The Collective Dynamics of Smoking in a Large Social Network

Based on repeated surveys of 12,067 closely interconnected people between 1971 and 2000, examines the extent to which smoking spreads socially and to which groups of smokers quit together, as well as trends in the number and social centrality of smokers

Friendship and Natural Selection

More than any other species, humans form social ties to individuals who are neither kin nor mates, and these ties tend to be with similar people. Here, we show that this similarity extends to genotypes. Across the whole genome, friends' genotypes at the SNP level tend to be positively correlated (homophilic); however, certain genotypes are negatively correlated (heterophilic). A focused gene set analysis suggests that some of the overall correlation can be explained by specific systems; for example, an olfactory gene set is homophilic and an immune system gene set is heterophilic. Finally, homophilic genotypes exhibit significantly higher measures of positive selection, suggesting that, on average, they may yield a synergistic fitness advantage that has been helping to drive recent human evolution

The Collective Dynamics of Smoking in a Large Social Network

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.Sociolog

Estimating Peer Effects on Health in Social Networks

We recently showed that obesity can spread socially from person to person in adults (Christakis and Fowler 2007). A natural question to ask is whether or not these results generalize to a population of adolescents. Three separate teams of researchers have analyzed the National Longitudinal Study of Adolescent Health (Add Health) and shown evidence of person-to-peron spread of obesity, but they use different methods and disagree on the interpretation of their results. Here, we conduct our own analysis of the Add Health data, provide additional evidence from the Framingham Heart Study on the social spread of obesity, and use Monte Carlo simulations to test the econometric methods we use to model peer effects. The results show that the existence of peer effects in body mass is robust to several specifications in both adults and in adolescents.Sociolog

Dynamic Spread of Happiness in a Large Social Network: Longitudinal Analysis Over 20 Years in the Framingham Heart Study

Objectives: To evaluate whether happiness can spread from person to person and whether niches of happiness form within social networks. Design: Longitudinal social network analysis. Setting: Framingham Heart Study social network. Participants: 4739 individuals followed from 1983 to 2003. Main outcome measures: Happiness measured with validated four item scale; broad array of attributes of social networks and diverse social ties. Results: Clusters of happy and unhappy people are visible in the network, and the relationship between people’s happiness extends up to three degrees of separation (for example, to the friends of one’s friends’ friends). People who are surrounded by many happy people and those who are central in the network are more likely to become happy in the future. Longitudinal statistical models suggest that clusters of happiness result from the spread of happiness and not just a tendency for people to associate with similar individuals. A friend who lives within a mile (about 1.6 km)and who becomes happy increases the probability that a person is happy by 25% (95% confidence interval 1% to 57%). Similar effects are seen in coresident spouses (8%, 0.2% to 16%), siblings who live within a mile (14%, 1% to 28%), and next door neighbours (34%, 7% to 70%). Effects are not seen between coworkers. The effect decays with time and with geographical separation. Conclusions: People’s happiness depends on the happiness of others with whom they are connected. This provides further justification for seeing happiness, like health, as a collective phenomenon.Sociolog