinking social network influences an individual's drinking (i.e., social influence), while the individual's drinking influences his or hr selection of heavy drinking network members (i.e., social selection; Krull, Sher, & Jackson, 2007; Schulenberg 1999). These simultaneous, reciprocal processes create a positive feedback loop that in a network of social relationships leads to non-linear dynamic effects of drinking behavior. Such effects can be modestly understood when only the individual components of the system are sampled and analyzed; however, the full interacting network must be studied in its entirety to account for many the complexities observed. Gathering full social network data can be difficult and expensive, and including multiple observations over time adds further complications. Because of the sampling difficulties and presence of non-linear dynamic effects, computer simulations of social networks are often used to understand these systems. Simulations of the spread of HIV, infectious diseases, and obesity have provided useful strategies for targeting prevention and treatment, and have yielded additional, specific hypotheses that can be explored in future simulated or real- world networks (Bahr, Browning, Wyatt, & Hill, 2009; KosiDski & Grabowski, 2007; Kretzschmar & Weissing, 1998). One study has conducted preliminary simulations of alcohol dependence in social networks (Braun, Wilson, Pelesko, Buchanan, & Gleeson, 2006), and found that treating 8% of the alcohol-dependent individuals at random created an exponential decay in alcohol dependence rates in the system, but treating 4% or 6% did not. However, this research did not address other relevant hypotheses that may be guided by simulation studies, and several methodological factors limit the generalizations of this study's findings. The present study will generate computer simulations of drinking in social networks for the purpose of understanding how drinking spreads within a social network. Computer simulations will generate various types of stochastic actor-based networks (Snijders, van de Bunt, & Steglich, 2010; Watts, 1999) and will simultaneously model changes in individual drinking over time as a function of social network drinking (i.e., social influence), and changes in social network composition as a function of individual drinking (i.e., social selection). Individual- and system-level covariates will be inclued in the model, such as gender, individual- level susceptibility to developing an alcohol problem, and system-level efforts that increase or decrease alcohol consumption. Simulated networks will be manipulated to test which components, when targeted for treatment or prevention, create maximal effects in reducing alcohol problems for the larger network. Results will also inform hypotheses for future research studies that may use real-world observations of social networks.