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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.
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.
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)
These ideas persist in studies of how social networks affect
physical and mental health/illness and coping strategies.
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?
The Paradox of STDs
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
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.
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)
Socially Cohesive STDs
Colorado Springs’ chlamydia networks had little potential for epidemic
propagation, in contrast to its gonorrhea network structure:
“[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)
Four largest chlamydia components Largest gonorrhea component (gang)
Do Your Friends Make You Fat?
“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)
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.
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”!
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/DC
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
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.
