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

Social Networks and Collateral Health Effects

Since a patient or a clinical trial participant is connected to other people through social network ties, medical interventions delivered to a patient, quite apart from their health effects in that person, may have unintended health effects in others to whom he is connected. The cumulative impact of an intervention is therefore the sum of the direct health outcomes in the patient plus the collateral health outcomes in others (figure). These effects, in both the patient and in their social contacts, might be positive or negative. Doctors, trialists, patients, or policy makers might see reason to take them into account when choosing treatment or evaluating benefit.Sociolog

Extent and Determinants of Error in Doctors' Prognoses in Terminally Ill Patients: Prospective Cohort Study

Objective: To describe doctors' prognostic accuracy in terminally ill patients and to evaluate the determinants of that accuracy. Design: Prospective cohort study. Setting: Five outpatient hospice programmes in Chicago. Participants: 343 doctors provided survival estimates for 468 terminally ill patients at the time of hospice referral. Main outcome measures: Patients' estimated and actual survival. Results: Median survival was 24 days. Only 20% (92/468) of predictions were accurate (within 33% of actual survival); 63% (295/468) were overoptimistic and 17% (81/468) were overpessimistic. Overall, doctors overestimated survival by a factor of 5.3. Few patient or doctor characteristics were associated with prognostic accuracy. Male patients were 58% less likely to have overpessimistic predictions. Non-oncology medical specialists were 326% more likely than general internists to make overpessimistic predictions. Doctors in the upper quartile of practice experience were the most accurate. As duration of doctor-patient relationship increased and time since last contact decreased, prognostic accuracy decreased. Conclusion: Doctors are inaccurate in their prognoses for terminally ill patients and the error is systematically optimistic. The inaccuracy is, in general, not restricted to certain kinds of doctors or patients. These phenomena may be adversely affecting the quality of care given to patients near the end of life.Sociolog

An empirical model for strategic network foundation

We develop and analyze a tractable empirical model for strategic network formation that can be estimated with data from a single network at a single point in time. We model the network formation as a sequential process where in each period a single randomly selected pair of agents has the opportunity to form a link. Conditional on such an opportunity, a link will be formed if both agents view the link as beneficial to them. They base their decision on their own characateristics, the characteristics of the potential partner, and on features of the current state of the network, such as whether the the two potential partners already have friends in common. A key assumption is that agents do not take into account possible future changes to the network. This assumption avoids complications with the presence of multiple equilibria, and also greatly simplifies the computational burden of anlyzing these models. We use Bayesian markov-chain-monte-carlo methods to obtain draws from the posterior distribution of interest. We apply our methods to a social network of 669 high school students, with, on average, 4.6 friends. We then use the model to evaluate the effect of an alternative assignment to classes on the topology of the network.

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

Spreading in Social Systems: Reflections

In this final chapter, we consider the state-of-the-art for spreading in social systems and discuss the future of the field. As part of this reflection, we identify a set of key challenges ahead. The challenges include the following questions: how can we improve the quality, quantity, extent, and accessibility of datasets? How can we extract more information from limited datasets? How can we take individual cognition and decision making processes into account? How can we incorporate other complexity of the real contagion processes? Finally, how can we translate research into positive real-world impact? In the following, we provide more context for each of these open questions.Comment: 7 pages, chapter to appear in "Spreading Dynamics in Social Systems"; Eds. Sune Lehmann and Yong-Yeol Ahn, Springer Natur

Mortality After the Hospitalization of a Spouse

Background: The illness of a spouse can affect the health of a caregiving partner. We examined the association between the hospitalization of a spouse and a partner’s risk of death among elderly people. Methods: We studied 518,240 couples who were enrolled in Medicare in 1993. We used Cox regression analysis and fixed-effects (case–time–control) methods to assess hospitalizations and deaths during nine years of follow-up. Results: Overall, 383,480 husbands (74 percent) and 347,269 wives (67 percent) were hospitalized at least once, and 252,557 husbands (49 percent) and 156,004 wives (30 percent) died. Mortality after the hospitalization of a spouse varied according to the spouse’s diagnosis. Among men, 6.4 percent died within a year after a spouse’s hospitalization for colon cancer, 6.9 percent after a spouse’s hospitalization for stroke, 7.5 percent after a spouse’s hospitalization for psychiatric disease, and 8.6 percent after a spouse’s hospitalization for dementia. Among women, 3.0 percent died within a year after a spouse’s hospitalization for colon cancer, 3.7 percent after a spouse’s hospitalization for stroke, 5.7 percent after a spouse’s hospitalization for psychiatric disease, and 5.0 percent after a spouse’s hospitalization for dementia. After adjustment for measured covariates, the risk of death for men was not significantly higher after a spouse’s hospitalization for colon cancer (hazard ratio, 1.02; 95 percent confidence interval, 0.95 to 1.09) but was higher after hospitalization for stroke (hazard ratio, 1.06; 95 percent confidence interval, 1.03 to 1.09), congestive heart failure (hazard ratio, 1.12; 95 percent confidence interval, 1.07 to 1.16), hip fracture (hazard ratio, 1.15; 95 percent confidence interval, 1.11 to 1.18), psychiatric disease (hazard ratio, 1.19; 95 percent confidence interval, 1.12 to 1.26), or dementia (hazard ratio, 1.22; 95 percent confidence interval, 1.12 to 1.32). For women, the various risks of death after a spouse’s hospitalization were similar. Overall, for men, the risk of death associated with a spouse’s hospitalization was 22 percent of that associated with a spouse’s death (95 percent confidence interval, 17 to 27 percent); for women, the risk was 16 percent of that associated with death (95 percent confidence interval, 8 to 24 percent). Conclusions: Among elderly people hospitalization of a spouse is associated with an increased risk of death, and the effect of the illness of a spouse varies among diagnoses. Such interpersonal health effects have clinical and policy implications for the care of patients and their families.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

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