Social Networks, Health, and Youth

I’ve been interested for a while now in how information and behavior can spread through social networks; an important and timely sub-topic in this field is the spread of health behaviors. This area of study is especially important in understanding the behaviors of adolescents, as there are a number of unhealthy behaviors (ranging from drug use to unhealthy eating to unsafe sex practices) which start in adolescence, persist into adulthood, and contribute to some of the leading causes of death and disability. (See this CDC page on adolescent health behavior)

As any parent or educator will likely tell you, the behavior of teens closely linked in a social network will often display many similarities: teens who smoke or drink, for instance, are often friends with other teens who smoke or drink. By establishing and tracking the spread of these behaviors scientifically, we can gain a greater understanding of the mechanisms at work and perhaps harness them to help spread healthy behaviors instead of unhealthy ones.

When we look at two teens who share a common behavior pattern (healthy or unhealthy), we must ask ourselves: Did they become friends because of their similar behavior (selection), did their behavior become similar as a result of being friends (influence), or was there some third factor at work which influenced them both separately (confounding factors)? One simple way to attempt to answer this question is through a longitudinal study, where data is collected for the same group of subjects at multiple times. By looking at the co-evolution of the social network and the behavior network, we can parse out the role that each of these factors plays. Here, I wanted to briefly discuss a few studies which have employed social network analysis and longitudinal data collection to gain a better understanding of how unhealthy behaviors can spread amongst teens.

The first is a study by Ennett and Bauman (PDF) which examines smoking as a function of position in the social network.(1) Specifically, they name three classes of social network patterns: cliques (a small group of at least three adolescents whose primary friendships are with each other), liaisons (adolescents who maintain multiple friendships without being in a particular friendship clique), and isolates (adolescents who have relatively few friendships with others.) Past research showing that cliques tend to share smoking behaviors leads many people to the assumption that smoking is a primarily peer group phenomenon. However, after looking at data from 1,092 students collected across 5 schools over 1 year (from the start of 9th grade to the start of 10th grade), the authors found that smoking was far more common among isolates (17-40% across schools) than among clique members (4-16%). Additionally, within the 87 cliques identified, they found that smokers tended to associate in the same cliques, with the majority of cliques composed entirely or almost entirely of non-smokers. In looking at the roles played by influence and selection, they indicate that both processes contributed equally to similarity in smoking behaviors for clique members, though they do not discuss how they performed their analysis.

The next paper described a 2009 study by Mercken, et al. (PDF) which utilizes a “stochastic actor-based model” to help in separating the roles of influence and selection “by simultaneously representing changes in friendship network structure and changes in smoking behavior among adolescents.” (2) In this study, they interviewed 1326 subjects from 11 Finnish schools 4 times over the course of 30 months (starting at the beginning of 7th grade). Each time, they asked about their friendship ties, their smoking behavior, that of their families, and their alcohol consumption. They found that adolescents who smoked more had a tendency to choose friends who likewise scored high on smoking behavior. Adolescents who smoke less than one cigarette per week were most likely to make friends with classmates who don’t smoke at all, while the most attractive potential friends for those who smoke one or more cigarettes per week were those who smoked at the highest rate. The authors did not report findings regarding the data collected on alcohol consumption, which hints at the fact that the patterns of spread may be different for different behaviors.

A natural question at this point is: are all friends created equal? Most of us growing up had a “best” friend in addition to our peer group. A 1997 study by Urberg, et al. (PDF) attempts to parse out the influence of close friends vs. that of one’s peer group as it pertains to both cigarette smoking and alcohol use.(3) In this study, they collected data from 1,028 Mid-western school-children in the 6th, 8th, and 10th grades; data was collected in two waves, once in the Fall and once in the Spring, and included assessments of friendship ties as well as cigarette and alcohol use. Interestingly, they found that it was smoking behavior of the peer group and not the close friend which predicted a transition into cigarette use, while it was the drinking behavior of the close friend and not the peer group which predicted a transition into alcohol use. They also found that those who have tried cigarettes or alcohol are more likely to know current users than those who have not (echoing the Mercken findings above).

Finally, because I couldn’t close a post on social networks and health without a Christakis/Fowler study, I wanted to mention a study from March of this year (2010) from Mednick, Christakis, and Fowler (PDF), which examined the interaction of two separate behaviors–low sleep and drug use–within a social network. (4) Looking at a sample of 8,349 adolescents from the ADD Health Data Corpus, they presented a number of interesting findings. First, they found that an individual’s behavior is correlated with the behavior of others in their network up to 4 degrees away. In the case of sleep, an individual was 29% more likely to sleep 7 hours or less if they had a friend who sleeps less than 7 hours; a friend of a friend correlated with a 17% increase, all the way down to a 5% increase for the friend of a friend of a friend of a friend. In the case of marijuana use, a direct connection to a user resulted in a 190% increase in the likelihood of use, while a 4th-degree connection still correlated with a 11% increase in use. Another interesting finding was that individuals central in the network were more likely to sleep less, with a two standard-deviation increase in centrality increasing the probability of sleeping 7 hours or less by 13% (controlling for other factors). Finally, they report on the interrelation between these behaviors, claiming that having a friend who slept 7 hours or less actually correlated with a 19% increase in smoking marijuana.

These 4 papers served as a useful introduction to both the methods of social network analysis and some of the interesting findings as they pertain to health behaviors and teens (across a number of behaviors – sleep, smoking, drinking, drugs). It is important for educators and health professionals to have an understanding of the social mechanisms as these will likely be a critical factor in preventing unhealthy behaviors from spreading amongst teens and persisting in their lives. Perhaps these same mechanisms can be used to spread positive behaviors such as exercise and civic-mindedness. In addition, it will be interesting to see how methods like these can be applied on a larger scale to Twitter or Facebook-sized social network corpora to track the spread of behaviors, ideas, diseases, and more across entire states or countries.

References:

Ennett, S.T. and Bauman, K.E. (2000). Adolescent social networks: Friendship cliques, social isolates, and drug use risk. In Hansen, W.B., et al. (eds) Improving prevention effectiveness. Tanglewood Research, Inc. Greensboro, NC.
Mercken, L., et al. (2009). Dynamics of adolescent friendship networks and smoking behavior. Social Networks.
Urberg, K.A., Değirmencioğlu, S.M., and Pilgrim, C. (1997) Close Friend and Group Influence on Adolescent Cigarette Smoking and Alcohol Use. Developmental Psychology, vol 33(5), pp. 834-844.
Mednick, S.C., Christakis, N.A., Fowler J.H. (2010) The Spread of Sleep Loss Influences Drug Use in Adolescent Social Networks. PLoS ONE vol 5(3): e9775.

Also, I want to thank Sarita Yardi and Vladimir Barash for directing me towards some of these papers.