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Lessons from social network analyses for behavioral medicine
Abstract
This study presents an overview of the rapidly expanding field of social network analysis, with an emphasis placed on work relevant to behavioral health clinicians and researchers. I outline how social network analysis is a distinct empirical methodology within the social sciences that has the potential to deepen our understanding of how mental health and addiction are influenced by social environmental factors.
Whereas there have been a number of recent studies in the mental health literature that discuss social influences on mental illness and addiction, and a number of studies looking at how social networks influence health and behaviors, there are still relatively few studies that combine the two. Those that have suggest that mood symptoms as well as alcohol consumption are clustered within, and may travel along, social networks.
Social networks appear to have an important influence on a variety of mental health conditions. This avenue of research has the potential to influence both clinical practice and public policy.
... Social network analysis (SNA) is an analytic tool that allows for quantitatively characterizing associations between symptoms or pathology between members in social groups (Borgatti, Mehra, Brass, & Labianca, 2009;Burt, Kilduff, & Tasselli, 2013;Rosenquist, 2011;Wasserman & Faust, 1994). The use of SNA in a group in which the majority of individuals know one another, such as a sorority, presents an opportunity to understand the peer-to-peer influences on maladaptive eating behaviors and attitudes during the college years within a social environment that may represent a vulnerability factor for their spread. ...
... Sociocentric SNA is obtained by having each individual in a particular "closed" network rate his or her relationship to all other individuals in this same network. Thus, sociocentric SNA does not rely on a single source of data (i.e., a single ego) and allows for a more objective characterization of a social network from multiple perspectives (Borgatti et al., 2009;Burt et al., 2013;Rosenquist, 2011;Wasserman & Faust, 1994). ...
Objective:
Disordered eating attitudes and behaviors are prevalent among college women, and peers appear to influence current and future eating pathology. Social network analysis (SNA) is an innovative quantitative method to examine relationships (i.e., ties) among people based on their various attributes. In this study, the social network of one sorority was modeled using exponential random graph model (ERGM) to explore if homophily, or the tendency for relationship ties to exist based on shared attributes, was present according to sorority members' disordered eating behaviors/attitudes and their body mass index (BMI).
Method:
Participants included members of one sorority at a large Southeastern university. All members were included on a roster unless they elected to opt out during the consent process, and 41 (19%) of the members completed the study measures. Participants completed the Social Network Questionnaire developed for this study (degree of "liking" of every member on the roster), the Eating Disorder Examination-Questionnaire (EDE-Q), and a demographics questionnaire in exchange for 1 h of community service credit.
Results:
The final sample consisted of mostly White women with an average age of 20. Homophily across liking ties was examined based on the EDE-Q Global scale, episodes of binge eating, and BMI. The greater the difference in EDE-Q scores, the more likely the participants were to like one another. The greater the difference in BMI, the less likely the participants were to like one another. Binge eating was unrelated to homophily.
Discussion:
College sorority women appear to prefer other women with dissimilar levels of disordered eating attitudes, suggesting complex interactions between stigmatized or valued disordered eating attributes. Women with similar BMI were more likely to like one another, confirming past findings.
... For example, drinking for social enhancement features prominently in measures of expectancies and motives 159,163,164 and estimates of drinking in an individual's proximal social network are highly correlated with personal alcohol use 188 . Studies using social network analysis, which quantitatively characterizes the structure of relationships among people [189][190][191] , have revealed that drinkers cluster together in networks and social network characteristics predict changes in drinking over time [192][193][194] , with parallel findings for other addictive disorders [195][196][197][198] . Clinically, changes in an individual's social circle to include fewer people who drink alcohol predict recovery 199,200 , and salutary changes in social networks is a putative mechanism by which Alcoholics Anonymous (AA) confers benefits 201 . ...
Alcohol is one of the most widely consumed psychoactive drugs globally. Hazardous drinking, defined by quantity and frequency of consumption, is associated with acute and chronic morbidity. Alcohol use disorders (AUDs) are psychiatric syndromes characterized by impaired control over drinking and other symptoms. Contemporary aetiological perspectives on AUDs apply a biopsychosocial framework that emphasizes the interplay of genetics, neurobiology, psychology, and an individual's social and societal context. There is strong evidence that AUDs are genetically influenced, but with a complex polygenic architecture. Likewise, there is robust evidence for environmental influences, such as adverse childhood exposures and maladaptive developmental trajectories. Well-established biological and psychological determinants of AUDs include neuroadaptive changes following persistent use, differences in brain structure and function, and motivational determinants including overvaluation of alcohol reinforcement, acute effects of environmental triggers and stress, elevations in multiple facets of impulsivity, and lack of alternative reinforcers. Social factors include bidirectional roles of social networks and sociocultural influences, such as public health control strategies and social determinants of health. An array of evidence-based approaches for reducing alcohol harms are available, including screening, pharmacotherapies, psychological interventions and policy strategies, but are substantially underused. Priorities for the field include translating advances in basic biobehavioural research into novel clinical applications and, in turn, promoting widespread implementation of evidence-based clinical approaches in practice and health-care systems.
... Recently, the social network method has been frequently used in various fields of research. A search on SCOPUS using the keyword "social network analysis" revealed over 2000 articles using SNA; for example, in supply chain studies [18], film industry studies [19], and health and medicine industry studies [20], social network methods are widely used. ...
The social network is affecting the connection of TV-drama production companies, and traditional partnerships are changing. Many companies are moving from traditional connections to gridded social networks. The changing relational network makes the relationship between TV-drama production companies change from linear and static to network and dynamic, and from the original cooperation based on “Contract” to the node-type cooperation of “Switch.” Through empirical study, the paper found that the social network relationship in the TV series production industry has become an essential social capital because the construction of social networks will effectively promote the development of the industry under the new competition and make TV-drama industry production chain, creative resources, and value derived channel, resource boundary and cooperation mechanism, and market competition pattern change. Ultimately, it will reshape the subject cognition, path cognition, and power cognition in the relationship network.
... In drawing upon global experiences, Bennett and Jessani (2011) noted that KT often relies upon partnerships, collaborations, and personal contact between researchers and research users. Consistent with views from programme-based researchers in Kenya (Lairumbi et al, 2008) and analogous to other studies, homophilous personal contacts with policymakers (Henry, 2011;Rosenquist, 2011;McPherson et al, 2001), past successful collaborations, and a history of entrenched trust (Putnam, 1993;Bennett et al, 2012;Coleman, 1989;Gillies, 1998), were important hallmarks of the social capital amassed by respondents and contributed to their perceived success in influencing change. In fact, in Ghana, academic-policymaker networks 'are cited as the only functional communication channel between researchers and policymakers' (Burris et al, 2011). ...
This article examines the complex interactions and strategies for engagement – both existing as well as desired – between academic Knowledge Brokers (KBs) and national health policymakers in Kenya. Based on semi-structured interviews with academic KBs and university leaders from six Schools of Public Health (SPHs) as well as national policymakers, the authors found that a delicate balance between leveraging personal individual relationships and establishing more sustained institutional partnerships is important for engagement. The authors provide a list of recommended strategies for effective and tailored engagement, and highlight the important but under-appreciated dual role of academic KBs within Kenyan universities. keywords: Kenya • schools of public health • engagement strategy • evidence-informed decision making • knowledge brokering
... Strong epidemiological evidence suggests that individuals with diversified social networks who interact with family members, friends, neighbors and fellow workers, are married, or belong to social and religious groups, live longer and healthier lives than those who are less socially embedded and involved [1][2][3][4]. Social networks constitute the structural dimension of social relationships [1] and are typically appraised based on their dyadic characteristics (reciprocity, intensity/strength, formality and complexity) or physical features (size, homogeneity, geographic dispersion and density) [5,6]. ...
Background:
The beneficial influence of social networks on health and wellbeing is well-established. In poor urban settlements in Bangladesh, BRAC's Manoshi programme trains community health workers (CHWs) to support women through pregnancy, delivery and postpartum periods. This paper test the hypothesis that the introduction of CHWs as weak ties into the social networks of Manoshi members mediates improvements in maternal and neonatal health (MNH) best practices by providing support, facilitating ideational change, connecting mother to resources, and strengthening or countering the influence of strong ties.
Methods:
1000 women who had given birth in the last three months were identified and interviewed as part of ongoing monitoring of 5 poor urban settlements in Dhaka, Bangladesh. A social networks questionnaire was administered which elicited women's perceived networks around pregnancy, delivery and post-partum periods. Mediation analysis was performed to test the hypothesis that penetration of Manoshi CHWs into women's perceived networks has a beneficial effect on MNH best practises.
Results:
The presence and influence of Manoshi CHWs in women's networks significantly mediated the effect of Manoshi membership on MNH best practices. Respondents who were Manoshi members and who listed Manoshi CHWs as part of their support networks were significantly more likely to deliver with a trained birth attendant (OR 3.61; 95%CI 2.36-5.51), to use postnatal care (OR 3.09; 95%CI 1.83-5.22), and to give colostrum to their newborn (OR 7.51; 95%CI 3.51-16.05).
Conclusion:
Manoshi has succeeded in penetrating the perceived pregnancy, delivery and post-partum networks of poor urban women through the introduction of trained CHWs. Study findings demonstrate the benefits of moving beyond urban health care delivery models that concentrate on the provision of clinical services by medical providers, to an approach that nurtures the power of social networks as a means to support the poorest and most marginalized in changing behaviour and effectively accessing appropriate maternal services.
... Despite the scope of the problem, major gaps remain in understanding emerging adult alcohol misuse, especially regarding the role of social factors. Social relationships are highly heterogeneous across individuals but can be systematically quantifi ed using social network analysis (SNA) (Borgatti et al., 2009;McPherson et al., 2001;Rosenquist, 2011). This approach systematically characterizes the structure and both person-level and network-level characteristics of interrelationships among people (i.e., social networks). ...
Objective:
Alcohol misuse is substantially influenced by social factors, but systematic assessments of social network drinking are typically lengthy. The goal of the present study was to provide further validation of a brief measure of social network alcohol use, the Brief Alcohol Social Density Assessment (BASDA), in a sample of emerging adults. Specifically, the study sought to examine the BASDA's convergent, criterion, and incremental validity in relation to well-established measures of drinking motives and problematic drinking.
Method:
Participants were 354 undergraduates who were assessed using the BASDA, the Alcohol Use Disorders Identification Test (AUDIT), and the Drinking Motives Questionnaire.
Results:
Significant associations were observed between the BASDA index of alcohol-related social density and alcohol misuse, social motives, and conformity motives, supporting convergent validity. Criterion-related validity was supported by evidence that significantly greater alcohol involvement was present in the social networks of individuals scoring at or above an AUDIT score of 8, a validated criterion for hazardous drinking. Finally, the BASDA index was significantly associated with alcohol misuse above and beyond drinking motives in relation to AUDIT scores, supporting incremental validity.
Conclusions:
Taken together, these findings provide further support for the BASDA as an efficient measure of drinking in an individual's social network. Methodological considerations as well as recommendations for future investigations in this area are discussed.
... Recently, there has been an explosion of the use of SNA in a wide range of domains. A keyword search on Scopus conducted using "social network analysis" revealed 2,173 papers with SNA used in such areas as: health and medicine (Rosenquist, 2011), supply chains (Kim, Choi, Yan, & Dooley, 2011), movies (Park, Oh, & Jo, 2011), cattle movements (Aznar, Stevenson, Zarich, & León, 2011), fraud detection (Šubelj, Furlan, & Bajec, 2011), spam detection (DeBarr & Wechsler, 2010), etc. Our previous work in this area include the analysis of co-authorship networks of the Australasian Conference on Information Systems (Cheong & Corbitt, 2009a), Pacific Asia Conference on Information Systems (Cheong & Corbitt, 2009b), and evaluating student participation in virtual classrooms (Cheong & Corbitt, 2009c). ...
Using tweets extracted from Twitter during the Queensland 2010-2011 floods, social network analysis techniques were used to generate and analyse the online network that emerged at that time. The aim was to develop an understanding of the online community in order to identify active players and their effectiveness in disseminating critical information. A secondary goal was to identify important online resources disseminated by these communities. Important and effective players during the Queensland floods were found to be: local authorities (mainly the Queensland Police Services), political personalities (Queensland Premier, Prime Minister, Opposition Leader, Member of Parliament), social media volunteers, traditional media reporters, and people from not-for-profit, humanitarian, and community associations. A range of important resources were identified during the Queensland flood; however, they appeared to be of a more general information nature rather than vital information and updates on the disaster. Given the positive results obtained by the active involvement of the local authorities and government officials in Queensland, and the increasing adoption of Twitter in other parts of the world for emergency situations, it seems reasonable to push for greater adoption of Twitter from local and federal authorities Australia-wide during periods of mass emergencies.
... Recently, there has been an explosion of the use of SNA in a wide range of domains. A keyword search on Scopus conducted using "social network analysis" revealed 2,173 papers with SNA used in such areas as: health and medicine (Rosenquist, 2011), supply chains (Kim, Choi, Yan, & Dooley, 2011), movies (Park, Oh, & Jo, 2011), cattle movements (Aznar, Stevenson, Zarich, & León, 2011), fraud detection (Šubelj, Furlan, & Bajec, 2011), spam detection (DeBarr & Wechsler, 2010), etc. Our previous work in this area include the analysis of co-authorship networks of the Australasian Conference on Information Systems (Cheong & Corbitt, 2009a), Pacific Asia Conference on Information Systems (Cheong & Corbitt, 2009b), and evaluating student participation in virtual classrooms (Cheong & Corbitt, 2009c). ...
Using tweets extracted from Twitter during the Australian 2010-2011 floods, social network analysis techniques were used to generate and analyse the online networks that emerged at that time. The aim was to develop an understanding of the online communities for the Queensland, New South Wales and Victorian floods in order to identify active players and their effectiveness in disseminating critical information. A secondary goal was to identify important online resources disseminated by these communities. Important and effective players during the Queensland floods were found to be: local authorities (mainly the Queensland Police Services), political personalities (Queensland Premier, Prime Minister, Opposition Leader, Member of Parliament), social media volunteers, traditional media reporters, and people from not-for-profit, humanitarian, and community associations. A range of important resources were identified during the Queensland flood; however, they appeared to be of a more general information nature rather than vital information and updates on the disaster. Unlike Queensland, there was no evidence of Twitter activity from the part of local authorities and the government in the New South Wales and Victorian floods. Furthermore, the level of Twitter activity during the NSW floods was almost nil. Most of the active players during the NSW and Victorian floods were volunteers who were active during the Queensland floods. Given the positive results obtained by the active involvement of the local authorities and government officials in Queensland, and the increasing adoption of Twitter in other parts of the world for emergency situations, it seems reasonable to push for greater adoption of Twitter from local and federal authorities Australia-wide during periods of mass emergencies.
This chapter focuses on the design of and research about policy interventions that positively alter health behaviour. This includes healthcare policies as well as intersectoral policies that influence health. It begins with a broad examination of what constitutes health policy, the process of making policy and the pertinence of health policy to behavioural medicine. Among health policies, it considers financial influences, service models and workforce policies and policies addressing social determinants of health. Insofar as policy research in relation to behavioural health is a relatively new and evolving field, the chapter focuses on policy research methods. It examines the questions policy research asks including policy content and design, analysis/evaluation and policy process. It also describes important methodologies, including participatory action research, impact evaluation, comparative and historical analysis, literature reviews, case studies, experimental and quasi-experimental methods, varied quantitative approaches and integration of mixed quantitative and qualitative approaches. It goes on to consider challenges facing policy research in behavioural medicine, including development of suitable theories, methodologies, the nature and role of evidence in informing policy and involving community and individual users in research. It closes with consideration of non-communicable or chronic diseases and health in all policies as important topics for policy research for behavioural medicine.
Intraurban neighborhood environments shape mental and neurological health at each stage of life. From preconception to death, environmental exposures such as air and noise pollution, crumbling buildings and sidewalks, high crime rates, poverty, and social supports influence neurological and psychological development and affect individuals' social and occupational function. These environments often vary substantially between neighborhoods, leading to different experiences and health indicators for persons living in urban environments.
In this article, we use newly developed statistical methods to examine the generative processes that give rise to widespread patterns in friendship networks. The methods incorporate both traditional demographic measures on individuals (age, sex, and race) and network measures for structural processes operating on individual, dyadic, and triadic levels. We apply the methods to adolescent friendship networks in 59 U.S. schools from the National Longitudinal Survey of Adolescent Health (Add Health). We model friendship formation as a selection process constrained by individuals' sociality (propensity to make friends), selective mixing in dyads (friendships within race, grade, or sex categories are differentially likely relative to cross-category friendships), and closure in triads (a friend's friends are more likely to become friends), given local population composition. Blacks are generally the most cohesive racial category, although when whites are in the minority, they display stronger selective mixing than do blacks when blacks are in the minority. Hispanics exhibit disassortative selective mixing under certain circumstances; in other cases, they exhibit assortative mixing but lack the higher-order cohesion common in other groups. Grade levels are always highly cohesive, while females form triangles more than males. We conclude with a discussion of how network analysis may contribute to our understanding of sociodemographic structure and the processes that create it.
To evaluate whether happiness can spread from person to person and whether niches of happiness form within social networks.
Longitudinal social network analysis.
Framingham Heart Study social network.
4739 individuals followed from 1983 to 2003.
Happiness measured with validated four item scale; broad array of attributes of social networks and diverse social ties.
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.
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:
Alcohol consumption has important health-related consequences and numerous biological and social determinants.
Objective:
To explore quantitatively whether alcohol consumption behavior spreads from person to person in a large social network of friends, coworkers, siblings, spouses, and neighbors, followed for 32 years.
Design:
Longitudinal network cohort study.
Setting:
The Framingham Heart Study.
Participants:
12 067 persons assessed at several time points between 1971 and 2003.
Measurements:
Self-reported alcohol consumption (number of drinks per week on average over the past year and number of days drinking within the past week) and social network ties, measured at each time point.
Results:
Clusters of drinkers and abstainers were present in the network at all time points, and the clusters extended to 3 degrees of separation. These clusters were not only due to selective formation of social ties among drinkers but also seem to reflect interpersonal influence. Changes in the alcohol consumption behavior of a person's social network had a statistically significant effect on that person's subsequent alcohol consumption behavior. The behaviors of immediate neighbors and coworkers were not significantly associated with a person's drinking behavior, but the behavior of relatives and friends was.
Limitations:
A nonclinical measure of alcohol consumption was used. Also, it is unclear whether the effects on long-term health are positive or negative, because alcohol has been shown to be both harmful and protective. Finally, not all network ties were observed.
Conclusion:
Network phenomena seem to influence alcohol consumption behavior. This has implications for clinical and public health interventions and further supports group-level interventions to reduce problematic drinking.
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.
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.
Discernible clusters of obese persons (body-mass index [the weight in kilograms divided by the square of the height in meters], > or =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.
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.
People are interconnected, and so their health is interconnected. In recognition of this social fact, there has been growing conceptual and empirical attention over the past decade to the impact of social networks on health. This article reviews prominent findings from this literature. After drawing a distinction between social network studies and social support studies, we explore current research on dyadic and supradyadic network influences on health, highlighting findings from both egocen-tric and sociocentric analyses. We then discuss the policy implications of this body of work, as well as future research directions. We conclude that the existence of social networks means that people's health is inter-dependent and that health and health care can transcend the individual in ways that patients, doctors, policy makers, and researchers should care about.
Alcohol consumption has important health-related consequences and numerous biological and social determinants.
To explore quantitatively whether alcohol consumption behavior spreads from person to person in a large social network of friends, coworkers, siblings, spouses, and neighbors, followed for 32 years.
Longitudinal network cohort study.
The Framingham Heart Study.
12 067 persons assessed at several time points between 1971 and 2003.
Self-reported alcohol consumption (number of drinks per week on average over the past year and number of days drinking within the past week) and social network ties, measured at each time point.
Clusters of drinkers and abstainers were present in the network at all time points, and the clusters extended to 3 degrees of separation. These clusters were not only due to selective formation of social ties among drinkers but also seem to reflect interpersonal influence. Changes in the alcohol consumption behavior of a person's social network had a statistically significant effect on that person's subsequent alcohol consumption behavior. The behaviors of immediate neighbors and coworkers were not significantly associated with a person's drinking behavior, but the behavior of relatives and friends was.
A nonclinical measure of alcohol consumption was used. Also, it is unclear whether the effects on long-term health are positive or negative, because alcohol has been shown to be both harmful and protective. Finally, not all network ties were observed.
Network phenomena seem to influence alcohol consumption behavior. This has implications for clinical and public health interventions and further supports group-level interventions to reduce problematic drinking.
In July of 2007, we published a paper in the New England Journal of Medicine that used dynamic data over 32 years from the Framingham Heart Study Social Network (FHS-Net) to study the conditions under which obesity might spread from person to person (Christakis and Fowler 2007, hereafter, CF). We found that obese persons formed clusters in the network at all time points and that these clusters extended to three degrees of separation (e.g., to a person’s friend’s friend’s friend). Moreover, statistical analyses suggested that the clusters were not solely attributable to the selective formation of social ties among obese persons. A person’s chances of becoming obese increased if he or she had a friend who became obese in a given time period.
Our analyses were restricted to adults, so a natural question to ask is whether or not the results would generalize to a population of adolescents. The existence of social norms regarding weight in both adults and adolescents should not be surprising,(Chang and Christakis 2003) but, of course, causal inference in dyads, let alone in broader social networks, is difficult (Manski 1993). These questions are addressed by two papers in this issue, “Is Obesity Contagious? Social Networks versus Environmental Factors in the Obesity Epidemic,” by Ethan Cohen-Cole and Jason Fletcher (hereafter CCF) and “Peer Effects in Adolescent Overweight,” by Justin Trogdon, James Nonnemaker, and Joanne Pais (hereafter TNP), and by a third working paper “Identifying Endogenous Peer Effects in the Spread of Obesity” by Timothy J. Halliday and Sally Kwak (hereafter HK). All three of these papers analyze the same dataset and population, the National Longitudinal Study of Adolescent Health (Add Health), albeit with different methods and assumptions. Unlike the FHS-Net, which followed adults over a 32-year period (average age 38 in 1971), AddHealth followed adolescents over a 7-year period (average age 16 in 1995).
All three sets of authors take the possibility of peer effects seriously, advancing the study of this important area. HK summarize their results by noting that they are able to replicate the pattern of results in our study, although their results are sensitive to specification of the dependent variable. If weight is characterized by a dichotomous variable indicating overweight (BMI>25), then an association between friends is significant, but the association in the continuous measure of BMI is not. Similarly, TNP use a variety of econometric strategies to conclude that there are peer effects for obesity in the Add Health sample, especially among females and among adolescents with high BMI. For example, they use an instrumental variables approach and a variety of definitions of endogenous peer groups to control for contextual effects. Here, we particularly address the CCF paper since it is the only one of the three papers that claims to reject the hypothesis that weight status can spread from person to person. To their credit, CCF exploit longitudinal data in a way that TNP and HK do not, but there are important problems in both their analysis and their interpretation of the results.
It is widely recognized that social relationships and affiliation have powerful effects on physical and mental health. When investigators write about the impact of social relationships on health, many terms are used loosely and interchangeably including social networks, social ties and social integration. The aim of this paper is to clarify these terms using a single framework. We discuss: (1) theoretical orientations from diverse disciplines which we believe are fundamental to advancing research in this area; (2) a set of definitions accompanied by major assessment tools; and (3) an overarching model which integrates multilevel phenomena. Theoretical orientations that we draw upon were developed by Durkheim whose work on social integration and suicide are seminal and John Bowlby, a psychiatrist who developed attachment theory in relation to child development and contemporary social network theorists. We present a conceptual model of how social networks impact health. We envision a cascading causal process beginning with the macro-social to psychobiological processes that are dynamically linked together to form the processes by which social integration effects health. We start by embedding social networks in a larger social and cultural context in which upstream forces are seen to condition network structure. Serious consideration of the larger macro-social context in which networks form and are sustained has been lacking in all but a small number of studies and is almost completely absent in studies of social network influences on health. We then move downstream to understand the influences network structure and function have on social and interpersonal behavior. We argue that networks operate at the behavioral level through four primary pathways: (1) provision of social support; (2) social influence; (3) on social engagement and attachment; and (4) access to resources and material goods.
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.
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.
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.
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.
This paper examines the reflection problem that arises when a researcher observing the distribution of behaviour in a population
tries to infer whether the average behaviour in some group influences the behaviour of the individuals that comprise the group.
It is found that inference is not possible unless the researcher has prior information specifying the compisition of reference
groups. If this information is available, the prospects for inference depend critically on the population relationship between
the variables defining reference groups and those directly affecting outcomes. Inference is difficult to implossible if these
variables are functionally dependent or are statistically independent. The prospects are better if the variables defining
reference groups and those directly affecting outcomes are moderately related in the population.
Inspired by empirical studies of networked systems such as the Internet,
social networks, and biological networks, researchers have in recent years
developed a variety of techniques and models to help us understand or predict
the behavior of these systems. Here we review developments in this field,
including such concepts as the small-world effect, degree distributions,
clustering, network correlations, random graph models, models of network growth
and preferential attachment, and dynamical processes taking place on networks.
Complex networks describe a wide range of systems in nature and society, much quoted examples including the cell, a network of chemicals linked by chemical reactions, or the Internet, a network of routers and computers connected by physical links. While traditionally these systems were modeled as random graphs, it is increasingly recognized that the topology and evolution of real networks is governed by robust organizing principles. Here we review the recent advances in the field of complex networks, focusing on the statistical mechanics of network topology and dynamics. After reviewing the empirical data that motivated the recent interest in networks, we discuss the main models and analytical tools, covering random graphs, small-world and scale-free networks, as well as the interplay between topology and the network's robustness against failures and attacks. Comment: 54 pages, submitted to Reviews of Modern Physics