Targeting social networks to maximize alcohol use disorder treatment & prevention

以社交网络为目标，最大限度地提高酒精使用障碍的治疗效果

• 批准号: 8254018
• 负责人: Kevin A Hallgren
• 金额: $ 3.17万
• 依托单位: UNIVERSITY OF NEW MEXICO
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-12-01 至 2013-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8254018
• 关键词: Accounting; Address; Affect; Air; Alcohol abuse; Alcohol consumption; Alcohol dependence; Alcoholics Anonymous; Area; Behavior; Biological Models; Communicable Diseases; Complex; Computer Assisted; Computer Simulation; Data; Development; Drug Formulations; Environmental Wind; Feedback; Future; Gender; Goals; HIV; Heating; Heavy Drinking; Hurricane; Individual; Lead; Linear Models; Modeling; Neighborhoods; Network-based; Nonlinear Dynamics; Obesity; Oceans; Persons; Population Dynamics; Predisposition; Prevention; Preventive Intervention; Process; Recruitment Activity; Research; Research Design; Sampling; Simulate; Social Network; Social status; Specific qualifier value; Speed; Surface; System; Techniques; Temperature; Testing; Time; Water; Waxes; Work; abstracting; alcohol abuse therapy; alcohol use disorder; contextual factors; cost; density of AOD outlets; design; drinking; drinking behavior; experience; improved; interest; mathematical model; member; peer; peer influence; pollutant; pressure; response; simulation; social

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.

个体饮酒与社交网络饮酒表现出一致的正相关（Beattie, 2001; Longabaugh, Wirtz, Zywiak, & O'Malley, 2010; Project MATCH Research Group, 1997, 1998）。这种关联被认为是由两个同时相互作用的过程引起的，其中重度饮酒的社会网络影响个人的饮酒（即社会影响），而个人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, & Jackson, 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，在社会关系网络中导致饮酒行为的非线性动态影响。当只对系统的各个组成部分进行采样和分析时，这种影响可以被适度地理解；然而，必须从整体上研究整个相互作用的网络，以解释观察到的许多复杂性。收集完整的社交网络数据既困难又昂贵，而且随着时间的推移包括多个观察结果会增加更多的复杂性。由于采样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，可以在未来的模拟或现实世界网络中探索（Bahr, Browning, Wyatt, & Hill, 2009; KosiDski & Grabowski, 2007; Kretzschmar & Weissing, 1998）。一项研究对社会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%则没有。然而，这项研究并没有解决其他相关的假设，这些假设可能是由模拟研究指导的，而且一些方法学因素限制了这项研究结果的推广。本研究将生成社交网络中饮酒的计算机模拟，目的是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 Bunt, & Steglich, 2010; Watts, 1999），并将同时模拟个人饮酒随时间的变化，作为社会网络饮酒的函数（即社会影响），以及社会网络构成的变化作为个人饮酒的函数（即社会选择）。个体和系统层面的协变量将包括在模型中，如性别、个体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。模拟网络将被操纵，以测试哪些组成部分，当有针对性的治疗或预防时，在减少更大网络的酒精问题方面产生最大的效果。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会网络的真实观察。