SOCIAL SUPPORT & HEALTH NETWORKS

1. SOCIAL SUPPORT & HEALTH NETWORKS The classical theorists of industrialization and modernization (Tönnies, Durkheim, Simmel) viewed urban residents as suffering from debilitating losses of community and intimacy compared to rural villagers. Emile Durkheim’s Suicide (1897) hypothesized that either high or low levels of integration (ties to social groups) and regulation (normative constraints) could lead to four types of self-murder: fatalistic, egoistic, anomic, altruistic. (Pescosolido & Levy 2002:8) In “The Metropolis and Mental Life” (1903), Georg Simmel argued that the modern city’s intense “nervous stimulation” produces a self that is rational, unemotional, blasé, alienated & autonomous. (“Stadtluft macht frei [und] krank” – City air makes you free .. and sick). Lacking traditional society’s constraints, urban dwellers form calculative & indifferent social relations, with their individualism reaping negative outcomes, such as loneliness and mental illness. These ideas persist in studies of how social networks affect physical and mental health/illness and coping strategies.

2. Mens Sana in Corpore Sano? Ill-structured interpersonal networks can influence development of physical disease, mental illness, substance abuse. In a vicious cycle, illnesses that disrupt ego’s support network can then lead to a downward spiral of job loss, isolation, homelessness, ... • Epidemiological studies reveal that many contagious diseases are not transmitted randomly, but by close contacts • Alcohol & drug addictions are sustained by peer enablers • Relapse & rehospitalization risk rises for severe mentally ill patients with unsupportive networks of families and friends (hostile, critical, emotionally overinvolved) Conversely, strong-tie support networks may help to inoculate people against negative outcomes, even catching the common cold! What public health policy implications of these researches?

3. Modeling Epidemics SIR model is a baseline epidemiological explanation of infectious disease transmission (= Susceptibility, Infection, Resistant /Recovered/ Removed [i.e., dead]). Three phases of an infectious epidemic: slow growth, explosion, burnout. Infamous examples: Bubonic Plague, 1918 Influenza, English foot-and-mouth, AIDS, Ebola, SARS, Swine Flu, … Using a random interaction assumption, classic SIR model’s parameters to explain epidemic patterns are pathogen (virus or bacteria) reproduction rate (R0) & relative sizes of infected & susceptible populations. But, modern transportation & social ties allow viruses to leap into new geographic and social territories: “On a small-world network, the key to explosive growth of a disease is the shortcuts” (Watts 2003:180). Because most contacts are very locally clustered, the infectives mainly interact with others who are already infected, preventing quick breakout into an epidemic. Only when shortcuts lead to fresh fields can random mixing processes generate explosive growth. Policy implication: find and block ties that connect diseased clusters to uninfected populations.

4. The Paradox of STDs Until recently, epidemiologists ignored how networks linked by sexual contact enable sexually transmitted diseases (STDs) to survive and spread. Infection rates usually too low to become epidemic, but higher rates in small core networks allow disease to remain endemic. Small behavioral changes may trigger rapid outbreak into the larger population. • The sexual networks in two small cities infected with chlamydia had similar sizes & structures: • Colorado Springs: 401 networks – size 2-12 – with 468 cases and 700 sexual contacts; the chlamydia infection rate increased by 46% from 1996 to 1999 • Winnipeg: 442 networks – size 2-20 – with 571 cases and 663 sexual contacts Most nets were dyads or triads, but a handful had more than 10 partners. “These smaller, sparsely linked networks, peripheral to the core, may form the mechanism by which chlamydia can remain endemic, in contrast with larger, more densely connected networks, closer to the core, which are associated with steep rises in incidence.”(Jolly et al. 2001)

5. Socially Cohesive STDs Colorado Springs’ chlamydia networks had little potential for epidemic propagation, in contrast to its gonorrhea network structure: Four largest chlamydia components Largest gonorrhea component (gang) “[O]verall network structure is fragmented and dendritic, notably lacking the cyclic (closed loops) structures associated with network cohesion and thus with efficient STD transmission. Comparison of network structure with that of an intense STD outbreak (characterised by numerous cyclic structures) suggests low level or declining endemic rather than epidemic chlamydia transmission during the study interval. … Finally, the gang associated STD outbreak … clearly demonstrates the relation between dense network connectivity and epidemicity. … [N]etwork cohesion seems strongly predictive of STD transmission intensity.” (Potterat et al. 2002:152 & 157)

6. All Stressed Out • The stress-buffering hypothesis asserts that social supports positively influences health and well-being by protecting people from the pathogenic effects of stressors (Cohen & Willis 1985; Wheaton 1985). • An alternative “main-effects hypothesis” claims that social supports positively influence health regardless of whether stressors occur. • Stressors & moderators factors may be personal or environmental: • Stressors include daily hassles (arguments, bad weather, unexpected change of plans) & major life events (death of friend or relative, serious illness or injury, divorce) • Moderators include support from family, friends, coworkers, classmates who offer advice, provide material aid, help overcome emotional distress, and share responsibilities Perceptions of support – beliefs & cognitions about the presence and quality of interpersonal ties – may be more crucial than the actual support received for reducing physical illnesses, psychological symptoms, and various maladaptive stress-behaviors, such as colds, ulcers, anger, anxiety, rage, depression, alcohol & drug & sexual abuse, delinquency, fighting, suicide, …

7. Do Your Friends Make You Fat? Nicholas Christakis studied whether the weight gains of an ego are associated with weight gains by ego’s friends, siblings, spouse, or neighbors. Obesity clusters extended to “three degrees of separation”! Among 12,067 egos in the Framingham Heart Study (1971-2003), 5,124 had friend ties to another. Largest friendship component was N=2,200 (see Pajek figure on next slide). Obesity was defined as a body-mass index (BMI) ≥ 30. Used time-lagged dependent variable to eliminate serial correlation of errors, control for genetic predispositions. “A person's chances of becoming obese increased by 57% 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%. If one spouse became obese, the likelihood that the other spouse would become obese increased by 37%. “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.” (Christakis & Fowler 2007)

8. Figure 1. Largest Connected Subcomponent of the Social Network in the Framingham Heart Study in the Year 2000 Social Network Image Animator (SoNIA) generated network videos (requires Macromedia Flash program to view) http://content.nejm.org/cgi/content/full/357/4/370/DC2

9. Who Brings You Chicken Soup? Social network diversity seems to reduce chances of catching a common cold or influenza, probably by preventing stress-released hormones that weaken immune processes, such as destroying the lymphocytes (white cells) that fight disease. Sheldon Cohen et al. (1998) gave 276 healthy volunteers nasal drops with common cold viruses, but only 40% got clinically ill. People with < four types of social ties caught colds 4 times more than those with ≥ six types. “Not only were they less susceptible to developing colds, they produced less mucus, were more effective in mucocilliary clearance of the nasal passage, and shed less virus.” The longer a stressful event’s duration, the greater the health risk. An argument with a spouse resolved in a few days has little effect. Marital discord lasting a month or more substantially increases the risk. “The type of stress also plays an important role in disease susceptibility. Job loss and divorce produced the most serious threat to the individual, whereas other less significant life challenges may not have the same impact.”

10. Formalizing Support Networks Can intentionally designed support networks – whether nonprofit or governmental – provide benefits to people with deficient ego-nets? • Disease-based support networks (e.g., CJD Support Network) try to help patients & families cope with stress, comply with difficult medical regimes • “12-step” self-help programs (e.g., Alcoholic Anonymous) deploy buddy systems to prevent relapses into self-destructive, anti-social behaviors • Caregivers themselves, especially women raising kids and caring for aged parents, may seek to alleviate their burdens & stresses by participating in emotional-support groups: • Parents without Partners; Elder Care Resources; Alzheimers Support Group

11. References Christakis, Nicholas A. and James H. Fowler. 2007. “The Spread of Obesity in a Large Social Network over 32 Years.” New England Journal of Medicine 357:370-79. <http://content.nejm.org/cgi/content/full/357/4/370> Cohen, Sheldon and Thomas A. Willis. 1985. “Stress, Social Support, and the Buffering Hypothesis.” Psychological Bulletin 98:310-357. Cohen, S., E. Frank, W.J. Doyle, D.P. Skoner, B.S. Rabin, and J.M. Gwaltney, Jr. 1998. “Types of Stressors that Increase Susceptibility to the Common Cold in Adults.” Health Psychology 17:214-223. Dukheim, Emile. 1897. Le Suicide. Paris: Alcan. Jolly A.M., S.Q. Muth, J.L. Wylie and J.J. Potterat JJ. 2001. “Sexual Networks and Sexually Transmitted Infections: A Tale of Two Cities.” Journal of Urban Health 78(3):433-445. Potterat J.J., S.Q. Muth, R.B. Rothenberg, H. Zimmerman-Rogers, D.L. Green, J.E. Taylor , M.S. Bonney, and H.A. White. 2002. “Sexual Network Structure as an Indicator of Epidemic Phase.” Sexually Transmitted Infections 78 Suppl 1:152-158. Simmel, Georg. 1903. “The Metropolis and Mental Life.” Pp. 409-424 in The Sociology of Georg Simmel, translated by Kurt Wolff. New York: Free Press. Watts, Duncan. 2003. Six Degrees: The Science of a Connected Age. New York: Norton. Wheaton, Blair. 1985. “Models for the Stress-Buffering Functions of Coping Resources.” Journal of Health and Social Behavior 26:352-365.