The Spread of Obesity in a Large Social Network over 32 Years

Abstract

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.

Peer effects, fast food consumption and adolescent weight gain

“…In a widely debated article, Christakis and Fowler (2007), using a 32-year panel dataset on adults from Framingham, Massachusetts and based on a logit specification, found that an individual's probability of becoming obese increased by 57% if he or she had a friend who became obese in a given interval. However, their analysis has been criticized for suffering from a number of limitations (see Cohen-Cole and Fletcher, 2008;Lyons, 2011;Shalizi and Thomas, 2011).…”

“…The evidence suggests the presence of strong peer effects in weight gain. Christakis and Fowler (2007), Trogdon et al (2008) and Renna et al (2008) seem to be unanimously pointing to the social multiplier as an important element in the obesity epidemics. 1 A social multiplier may amplify, at the aggregate level, the impact of any shock that affects obesity at the individual level.…”

Designing and Undertaking a Health Economics Study of Digital Health Interventions

“…22 In similar fashion, the effectiveness and cost effectiveness of DHIs may depend on diffusion through social networks for uptake and effect. For example, an Internet-delivered hand washing intervention resulted in reductions in respiratory infection in the user and also in family members who had not engaged with the intervention directly, 21 and smaller effects could spread more widely.…”

“…In addition, there may be feedback loops and potentially non-linear relationships, such as effectiveness at the individual level being partly dependent on nature of uptake at the group level (e.g.www.gettheworldmoving.com). 23 Since Christakis and Fowler, 22,24,25 there has been an explosion of epidemiologic studies using social network analytic methods for describing and understanding network effects. 26 However, there have been far fewer published attempts to use such methods as the basis for the design and evaluation of DHIs.…”

Preventing Type 2 Diabetes in Communities Across the U.S.

“…25,[28][29][30][31] Promising targets of the food environment that have been associated with diabetes risk in observational studies include approaches to increase fruit, vegetable, and whole-grain intake, and to reduce portion sizes and sweetened beverages. 27,30 Several strategies have been proposed for the food environment, including better food and menu labeling, and incentives for communities to support diverse healthy foods through farmers' markets and other diverse groceries.…”