Interview with Dr. Paik

What is your educational background?
I have a BA in Public Policy, and an MA and PHD in Sociology.  All of my
degrees are from the University of Chicago, but I did spend four years
working in Washington, DC after college.


How did you get into sociology?
While working in DC, I became fascinated with cooperative relationships
and conflict in organizations.  Indeed, I was living through my share of
both.


How and when did you get started in network research?
Network analysis was a natural way to understand the organization of
work, so I immediately started taking classes in the area.  I took classes
on networks from Ron Burt, John Padgett, Roger Gould, and my advisor Edward
Laumann.  Ed was the person who introduced me to the concept of "sexual
networks." 


What do you think are the biggest benefits of network research? The biggest drawbacks?
The biggest benefit of network analysis is that it emphasizes how social
relationships take concrete forms (i.e., social structure).  This allows
researchers to specify pathways of influence and diffusion.  The biggest
drawback, however, is that network analysis is incredibly data intensive.
As such, we are often limited by the amount of data that we can collect and
by significant amounts of missing data.


What is the most challenging part of researching networks?
The most challenging part of this field is keeping up with it.  Since it
is interdisciplinary in nature, it is extremely fast moving and technical.


You have done some network research into health issues, do you think network research hold promise for public health?
 I do think that combining network analysis with the study of public
health is an excellent opportunity.  There have been several high profiles
articles and texts (e.g., Tom Valente's new textbook) that have highlighted
the natural fit between public health and network analysis.


What do you think sociology can offer to the field of public health?
I would say that network analysis is not just a sociological venture.
Its origins are in sociology and anthropology, but today's researchers come
from many fields, including business, public health, economics, and computer
science.  Sociology, however, can offer ways to think about network
structures.


Thank you Dr. Paik for answering my questions!


~L

Social Networks and Health (Anthony Paik)

I should probably start this with a disclaimer—this is not intended to be an in-depth description of the use of social networks for research. It is simply my rehashing of a departmental seminar that has been filtered through my statistics-challenged brain (I finished all my college math in high school and did my undergrad studies in music so it has literally been years since I studied math with any intensity)

First of all, why do we even want to study social networks? Networks can give different views of a subject than standard data alone can. Networks allow us to see how structure matters, separate metaphors and concrete differences, attributes vs. relations, study how an individual's choices constrained by the social structure they reside in, and compare micro and macro views of the same data.

Visualizing Network Data

Network data can be viewed as both a graph and matrix. Graphs are made by plotting nodes and denoting connections using lines. In one type of graph, known as spring and bedding, nodes are pulled closer by the presence of ties. A matrix corresponds to a graph and numerically describes connections between nodes. (See my extremely simplified, rather rough examples of a graph and matrix below)

	N1	N2	N3	Etc…
N1	-	1	0
N2	0	-	1
N3	1	1	-
Etc…

            1=connection between nodes

            0=no connection

 

After creating visualizations of the data we can then describe and analyze it. The different ways of describing are by composition and structure. Composition would focus on the alters (or nodes) that make up the bedding of the data. Structure can be looked at different ways. By density (less dense areas vs. more dense areas), modal degree, distance between nodes, similarities or patterns of ties.

 

Analyzing Network Data

There are three basic types of data analysis

    Dyadic analysis involves gathering information from a pair of alters

    Egocentric analysis is the collection of data around a single node

    Complete analysis looks at all the information from everyone

The tools that can be used to assist in these types of analysis are visualizations, cultures and subgroups, the network as a dependent variable (this looks at the probability of tie formation in the network), network as the independent variable (this looks as contagion and influence including the flow of information, power/authority, cohesion/solidarity, and competition/comparison)

Networks, like any research, are not perfect. One common issue is the homophily or selection problem. The question with this problem is "did the friendship form on the basis of the variable being studied?" There is a tendency for people with same characteristics to become friends so this is a very real problem. Another issue is confounds—are the results related to a larger contet (e.g. environment) that is shared by both alters?

 

Health Implications

This is where network research appeals to public health practitioners. And I thought this portion of the lecture helped me understand the previous intro to networks. Following is a brief review of actual research using each of the three basic types of network analysis.

Dr. Paik described his research utilizing a longitudinal study of adolescent health (ADO Health)—Wave 1 occurred in 1994-95, with subsequent waves in 96, 01, 04

Examples of the different types of data collection used:

    Dyadic data—interpersonal violence

    Egocentric—sexual concurrency, chlamydia infections

    Complete—peer effects of nonromantic sex

Dyadic data collection to evaluate interpersonal violence:

The independent variable was the partner's prior violence from Wave I. The dependent variable was the victimization of the alter in Wave II. A positive correlation was seen, which essentially means that a partner with a history of violence is more likely to continue to be violent.

Egocentric data collection to determine the connection between sexual concurrency and chlamydia infections:

The dependent variable here is a chlamydia infection. The independent variable was the presence of concurrent (multiple) sexual partners. A positive correlation was seen between these variables. As many people would assume, more prior partners increased infections, but having concurrent sexual partners had an even larger increase in infections.

Using the network to find patterns regarding nonromantic sex and delinquency:

The dependent variable in this example is having nonromantic sex during Wave I (or "hooking up" in laymen's terms). Two different analyses occurred using the mean of friends' delinquency as the first independent variable and the network centrality of the respondent weighted by the centrality of his/her friends as the second independent variable.

As one might assume: the respondents' delinquency had a positive effect on the occurrence of hook-ups as did the mean of the friends' delinquency. Simply looking at the friends' centrality showed no effect, but weighting centrality with delinquency showed two differences: having delinquent and central friends increased the odds that a respondent would hook-up but having delinquent, non-central friends actually decreased those odds.

Further analysis?

    Are hook-ups contagious as the data seems to suggest?

To look at this they looked at the ties found between those who have never hooked up, ties found between those who had hooked up and those who had not hooked up, and the ties found between people who had had hookups.

    Hook-ups did appear to be "contagious". These findings can then be put to use from a public health perspective.

 

Admittedly, this overview of social networks is extremely simplified and possibly just plain wrong, but if it has piqued your interest in networks Dr. Paik suggests a few books that are helpful for learning more.

    Networks and Health by Tom Valente

    Networks an introduction by Mark Newman

    Networks, Crowds, Markets by Easley/Kleinberg

 

 

Coming up—a brief interview with Dr. Paik

 

~L

Dr. Ed Wagner

Last week I attended a lecture that was part of the "Prevention and Chronic Care Management" conference hosted by the College of Public Health. Dr. Ed Wagner was awarded the Richard and Barbara Hanson Leadership Award and Distinguished Lectureship (I think the only thing academics love more than acronyms is really loooong names). I would have loved to attend the full conference, but other duties called my name. His lecture was titled "Primary Care and the Future of American Medicine." The title itself was compelling enough to rouse me from my bed before 9 a.m. (a monumental feat). But wake I did and I am very happy for it.

Dr. Wagner’s speech was titled “Primary Care and the Future of American Medical Care.” He began his speech by looking at the current state of primary care. For the purposes of this speech family medicine, internal medicine, and pediatrics were the specialities considered to be “primary care” (some sources also define psychiatry as a primary care field). From 1999-2009 the percentage of medical students choosing family medicine dropped from 15% to approximately 7%. Internal medicine fared even worse with a drop from 13% to 2%. In addition to fewer students entering these specialities, more current practitioners are leaving.

Why does the decline of primary care even matter? For several reasons: countries with better primary care have better health outcomes and lower costs, U.S. states with higher primary care/population rations have reduced costs and better quality. A survey asked if it was important to have “one practice/clinic where doctors and nurses know you, provide and coordinate the care that you need.” 95% agreed that it was important, 85% of those “strongly agreed” with the statement. The decline of primary care physicians per capita has also led to significant declines in satisfaction with the multiple areas of primary care, such as doctor-patient communication, interpersonal treatment, thoroughness of physical exam, visit-based continuity, and care integration.

What is the reason for this decline? One is that as the population grows older and sicker, PCPs are responsible for more content, increased demand and increased complexity. Other reasons are declining income and working harder to just keep up (“hamster wheel”).

Dr. Wagner's has done extensive work in chronic care and this is where the rest of the talk headed. He noted the increase in chronic conditions and how chronic conditions exponentially increase health care costs. One slide showed chronic conditions of medicare beneficiaries: 65% of beneficiaries had two or more chronic conditions and incurred 95% of expenditures! Chronic conditions have always been complex, but with new medications (which are often used in combination with each other) and ever-changing guidelines, the complexity has become even greater. This has affected the ability of the patient to manage his/her own care as well as resulted in a decreased ability of providers to support and educate patients regarding their care. What is the result of all this? "Patients with major chronic illnesses receive recommended care about 1/2 the time."

So, we need a way to combat declining primary care physicians, declining outcomes, and increased costs. The current buzzword is the medical home. The medical home initially grew from the American Academy of Pediatric's pediatric medical home. When combined with the chronic care model, one comes up with the patient-centered medical home (PCMH). The PCMH is a topic too large to discuss in this post, but the central idea is that each patient has a primary-care provider that is their first point of contact for health care and is the person that each subsequent encounter (PCP visit, specialist referral, hospital admission) refers back to. 

The PCMH has been shown to be effective in improving outcomes, but it still faces roadblocks to widespread implementation. Many providers are lacking the IT and infrastructure that is essential to a medical home and the current payment system doesn't reimburse for coordinating functions.

I highly suggest that everyone reads more about the patient-centered medical home as well as the chronic care model.