The Value of the Frame: Painting Complexity using Two Chronic Disease Models

Amber D. Elkins, Dennis M. Gorman, Jay E. Maddock, Hye-Chung Kum, Mark A. Lawley

Abstract


As with all chronic diseases, it is now recognized that type 2 diabetes is a complex health issue, the etiology of which involves numerous risk factors operating at different ecological levels of analysis. However, this ecological complexity of the problem seldom manifests itself in the interventions for preventing the problem, which typically focus on changing behavior through universal health education, with the assumption of a homogeneous population. This paper examines the limitations of this way of framing the problem of type 2 diabetes, particularly its failure to capture the way in which this problem emerges because of dynamic interactions between individuals and their environments and how these interactions vary in fundamental ways depending upon the context within which they occur. Specifically, the paper examines how framing of type 2 diabetes in the Lower Rio Grande Valley (LRGV) affects which systems modeling method selects to understand the problem and to help guide policy-makers to ameliorate it. Each systems model has a paradigm characterizing it by a set of fundamental rules and underlying concepts. That is, each method bases on assumptions of how the model should be constructed and the knowledge obtainable from such assumptions. By assuming the model should be constructed in a certain way, the modeler (whether implicitly or explicitly) frames the problem by making assumptions about the phenomenon-of-interest. Choosing to develop any model asserts that the model proscribes to paradigmatic assumptions for how it would contribute something of value) in some capacity (for a purpose), which is ultimately affected by understanding, interpretation, and application of the problem. The paper describes how specific types of systems methods, those using agent-based models (ABMs) and system dynamics models (SDMs), can produce very different ways of understanding the problem of, and the leverage points for, type 2 diabetes in the LRGV. Additionally, it moves beyond simply outlining the general differences in the use and applications of ABM and SDM, to presenting models demonstrating how framing of the problem and model paradigmatic assumptions affect understanding of the problem of type 2 diabetes in the LGRV and its potential leverage points. While the examples are specific to a health problem in a specific community, the significance of such an approach is in its generalizability to how understanding social system behavior depends upon how framing the problem and the paradigmatic assumptions of the modeling method affect our understanding of social systems and public health problems.


Keywords


complexity, public health, chronic disease, system dynamics, agent-based models

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