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: 8368089
• 负责人: Kevin A Hallgren
• 金额: $ 2.3万
• 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 2013-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8368089
• 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.

个人饮酒与社交网络饮酒表现出一致，正相关（Beattie，2001； Longabaugh，Wirtz，Zywiak和O'Malley，2010年；项目匹配研究小组，1997年，1998年）。人们认为这种关联是由两个同时进行互动过程引起的，在该过程中，大量饮酒的社交网络会影响一个人的饮酒（即社交影响），而个人的饮酒会影响他或人力资源的重型饮酒网络成员的选择（即社交选择； Krull，Sher，＆Jackson，＆Jackson，＆Jackson，2007; Schulenberg 1999）。这些同时的，相互的过程创造了一个积极的反馈回路，在社会关系网络中，这会导致饮酒行为的非线性动态影响。当只有对系统的各个组件进行采样和分析时，就可以适度理解此类效果。但是，必须全面研究完整的交互网络，以说明观察到的许多复杂性。 收集完整的社交网络数据可能很困难且昂贵，并且随着时间的流逝，包括多个观察结果会增加并发症。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。 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).一项研究已经对社交网络中的酒精依赖性进行了初步模拟（Braun，Wilson，Pelesko，Buchanan和Gleeson，2006年），发现以随机治疗8％的酒精依赖人在系统中的酒精依赖率中造成了指数衰减，但是治疗4％或6％的人却没有。但是，这项研究并未解决可能通过模拟研究指导的其他相关假设，而几个方法论因素限制了本研究发现的概括。 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。计算机模拟将产生各种类型的基于随机角色的网络（Snijders，van de Bunt和Steglich，2010; Watts，1999），并将同时模拟单个饮酒的变化，因为随着时间的推移，社交网络饮酒的功能（即社交影响），以及社交网络组成的变化，是个人饮酒的功能（即社交选择）。个体和系统级别的协变量将包括在模型中，例如性别，个人水平的易感性，对出现酒精问题的敏感性以及增加或减少酒精消耗的系统水平的努力。模拟网络将被操纵，以测试哪些组件在以治疗或预防为目标时，在减少较大网络的酒精问题方面会产生最大影响。结果还将为未来研究的假设提供有关可能使用社交网络的现实观察结果的假设。

项目成果

Conducting Simulation Studies in the R Programming Environment.

• DOI: 10.20982/tqmp.09.2.p043
• 发表时间: 2013-10-12
• 期刊: Tutorials in quantitative methods for psychology
• 作者: Hallgren KA