The spread of obesity in a large social network over 32 years
Authors: Nicholas A Christakis
James H Fowler
Journal: New England Journal of Medicine (2007)
Discipline: CLINICAL MEDICINE
Keywords: obesity; social network; weight gain; clusters; body-mass index
Affiliations: Department of Health Care Policy, Harvard Medical School, Boston, MA 02115, USA. 

Abstract:  Background: 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. Methods: 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. Results: Discernible clusters of obese persons (body-mass index [the weight in kilograms divided by the square of the height in meters], &gt; 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. Conclusions: 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. 

Retracted: No

PubPeer comments:
 It s not clear why this highly-cited article has been so influential. It s just a highly-confounded exercise in post hoc correlation-fishing, used to support a very vague assertion. Using a lax 95% CI, the authors conclude...what? That  obesity appears to spread through social ties. What does this even mean? Here are the specific  results :  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. The idea that siblings, who share many of the same genes, might be more likely to have common outcomes (given an environment diet that is widespread and known to produce widespread obesity) for this reason doesn t seem to have occurred to the authors. The idea that people living in the same house might have similar diets, and that this might account for the  clustering, also doesn t seem to have occurred to them. Or is this the type of thing they re referring to by obesity  spreading through social ties ? They seem to be willfully turning a blind eye to potential confounds in favor of a frankly mystical alternative. There statistical choices are ad hoc, unrationalized. For example:  We assumed an independent working correlation structure for the clusters. Okey doke. According to Semantic Scholar:  An attractive property of the GEE is that one can use someworkingcorrelation structure that may be wrong, but the resulting regression coefficientestimate is still consistent and asymptotically normal. One convenient choice is theindependence model: treat the correlated responses as if they were independent. Is this a valid assumption? It would be nice to have these choices justified. , 
 It should also be noted that previous articles in the literature have found this paper s findings to be a potentially  spurious inference : https: www.sciencedirect.com science article pii S0167629608000362 ,