George Kaplan, PhD

Scholar Project 

Complex Systems, Population Health and Health Disparities 

Dr. Kaplan studies why some communities are healthy and others are not. Building bridges between two fields—public health and complex systems science—he’s exploring how computer simulations may be used to uncover insight about the factors that shape population health. That insight, in turn, can help inform social policies and programs.

Dr. Kaplan’s decades-long work has been to develop an expanded view of factors that contribute to population health and health disparities – not only physiological, medical, and behavioral factors, but also socioeconomic, educational, cognitive, emotional/psychosocial, neighborhood-based, work-related, legal, etc.

Over time, he realized something crucial was missing, namely how the “whole” is generated from the “parts.” Focus on the atomistic has led ironically to massive accumulation of facts, with no idea how they fit together. Dr. Kaplan experienced a major “ah ha” moment in discovering the world of complex systems simulation as used in the fields of engineering, physical sciences, and computer sciences.

With support from TIIH and the National Institutes of Health, Dr. Kaplan is exploring the potential for computer simulations to provide meaningful, useful integration of a wide range of factors that contribute to population health. His exploration so far has led to the development of a unique, JAVA-based computer model that attempts to generate an artificial world that operates based on what we know about the dynamic relationships among the multiple determinants of health.

Dr. Kaplan finds that a strength of this model is that it allows us to represent a multi-level, dynamic etiologic theory, while also providing the opportunity via computer simulation to pose a series of counter-factuals–“what-if” queries that are of great interest from a policy point of view. This is of particular importance when we want to capture the “big picture” where many forces are shaping health, with lots of interactions between them, and where it would never be feasible to conduct comprehensive trials or experiments that represent the confluence of these forces.

In some recent work, the model was used to simulate the determinants of racial disparities in obesity, and the role of three neighborhood policies (increasing availability of good food stores, increasing physical activity infrastructure and walkability, and increasing school quality) on the reduction of black/white disparities in Body Mass Index (BMI). The simulation model, which is grounded in the best evidence, allowed him to simulate 125 different combinations of these policies and to observe their differential impact on disparities in obesity across generations. Each of these policies had some impact on BMI and BMI disparities, but it was the combination of them that was most effective. With some combinations of these policies it was possible to demonstrate 90 percent reductions in BMI disparities between black and whites.

While much more work needs to be done in the development and validation of this simulation model, this example shows how the use of such an empirically-informed computer simulation model, which embraces complexity rather than eschewing it, allows us to deal with the complex pathways that link neighborhood policies to health related factors and disparities in those factors, and illustrates the use of a new set of tools in understanding integrative health.