Detecting implausible social network effects in acne, height, and headaches: longitudinal analysis

By Ethan Cohen-Cole, Jason M. Fletcher. 2008

190 citations recorded by the source corpus.

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.

Subfields: Experimental and Cognitive Psychology, General Health Professions, Health

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Citations in the corpus

Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019, background): “…Christakis and Fowler (2007) found obesity to spread through peer inﬂuence in social networks, others suggest that unobserved confounds correlating with the social network structure (Lyons, 2011) or environmental exposure (Cohen-Cole and Fletcher, 2008) may be the culprit rather than peer inﬂuence.”
A Survey of Statistical Network Models (2009, background): “For example Cohen-Cole and Fletcher note that there are plausible alternative explanations to the network structure based on contextual factors [77], and in a separate paper demonstrate that the same methodology detects “implausible” social network effects for such medical conditions as acne and hea”
Peer influences on adolescent alcohol consumption: evidence using an instrumental variables/fixed effect approach (2011, unknown): “See Cohen-Cole and Fletcher (2008c) for an example where one can estimate large “social effects” for outcomes where the true social effect is zero, such as the transmission of height.”
Peer Effects in the Diffusion of Solar Photovoltaic Panels (2012, methodology): “Thus, we follow Cohen-Cole and Fletcher (2008b) and perform a robustness check by including zip code indicator variables interacted with a time trend and quadratic time trend.”
Systems Science Methods in Public Health (2012, unknown): “The methods and conclusions of some of these studies have been challenged (34, 95), but the visibility of this work has helped to highlight the continuing importance of NA methods in modern public health science.”
Social contagion theory: examining dynamic social networks and human behavior (2011, background): “One paper that attempted to refute the regression approach embodied in equation (1) claimed to document the spread of phenomena in adolescents which were assumed to be intrinsically incapable of spread, such as acne, headaches, and height [81].”
Homophily and Contagion Are Generically Confounded in Observational Social Network Studies (2010, result): “…in 4010 of these 5000 trials is Effect of Phantom ‘Influencer' on ‘Influenced' in Time Series Effect of Phantom ‘Influenced' on ‘Influencer' in Time Series the estimate less than zero despite the lack of a direct connection, in line with the empirical results of Cohen-Cole and Fletcher (2008).”
Social Networks and Causal Inference (2013, background): “…similar results using the same methodology for factors such as height, acne, and headaches for which social influence seems much less plausible (Cohen-Cole and Fletcher 2008), inadequate control for homophily (Shalizi and Thomas 2011), changes in friendship structure giving rise to spurious…”