Person:

Christakis, Nicholas A.

Objectives. We assessed whether widowhood is associated with risk of diagnosis with a sexually transmitted infection (STI) among older adults in the United States and whether the associations observed in men differed before and after the introduction of sildenafil, the first oral erectile dysfunction medication approved by the Food and Drug Administration. Methods. We used Cox proportional hazards regression to analyze the time to first STI diagnosis in a random sample of married, Medicare-eligible couples aged 67 to 99 years in 1993 (N = 420 790 couples). Results. Twenty-one percent of male and 43% of female participants lost a spouse during the 9-year study period. Only 0.65% of men and 0.97% of women were diagnosed with an STI. Widowhood was associated with an increased risk of STI diagnosis for men only, with the largest effects found 0.5 to 1 year after a wife’s death. Effects for men were larger after the introduction of sildenafil. Conclusions. Widowhood in older men, but not women, increased the risk for STIs, especially in the postsildenafil era. Clinicians should address sexual health issues with older patients, especially bereaved men taking erectile dysfunction medications.

The impact of disease-causing defects is often not limited to the products of a mutated gene but, thanks to interactions between the molecular components, may also affect other cellular functions, resulting in potential comorbidity effects. By combining information on cellular interactions, disease--gene associations, and population-level disease patterns extracted from Medicare data, we find statistically significant correlations between the underlying structure of cellular networks and disease comorbidity patterns in the human population. Our results indicate that such a combination of population-level data and cellular network information could help build novel hypotheses about disease mechanisms.

The discrepancy between an individual’s loneliness and the number of connections in a social network is well documented, yet little is known about the placement of loneliness within, or the spread of loneliness through, social networks. We use network linkage data from the population-based Framingham Heart Study to trace the topography of loneliness in people’s social networks and the path through which loneliness spreads through these networks. Results indicated that loneliness occurs in clusters, extends up to three degrees of separation, is disproportionately represented at the periphery of social networks, and spreads through a contagious process. The spread of loneliness was found to be stronger than the spread of perceived social connections, stronger for friends than family members, and stronger for women than for men. The results advance our understanding of the broad social forces that drive loneliness and suggest that efforts to reduce loneliness in our society may benefit by aggressively targeting the people in the periphery to help repair their social networks and to create a protective barrier against loneliness that can keep the whole network from unraveling.

Social networks exhibit strikingly systematic patterns across a wide range of human contexts. While genetic variation accounts for a significant portion of the variation in many complex social behaviors, the heritability of egocentric social network attributes is unknown. Here we show that three of these attributes (in-degree, transitivity, and centrality) are heritable. We then develop a “mirror network” method to test extant network models and show that none account for observed genetic variation in human social networks. We propose an alternative “Attract and Introduce” model with two simple forms of heterogeneity that generates significant heritability as well as other important network features. We show that the model is well suited to real social networks in humans. These results suggest that natural selection may have played a role in the evolution of social networks. They also suggest that modeling intrinsic variation in network attributes may be important for understanding the way genes affect human behaviors and the way these behaviors spread from person to person.

While it is well known that the widowed suffer increased mortality risks, the mechanism of this survival disadvantage is still under investigation. In this article, we examine the quality of health care as a possible link between widowhood and mortality using a unique data set of 475,313 elderly couples who were followed up for up to nine years. We address whether the transition to widowhood affects the quality of care that individuals receive and explore the extent to which these changes mediate the elevated mortality hazard for the widowed. We analyze six established measures of quality of health care in a fi xed-effect framework to account for unobserved heterogeneity. Caregiving and acute bereavement during the transition to widowhood appear to distract individuals from taking care of their own health care needs in the short run. However, being widowed does not have long-term detrimental effects on individuals’ ability to sustain contact with the formal medical system. Moreover, the short-run disruption does not mediate the widowhood effect on mortality. Nevertheless, long after spousal death, men suffer from a decline in the quality of informal care, coordination between formal and informal care, and the ability to advocate and communicate in formal medical settings. These findings illustrate women’s centrality in the household production of health and identify important points of intervention in optimizing men’s adjustment to widowhood.

The use of networks to integrate different genetic, proteomic, and metabolic datasets has been proposed as a viable path toward elucidating the origins of specific diseases. Here we introduce a new phenotypic database summarizing correlations obtained from the disease history of more than 30 million patients in a Phenotypic Disease Network (PDN). We present evidence that the structure of the PDN is relevant to the understanding of illness progression by showing that (1) patients develop diseases close in the network to those they already have; (2) the progression of disease along the links of the network is different for patients of different genders and ethnicities; (3) patients diagnosed with diseases which are more highly connected in the PDN tend to die sooner than those affected by less connected diseases; and (4) diseases that tend to be preceded by others in the PDN tend to be more connected than diseases that precede other illnesses, and are associated with higher degrees of mortality. Our findings show that disease progression can be represented and studied using network methods, offering the potential to enhance our understanding of the origin and evolution of human diseases. The dataset introduced here, released concurrently with this publication, represents the largest relational phenotypic resource publicly available to the research community.

A field is emerging that leverages the capacity to collect and analyze data at a scale that may reveal patterns of individual and group behaviors.

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.

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.

Background: The prevalence of obesity has increased substantially over the past 30 years. We performed a quantitative analysis of the nature and extent of the person-to-person spread of obesity as a possible factor contributing to the obesity epidemic.

Methods: We evaluated a densely interconnected social network of 12,067 people assessed repeatedly from 1971 to 2003 as part of the Framingham Heart Study. The body-mass index was available for all subjects. We used longitudinal statistical models to examine whether weight gain in one person was associated with weight gain in his or her friends, siblings, spouse, and neighbors.

Results: Discernible clusters of obese persons (body-mass index [the weight in kilograms divided by the square of the height in meters], 30) were present in the network at all time points, and the clusters extended to three degrees of separation. These clusters did not appear to be solely attributable to the selective formation of social ties among obese persons. A person's chances of becoming obese increased by 57% (95% confidence interval [CI], 6 to 123) if he or she had a friend who became obese in a given interval. Among pairs of adult siblings, if one sibling became obese, the chance that the other would become obese increased by 40% (95% CI, 21 to 60). If one spouse became obese, the likelihood that the other spouse would become obese increased by 37% (95% CI, 7 to 73). These effects were not seen among neighbors in the immediate geographic location. Persons of the same sex had relatively greater influence on each other than those of the opposite sex. The spread of smoking cessation did not account for the spread of obesity in the network.

Conclusions: Network phenomena appear to be relevant to the biologic and behavioral trait of obesity, and obesity appears to spread through social ties. These findings have implications for clinical and public health interventions.