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Interview With George Kaplan---One Of Four Co-Editors Of A New Book on Complex Systems Approaches To Problems In Population Health

We learned recently about the publication of “Growing Inequality—Bridging Complex Systems, Population Health, and Health Disparities” co-edited by George Kaplan, Ana Diez-Roux, Carl Simon, and Sandro Galea. We read very positive reviews about the book and invited the University of Michigan’s George Kaplan to answer a few short questions to help readers better understand how the book came to be and its significance for epidemiologists. Below is the interview.

EM: Creating the network you describe was unique. What were some of the rewards for you as a participant/organizer?

Kaplan: Over many years – really many decades – I became more and more convinced that we as epidemiologists needed to broaden our perspectives in order to understand and improve population health for all. While the search for independent causes – the often elusive magic bullets – had worked in some cases, many of the problems that we were interested in were more characterized by tangled webs of factors where the search for a single cause was not realistic and often led to definitions of problems that bore little resemblance to the real world.

Being able to bring researchers who had experience in modeling and simulating complex systems together with those who knew something about the biological, behavioral, social, spatial, and institutional factors that produce disease outcomes in individuals and populations was a rare opportunity to push the envelope, to move from Occam’s quest for simplicity towards Einstein’s dictum that “everything should be made as simple as possible, but not simpler.”

My colleagues and I, assembled into the Network on Inequality, Complexity and Health, (NICH) hoped to demonstrate the feasibility of embracing the complexity of disease causation, rather than trying to eliminate it or control for it. That in itself would have been reward enough, but the response we got from other colleagues, particularly those in early stages of their careers, indicating a thirst to learn more about these approaches was the icing on the cake.

EM:  Did the interdisciplinary benefits that were anticipated or imagined really come to pass?  What were some of the challenges in working as a network or through this network process?

Kaplan: All together we involved around 50 colleagues in this journey, with 18 coalescing into the membership of NICH and meeting for almost five years. It was a very diverse group, representing perhaps well more than a dozen disciplines (epidemiology, neurosciences, computer science, economics, political science, public policy, mathematics, communications, nutrition, law, education, medicine, psychology, child development, etc.)..

While such a diverse group brings great strength, it does bring with it certain challenges. Many of the people from the health and social sciences had no or little previous experience with complex systems modeling, and several of the complex systems modelers in the network had little or no experience working on health disparities or population health topics. Thus, there was a continual process of providing background information in all three areas, and of learning from each other. While valuable, this was an iterative process that was time consuming but essential. While not fundamentally different from my experience in other interdisciplinary groups, there was the added challenge of becoming comfortable with the logic, process of developing, and understanding of the results of complex systems simulations.

In individual interviews with each NICH members, there was great enthusiasm for the network process. Typical were statements like,

“…[the diversity of the network] allowed me to see and work on connections that I wouldn’t have worked on otherwise,”

 “…exposure to new ideas was great, excellent people,”

“…has dramatically expanded the scope and reach of what I do…invaluable colleagues,”

“I have moved to a serious commitment to modeling health disparities and population health,”

“…increase[ed] my interest in life course issues,”

“…have added serious discussion of social determinants of health and health disparities issues to my teaching of non-public health students,”

“….changes the way that I look at things…enlarged my vision,” 

No member indicated in any way that they would have preferred less diversity in backgrounds and methods, and most commented on the benefits of such diversity to their thinking.

Finally, I would say that over time the network members better understood and became more comfortable with the use of in-silico/virtual/simulated worlds in which to examine counter factuals that certainly were not amenable to test in other ways.

EM:  Can you give a couple of examples of payoffs from the network process and payoffs regarding any of the specific topics investigated?

Kaplan: The biggest payoffs were probably those that had to do with our ability to stimulate network members and others into using complex systems approaches in epidemiology and public health, and to legitimize such efforts. There was dramatic increase in courses and programs centered on using complex systems analytic approaches in a number of departments and schools, and we attracted over 800 registrants, from 39 states and 19 countries, to a symposium at the NIH Natcher Center that was centered around NICH and its approach.

It also became clear that while there is the potential for considerable payoff, there are considerable educational, training, and resource needs in order to pursue the use of complex systems methods in epidemiology and other areas of public health.

There were any interesting  findings, with some mentioned below . They are initial forays – much work needs to be done. In fact, a rather small part of the funding for NICH supported these efforts. They are more proof of concept than finished work, as the purpose of the network was to demonstrate innovative approaches not to generate complete bodies of work. You will note that most of the approaches are considerably ‘upstream’ of most epidemiologic studies. That reflected both the composition of the NICH group, as well as a rapidly evolved consensus that complex systems approaches could be useful for approaching such ‘tangled’ problems.

a)       Orr et al. found that policies that modified the neighborhood educational, physical activity, and nutritional environment were found to dramatically reduce Black/White BMI disparities. But the effects took considerable time, and each policy had a different time course.

b)     Stange et al. found that access to primary care actually increased the effects of specialty care in treating specific diseases.

c)      Yang et al. found that for low SES populations, changing attitudes about walking was not sufficient to change behavior unless the environments were changed as well.

d)     Kreuger et al. showed that the factors that affect preventive dental care utilization differ according to SES – cost and access were the predominant factors for low SES patients, while issues of trust impacted more on high SES patients.

e)      Kassman and Klasik simulated the factors driving disparities in college enrollment and found that affirmative action based on SES was not as effective as race-based affirmative action in reducing disparities in enrollment.

f)       Simon et al. modeled the dynamics of the spread of crime and mass incarceration, and developed a kind of “flight simulator” that allowed them to examine differential effects of policies related to incarceration rates, recidivism etc.

g)  Wolfson and Beall, created a detailed simulation to help understand the different associations between income inequality
      and health in the US and Canada, and were able to assess onomic mobility than to the role of neighborhood racial and
      income segregation. Their initial results suggest that the different patterns in the US and Canada are more related to
      differences in intergenerational socioeconomic mobility than to neighborhood level factors.

h)   Kumanyika et al. simulated various factors that might account for differences in levels of physical activity between Black men and women. They found an important role for the supportive behavior of others in explaining the relatively lower levels of leisure-time activity in Black women.

i)    Boyce et al., found an important role for classroom structure and teacher behavior in the mental health consequences of classroom hierarchy.

Book information:

Sample chapter and TOC:


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