Transcript SOC 8311 Basic Social Statistics

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.
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?
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.
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)
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)
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, …
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 (19712003), 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)
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
Who Brings You Chicken Soup?
Social network diversity seems to reduce chances of catching
a common cold or influenza, probably by preventing stressreleased 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.”
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 emotionalsupport 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.