analysis (SNA). System dynamics models differ from ABM and MSM by aggregating factors and their interactions within endogenous systems to better understand high‐level phenomena such as the impacts of interventions and policies and their unintended consequences (Homer and Hirsch 2006). SNA studies the relationships between actors and entities—be they individuals, organizations, or countries. Like ABM, SNA can be useful in telescoping between the micro (individual) and the macroscales of analysis; yet unlike ABM, SNA does not always include dynamic simulation nor account for adaptation. Some forms of SNA overlap with ABM. SNA is widely used for understanding the transmission of infectious diseases such as HIV/AIDS and influenza, and the contagion of behaviors such as obesity and depression, since each of these can be transmitted socially (Christakis and Fowler 2007).
2.3 Comparison of Agent‐Based and Microsimulation Models
Figure 2.1 illustrates some key differences between ABM, MSM, and statistical models (e.g. regression) as commonly used in population health. Unlike traditional models which draw on existing observational data, system science approaches such as ABM and MSM conduct ex ante assessments—for example, to consider the potential effects of policy interventions for which no data yet exist (and which therefore cannot easily be addressed by linear regression). In doing so, they leverage an ability to account for dynamic histories of individual agents, thereby incorporating changes in exposures over time, and to account for heterogeneous actors and behavioral responses. Behavioral responses include changes in the behaviors of agents in response to a new economic policy (e.g. tax policy) that imposes changes in individuals' budget constraints. Microsimulation is particularly well suited for studying the impacts of economic policies, including tax and welfare policies. Meanwhile, neither ABM nor MSM are specifically designed to enhance causal inference—such as by removing endogeneity—unlike advanced epidemiologic methods such as marginal structural regression and inverse probability weighting approaches that have been developed in recent years (Hernan and Robins 2010).
Figure 2.1 Key differences between agent‐based modeling, microsimulation modeling, and traditional statistical models.
An important distinction between ABM and MSM as commonly used in population health and social science is that MSM generally do not include any characterization of social interactions between individuals (except indirectly via a social‐level variable). By contrast, ABM models are generally focused on such interactions. Hence, MSM might be best suited for, say, consideration of tax policy, whereas ABM might be better suited for studying contagion of infectious disease.
2.4 Why ABM and MSM are Useful for Studying the Social Determinants of Health
In both of the fields of social epidemiology and social policy, understanding the nature of these relationships (such as the effect of a particular social determinant on health) using traditional models is greatly limited by the lack of consideration of the complexity of systems. In order to delineate the true effects of the social determinants of health within the complex systems of entire societies—characterized by multiple agents, nonlinearities, and complex feedback loops—novel modeling and simulation tools such as ABM and MSM are often required. For example, simulation studies can model the intergenerational transmission of socioeconomic disadvantage, an inquiry that is impractical in more traditional studies. Importantly, systems science approaches such as ABM and MSM can enable exploration of the possible impacts of policy options before actually implementing them (Maglio and Mabry 2011), which can avoid the ethical and feasibility issues that can arise from implementing interventions in real life. For example, in the review of the evidence‐based interventions for the social determinants of health by Bambra et al. (2010) described in the last chapter, no intervention studies on income inequality were found. Through MSM, we can readily simulate the potential health effects of a tax policy that modifies the income distribution within a population.
The systems science approaches emerging in social epidemiology and public health research today are hardly new. Their historical use dates back to several decades within other disciplines, including physics, economics, engineering, and systems biology (Mabry et al. 2010). For example, systems science approaches such as ABM and MSM have been previously used to address wide‐ranging topics such as overfishing, the decline of ancient civilizations, climate change, and terrorism networks (Mabry et al. 2010). Their recent adoption into the public health arena can be attributed to a growing recognition of their utility for addressing intractable public health problems such as the spreading obesity epidemic (Hammond 2009) and the complexity of tobacco control policies (Tengs et al. 2001; Levy et al. 2002). Other recent applications of ABM to population health include analyzing the spread of infectious disease epidemics such as pandemic flu (Longini et al. 2005); modeling the social determinants of behaviors such as alcohol and drug use (Hoffer et al. 2009); and simulating dynamics of chronic diseases such as diabetes at a population level (Jones et al. 2006).
As should become evident throughout the remainder of this book, the possible applications of ABM and MSM to the social and economic determinants of population health are vast. These potential applications range from studying the spread of infectious diseases (e.g. COVID‐19) or spread of intractable problems such as the obesity epidemic, to modeling the social determinants of behaviors such as alcohol or drug use, to simulating the public health impacts of enacting new tax policies. These techniques have been largely developed and applied in other fields including computer science, political science, economics, and social policy. Diffusion and adoption of these approaches into the fields of social epidemiology and public health are more recent, and there remains a tremendous potential for transforming the landscape of these fields by integrating these novel applications.
2.5 Structure of this Book
In this introductory section (Part I), Chapter 1 defines the social determinants of health, discusses conventional approaches for studying them, and indicates the methodological limitations in identifying their impacts and comments on the public health significance of addressing the social determinants of health. In Chapter 2, we have provided a rationale and overview of current concepts and methods for applying two major sets of analytical tools, ABM and MSM, considered within a larger toolkit of modeling and simulation approaches, to study these social determinants.
In the next section, Part II (Chapters 3–6), we focus on conceptual and empirical applications of ABM to help “unpack” our understanding of the social determinants of health. It consists of four chapters providing an overview of current concepts and methods used for ABM and provides a state‐of‐the‐art, critical synthesis of the ABM evidence base both in the social sciences and in social epidemiology on the social determinants of health to inform future public health research and practice.
Chapter 3 reviews the key terms for agent‐based models in practitioner language, highlights ABM methods to assess the social determinants of health at a population level, discusses applications for studying the social determinants of health and novel extensions of this methodology, and provides an illustrative example. Chapter 4 gives detailed examples of empirically powerful
