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（Beattie，2001; Longabaugh，维尔茨，Zywiak，& O'Malley，2010; Project MATCH Research Group，1997，1998）。这种关联被认为是由两个同时发生的相互作用的过程引起的，其中大量饮酒的社交网络影响个体的饮酒（即，社会影响），而个体的饮酒影响他或她对重度饮酒网络成员的选择（即，社会选择; Krull，Sher，&杰克逊，2007; Schulenberg 1999）。这些同时发生的、相互作用的过程创造了一个积极的反馈循环，在社会关系网络中，它导致了饮酒行为的非线性动态效应。当只对系统的各个组成部分进行采样和分析时，可以适度地理解这种影响;然而，必须对整个相互作用的网络进行整体研究，以解释观察到的许多复杂性。 收集完整的社交网络数据可能是困难和昂贵的，随着时间的推移包括多个观察增加了进一步的复杂性。由于采样困难和非线性动态效应的存在，社交网络的计算机模拟通常用于理解这些系统。对艾滋病毒、传染病和肥胖症传播的模拟为针对性预防和治疗提供了有用的策略，并产生了额外的、具体的假设，这些假设可以在未来的模拟或真实的世界网络中进行探索（Bahr，布朗宁，Wyatt，& Hill，2009; KosiDski & Grabowski，2007; Kretzschmar & 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%的酒精依赖个体会使系统中的酒精依赖率呈指数衰减，但治疗4%或6%则不会。然而，这项研究并没有解决其他相关的假设，可能是由模拟研究的指导下，和几个方法的因素限制了这项研究的结果的概括。 本研究将产生计算机模拟饮酒在社交网络的目的是了解饮酒如何传播的社会网络。计算机模拟将生成各种类型的基于随机行为者的网络（Snijders，货车de Bunt，& Steglich，2010; Watts，1999），并将同时模拟个人饮酒随时间的变化，作为社交网络饮酒的函数（即，社会影响），以及作为个体饮酒的函数的社会网络组成的变化（即，社会选择）。个体和系统水平的协变量将被纳入模型中，例如性别、个体水平对酒精问题的易感性以及增加或减少酒精消费的系统水平的努力。模拟网络将被操纵，以测试哪些组件，当有针对性的治疗或预防，创造最大的影响，减少酒精问题的更大的网络。研究结果还将为未来可能使用社交网络的真实世界观察的研究提供假设。

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.