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      An Appraisal of Social Network Theory and Analysis as Applied to Public Health: Challenges and Opportunities

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      Annual Review of Public Health

      Annual Reviews

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          The spread of obesity in a large social network over 32 years.

          The prevalence of obesity has increased substantially over the past 30 years. We performed a quantitative analysis of the nature and extent of the person-to-person spread of obesity as a possible factor contributing to the obesity epidemic. We evaluated a densely interconnected social network of 12,067 people assessed repeatedly from 1971 to 2003 as part of the Framingham Heart Study. The body-mass index was available for all subjects. We used longitudinal statistical models to examine whether weight gain in one person was associated with weight gain in his or her friends, siblings, spouse, and neighbors. Discernible clusters of obese persons (body-mass index [the weight in kilograms divided by the square of the height in meters], > or =30) were present in the network at all time points, and the clusters extended to three degrees of separation. These clusters did not appear to be solely attributable to the selective formation of social ties among obese persons. A person's chances of becoming obese increased by 57% (95% confidence interval [CI], 6 to 123) if he or she had a friend who became obese in a given interval. Among pairs of adult siblings, if one sibling became obese, the chance that the other would become obese increased by 40% (95% CI, 21 to 60). If one spouse became obese, the likelihood that the other spouse would become obese increased by 37% (95% CI, 7 to 73). These effects were not seen among neighbors in the immediate geographic location. Persons of the same sex had relatively greater influence on each other than those of the opposite sex. The spread of smoking cessation did not account for the spread of obesity in the network. Network phenomena appear to be relevant to the biologic and behavioral trait of obesity, and obesity appears to spread through social ties. These findings have implications for clinical and public health interventions. Copyright 2007 Massachusetts Medical Society.
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            Social science. Computational social science.

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              The spread of behavior in an online social network experiment.

               Damon Centola (2010)
              How do social networks affect the spread of behavior? A popular hypothesis states that networks with many clustered ties and a high degree of separation will be less effective for behavioral diffusion than networks in which locally redundant ties are rewired to provide shortcuts across the social space. A competing hypothesis argues that when behaviors require social reinforcement, a network with more clustering may be more advantageous, even if the network as a whole has a larger diameter. I investigated the effects of network structure on diffusion by studying the spread of health behavior through artificially structured online communities. Individual adoption was much more likely when participants received social reinforcement from multiple neighbors in the social network. The behavior spread farther and faster across clustered-lattice networks than across corresponding random networks.
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                Author and article information

                Journal
                Annual Review of Public Health
                Annu. Rev. Public Health
                Annual Reviews
                0163-7525
                1545-2093
                March 20 2017
                March 20 2017
                : 38
                : 1
                : 103-118
                Affiliations
                [1 ]Institute for Prevention Research, Department of Preventive Medicine, School of Medicine, University of Southern California, Los Angeles, California 90034;
                10.1146/annurev-publhealth-031816-044528
                © 2017

                https://creativecommons.org/licenses/by-sa/4.0/

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