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.

Public Health Relevance

This research will provide an understanding of how problem drinking may spread within a non-linear dynamic network of social relationships. Findings will provide a framework for guiding research on drinking and social networks, and may be used to maximize the effects of prevention and intervention efforts.

National Institute of Health (NIH)
National Institute on Alcohol Abuse and Alcoholism (NIAAA)
Predoctoral Individual National Research Service Award (F31)
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Health Services Research Review Subcommittee (AA)
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Bloss, Gregory
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University of New Mexico
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United States
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Hallgren, Kevin A; Owens, Mandy D; Brovko, Julie M et al. (2015) Trajectories of Drinking Urges During Individual- and Couple-based Cognitive-Behavioral Treatment for Alcohol Use Disorders. Alcohol Treat Q 33:161-184
Hallgren, Kevin A; Ladd, Benjamin O; Greenfield, Brenna L (2013) Psychometric properties of the Important People Instrument with college student drinkers. Psychol Addict Behav 27:819-25
Hallgren, Kevin A (2013) Conducting Simulation Studies in the R Programming Environment. Tutor Quant Methods Psychol 9:43-60
Hallgren, Kevin A; McCrady, Barbara S (2013) Interference in the alcohol Stroop task with college student binge drinkers. J Behav Health 2:112-119
Hallgren, Kevin A (2012) Computing Inter-Rater Reliability for Observational Data: An Overview and Tutorial. Tutor Quant Methods Psychol 8:23-34