The collective dynamics of smoking in a large social network

Abstract

Background: The prevalence of smoking has decreased substantially in the United States over the past 30 years. We examined the extent of the person-to-person spread of smoking behavior and the extent to which groups of widely connected people quit together.

Methods: We studied a densely interconnected social network of 12,067 people assessed repeatedly from 1971 to 2003 as part of the Framingham Heart Study. We used network analytic methods and longitudinal statistical models.

Results: Discernible clusters of smokers and nonsmokers were present in the network, and the clusters extended to three degrees of separation. Despite the decrease in smoking in the overall population, the size of the clusters of smokers remained the same across time, suggesting that whole groups of people were quitting in concert. Smokers were also progressively found in the periphery of the social network. Smoking cessation by a spouse decreased a person's chances of smoking by 67% (95% confidence interval [CI], 59 to 73). Smoking cessation by a sibling decreased the chances by 25% (95% CI, 14 to 35). Smoking cessation by a friend decreased the chances by 36% (95% CI, 12 to 55 ). Among persons working in small firms, smoking cessation by a coworker decreased the chances by 34% (95% CI, 5 to 56). Friends with more education influenced one another more than those with less education. These effects were not seen among neighbors in the immediate geographic area.

Conclusions: Network phenomena appear to be relevant to smoking cessation. Smoking behavior spreads through close and distant social ties, groups of interconnected people stop smoking in concert, and smokers are increasingly marginalized socially. These findings have implications for clinical and public health interventions to reduce and prevent smoking.

Figure 1. Smoking in the Framingham Social Network

This is a random sample of 1000 subjects in the FHS social network chosen from the largest connected subcomponent at exam 1 (left) and exam 7 (right). Node border indicates gender (red=female, blue=male), node color indicates cigarette consumption (yellow is for ≥1 cigarettes per day), node size is proportional to number of cigarettes consumed, and arrow colors indicate relationship (friends and spouses = orange, family = purple). By 2000, it is apparent that smokers are more likely to occur at the periphery of their networks. And smokers are usually in smaller subgroups than nonsmokers. The circles in the panel for 2000 identify densely connected clusters of green circles where there are no smokers at all or where the smokers sit at the edge of the subgroup.

Figure 2. Effect of Social and Geographic Distance from Social Contacts Who Smoke on the Probability that a Subject is a Smoker in the Framingham Heart Study Social Network

(a) Mean effect of social proximity to a contact. This is derived by comparing the conditional probability of being a smoker in the observed network with an identical network (with topology preserved) in which the same number of persons who smoke are randomly distributed. Contact social distance refers to closest social distance between the contact (“alter”) and the subject (“ego”) (a direct contact = distance 1, contact’s contact = distance 2, etc.). Within any given social distance, the effect of the smoking behavior in a social contact upon subject’s smoking behavior increases across the exams from 1971 to 2003. (b) This figure shows the effects observed between directly connected persons (social distance 1) for six groups, ordered by distance between residences. The average distances in each group are as follows: group 1 = 0 miles, group 2 = 0.27 miles, group 3 = 1.46 miles, group 4 = 3.48 miles, group 5 = 9.37 miles, and group 6 = 471.9 miles. Error bars in both panels show 95% confidence intervals based on 1,000 simulations. Both panels exclude neighbor and co-worker ties.

Figure 3. Cluster Size and Centrality of Smokers Across Time

(a) Smokers remained in tightly-knit groups, even as the incidence of smoking sharply declined. Marginal smokers are not leaving smoking groups; instead, whole clusters are quitting and those that are not maintain their previous size. (b) Eigenvector centrality computed at each wave for smokers and non-smokers. While the centrality of non-smokers remains roughly stable across all waves, smokers become increasingly less central, and more peripheral, in the social network. Bars in both panels show 95% confidence intervals. In the left panel, confidence intervals are too small to see (the largest is slightly larger than the height of the dark squares). Both panels exclude neighbor and co-worker ties.

Figure 4. Association of Smoking Status Between Subjects and Their Social Contacts

The figure shows the probability that a subject (an “ego”) smokes given that their social contact (an “alter”) quits smoking, for generalized estimating equation logit models of smoking on several different sub-samples of the Framingham Heart Study Social Network. The dependent variable in each model is subject smoking and independent variables include lagged subject smoking status, contact smoking status, lagged contact smoking, subject age, gender, and education, and fixed effects for each wave. Full models and equations are available in the supplement. Mean effect sizes and 95% confidence intervals calculated by simulating first difference in contact contemporaneous smoking (changing from 1 to 0) using 1,000 randomly drawn sets of estimates from coefficient covariance matrix and assuming all other variables are held at their means. “Small firm coworkers” are those where six or fewer FHS participants work at the same physical location.