The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Detecting implausible social network effects in acne, height, and headaches: longitudinal analysis
Open accessAbstract
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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The Spread of Obesity in a Large Social Network over 32 Years
Christakis, N. A., & Fowler, J. H. (2007) · New England Journal of Medicine · doi:10.1056/nejmsa066082 · Cited by 5,007
The collective dynamics of smoking in a large social network
Christakis, N. A., & Fowler, J. H. (2008) · New England Journal of Medicine · doi:10.1056/nejmsa0706154 · Cited by 2,390
Sterne, J. A. C. (2001) · BMJ · doi:10.1136/bmj.322.7280.226 · Cited by 1,466
Cited by
Social contagion theory: examining dynamic social networks and human behavior
Christakis, N. A., & Fowler, J. H. (2013) · Statistics in Medicine · doi:10.1002/sim.5408 · Cited by 1,303
Homophily and contagion are generically confounded in observational social network studies
Shalizi, C. R., & Thomas, A. C. (2011) · Sociological Methods & Research · doi:10.1177/0049124111404820 · Cited by 994
“…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).”
ResultA Survey of Statistical Network Models
Goldenberg, A., Zheng, A. X., Fienberg, S. E., & Airoldi, E. M. (2010) · Foundations and Trends® in Machine Learning · doi:10.1561/2200000005 · Cited by 819
Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries
Olteanu, A., Castillo, C., Diaz, F., & Kiciman, E. (2016) · doi:10.2139/ssrn.2886526 · Cited by 763
Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries
Olteanu, A., Castillo, C., Diaz, F., & Kıcıman, E. (2019) · Frontiers in Big Data · doi:10.3389/fdata.2019.00013 · Cited by 763
Metadata
- Authors
- Cohen-Cole, E., & Fletcher, J. M.
- Venue
- BMJ
- Publisher
- BMJ
- Published
- 2008-12-04
- Volume
- 337
- Issue
- dec04 2
- Pages
- a2533-a2533
- ISSN
- 0007-1447 (ISSN-L)
- 0959-8138
- 1468-5833
- OpenAlex
- W2030509747
- Cited by
- 183
- References
- 3
- FWCI
- 14.48
- Type
- journal article
- Language
- en
- Open access
- hybrid
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