THE SPREAD OF ALCOHOL CONSUMPTION BEHAVIOR IN A LARGE SOCIAL NETWORK

By J. N. Rosenquist, Joanne M. Murabito, James H. Fowler, Nicholas A. Christakis. Annals of Internal Medicine, 2010

624 citations recorded by the source corpus.

Background Alcohol consumption has important health related consequences and numerous biological and social determinants. Objective Quantitatively explore the possibility of person-to-person spread of alcohol consumption behavior within a large social network involving friends, co-workers, siblings, spouses, and neighbors followed for 32 years. Design Longitudinal network cohort study Setting The Framingham Heart Study. Participants 12,067 people assessed at several time points between 1971 and 2003. Measurements At each time point, we measured: (1) self-reported alcohol consumption behavior, both in terms of number of drinks per week on average over the past year as well as number of days drinking within the past week, and (2) social network ties. Results Discernible clusters of drinkers and abstainers were present in the network at all time points, and the clusters extended to three degrees of separation (e.g., to a person's friends’ friends’ friends). These clusters were not solely due to selective formation of social ties among drinkers, but rather also appear to reflect inter-personal influence. Changes in the alcohol consumption behavior of an individual's social network had a statistically significant affect on an individual's subsequent alcohol consumption behavior. Immediate neighbors and coworkers did not exhibit statistically significant affects on drinking behavior. Limitations The measure of alcohol consumption is not a clinical one. Also, it is unclear whether these effects have positive or negative effects on long-term health, give that alcohol has been shown to have both harmful and protective health effects. Finally, not all network ties were observed. Conclusions Network phenomena appear to influence alcohol consumption behavior. This has implications for clinical and public health interventions, and further supports group-level interventions to reduce problematic drinking.

Subfields: Epidemiology, Experimental and Cognitive Psychology, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/0c9444b6907c9259b24007afc51c022a81ced95f

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Citations in the corpus

Social contagion theory: examining dynamic social networks and human behavior (2011, methodology): “Using similar modeling approaches and exploiting data from many sources, we have examined the “spread” of obesity [14, 15], smoking [16], alcohol consumption [17], health screening [18], happiness [19], loneliness [20], depression [21], sleep [22], drug use [22], divorce [23], food consumption [24],”
Loneliness, social isolation, and behavioral and biological health indicators in older adults. (2011, background): “Christakis and colleagues in studies on the Framingham offspring cohort showed that social networks were associated with positive health behaviors such as smoking cessation (Christakis & Fowler, 2008) and alcohol abstinence (Rosenquist, Murabito, Fowler, & Christakis, 2010) as well as risk factors s”
Family Health Behaviors (2017, positive): “…quality of care and health behaviors 7These include Franks et al. (2002), Leonard and Mudar (2004), Clark and Etilé (2006), Christakis and Fowler (2007), CohenCole and Fletcher (2008), Falba and Sindelar (2008), Rosenquist et al. (2010), Banks et al. (2013), McGeary (2013), and Cobb et al. (2014).”
Social Network Data Analytics (2011, methodology): “[56] provide a random-walk based probabilistic interpretation of spectral clustering algorithms including Normalized Cut [72].”