This note's aim is to investigate the sensitivity of Christakis and Fowler's claim [Christakis, N., Fowler, J., 2007. The spread of obesity in a large social network over 32 years. The New England Journal of Medicine 357, 370-379] that obesity has spread through social networks. It is well known in the economics literature that failure to include contextual effects can lead to spurious inference on "social network effects." We replicate the NEJM results using their specification and a complementary dataset. We find that point estimates of the "social network effect" are reduced and become statistically indistinguishable from zero once standard econometric techniques are implemented. We further note the presence of estimation bias resulting from use of an incorrectly specified dynamic model.

Objective To investigate whether “network effects” can be detected for health outcomes that are unlikely to be subject to network phenomena.Design Statistical analysis common in network studies, such as logistic regression analysis, controlled for own and friend’s lagged health status. Analyses controlled for environmental confounders.Setting Subsamples of the National Longitudinal Study of Adolescent Health (Add Health).Participants 4300 to 5400 male and female adolescents who nominated a friend in the dataset and who were both longitudinally surveyed.Measurements Health outcomes, including headache severity, acne severity, and height self reported by respondents in 1994-5, 1995-6, and 2000-1.Results Significant network effects were observed in the acquisition of acne, headaches, and height. A friend’s acne problems increased an individual’s odds of acne problems (odds ratio 1.62, 95% confidence interval 0.91 to 2.89). The likelihood that an individual had headaches also increased with the presence of a friend with headaches (1.47, 0.93 to 2.33); and an individual’s height increased by 20% of his or her friend’s height (0.18, 0.15 to 0.26). Each of these results was estimated by using standard methods found in several publications. After adjustment for environmental confounders, however, the results become uniformly smaller and insignificant.Conclusions Researchers should be cautious in attributing correlations in health outcomes of close friends to social network effects, especially when environmental confounders are not adequately controlled for in the analysis.

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Summary This paper considers the identification of social interaction effects in the context of multivariate choices. First, we generalize the theoretical social interaction model to allow individuals to make interdependent choices in different activities. Based on the theoretical model, we propose a simultaneous equation network model and discuss the identification of social interaction effects in the econometric model. We also provide an empirical example to show the empirical salience of this model. Using the Add Health data, we find that a student's academic performance is not only affected by academic performance of his peers but also affected by screen‐related activities of his peers.

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