New Horizons in Modeling and Simulation for Social Epidemiology and Public Health. Daniel Kim
Solar and Irwin (2007).
Figure 1.3 The 3 P's (people, places, and policies) population health triad.
Figure 1.4 Examples of multiple public sectors collectively adopting a Health in All Policies (HiAP) approach.
Figure 2.1 Key differences between agent‐based modeling, microsimulation modeling, and traditional statistical models.
Figure 3.1 The PARTE framework. Source: Reproduced from Hammond (2015).
Figure 4.1 Schelling checkerboard (initial state).
Figure 4.2 Schelling checkerboard (first six moves).
Figure 4.3 Schelling checkerboard (final state).
Figure 4.4 Power law phenomena crop up throughout the social sciences: (a) US firm sizes. (b) Battle deaths by war (1816–2007). (c) US city populations (2010). (d) Word usage in English language books. (e) The distribution of Twitter followers among popular accounts.
Figure 4.5 The Long House Valley in northeastern Arizona, present day.
Figure 4.6 Dynamic landscape of potential maize production in Long House Valley.
Figure 4.7 Actual and simulated population of Long House Valley between 800 and 1300 ad.
Figure 7.1 Building blocks of a microsimulation model.
Figure 8.1 Marginal effective tax rates (%) across the European Union, 2007. Source: Jara and Tumino (2013) using EUROMOD.
Figure 8.2 Total net child‐contingent payments vs. gross family/parental benefits per child as a percentage of per capita disposable income. Source: Figari et al. (2011b) using EUROMOD.
Figure 8.3 Impact of fiscal consolidation measures by household income decile group. Source: Paulus et al. (2017) using EUROMOD.
Figure 8.4 Europe and the United States: own‐wage elasticities. Source: Bargain et al. (2014) using EUROMOD and TAXSIM.
Figure 9.1 Published documents in Web of Science using combined keywords “microsimulation” and (“health” OR “disease”), 1991–2017.
Figure 11.1 Conceptual components of a potential ABM–MSM hybrid model and its applications. Source: Adapted from Bae et al. (2016).
List of Tables
Table 4.1 Selected subsequent papers related to Schelling’s original ABM papers.
Table 4.2 Selected subsequent papers related to Axtell et al.’s original ABM paper.
Table 5.1 Number of hits from the literature search for peer‐reviewed published articles using ABM in each topic area.
Table 7.1 Selection of microsimulation health‐related models.
Table 8.1 Number of entries related to “microsimulation” in social sciences.
Table 8.2 Incidence of indirect tax payments.
1 A Primer on the Social Determinants of Health
Daniel Kim
Bouvé College of Health Sciences, Northeastern University, Boston, MA, USA
School of Public Policy and Urban Affairs, Northeastern University, Boston, MA, USA
1.1 Introduction
We begin this book with a simple example of cross‐country comparisons of life expectancy that illustrates the striking differences in health across populations. The social determinants of health—fundamental social and economic conditions in which we live, work, and play—may help to shape and explain such stark population health inequalities. In this introductory chapter, I present a conceptual framework for the social determinants of health and two related population health frameworks—the 3 P's (people, places, and policies) Population Health Triad and the Health in All Policies (HiAP) approach. I next discuss approaches for studying the social determinants of health, highlight what we know so far about them, and give some practical examples of their estimated large public health impacts if we were to intervene and modify them.
1.2 The Health Olympics: Winners and Losers
The “Health Olympics” is a term that was coined to describe how rich countries perform relative to each other in life expectancy at birth (Population Health Forum 2003). Figure 1.1 shows these results for 2017 by sex and for the sexes combined based on data for Organisation for Economic Co‐operation and Development (OECD) countries (OECD 2018). In these hypothetical Olympics, there are clear winners and losers.
Despite being one of the richest nations in the world, the United States fails to medal in this imaginary international competition; in fact, it falls well short of the podium, placing twenty‐seventh, with an overall life expectancy of 78.6 years. By contrast, Japan wins the gold medal for life expectancy for men and women combined at 84.1 years—first among women at 87.1 years, second among men at 81.0 years—and bests the United States by 5.5 years, an enormous gap in life expectancy at a population level. Meanwhile, Australia and a number of countries in the European Union either land on the medal podium or are at least very close to it (Figure 1.1).
Figure 1.1 Life expectancy at birth for OECD countries.
Source: From OECD (2018).
Differences in life expectancy at birth are often ascribed to a number of factors, including variations in living standards, lifestyle risk factors, education, and access to health services. But what additional insights can research shed in relation to such patterns? In 2013, the U.S. National Academy of Sciences (NAS) commissioned a scientific panel to explore such cross‐national comparisons in life expectancy. This panel released its findings in a report entitled U.S. Health in International Perspective: Shorter Lives Poorer Health (National Research Council and Committee on Population 2013). The panel compared health outcomes in the United States to those of 16 comparable high‐income countries, including whether the US health disadvantage exists across all ages. It also explored potential explanations and assessed the broader implications of these findings. The panel identified a strikingly consistent and pervasive pattern of higher mortality and worse health among Americans compared to those in other nations between the late 1990s and 2008. This health disadvantage starts at birth, affects all age groups up to age 75, and encompasses multiple health and disease outcomes and conditions (e.g. injuries and homicide, infections, heart disease, obesity, and arthritis) and biological and behavioral risk factors (National Research Council and Committee on Population 2013).
Furthermore, the NAS panel reported that premature deaths occurring before age 50 accounted for as much as two‐thirds of the difference in life expectancy in men between the United States and other countries and one‐third of the difference in women (National Research Council and Committee on Population 2013). Skyrocketing overdoses of drugs, primarily due to opioids, are a major contributor to these premature deaths (National Center for Health Statistics