what is destined to become a key resource for public health students, professors, and practitioners. New Horizons in Modeling and Simulation for Social Epidemiology & Public Health, with its focus on agent‐based modeling, microsimulation, and social determinants of health, is the perfect complement to the recent entrants in this area (El‐Sayed and Galea, 2017; Kaplan et al., 2017) as well as Dr. Thomas Valente’s (2010) impactful Social Networks and Health.
As indicated by its title, New Horizons in Modeling and Simulation for Social Epidemiology & Public Health is designed to give graduate students in public health an introduction to modeling and simulation to address research questions in social epidemiology and public health. While this is an excellent resource for this audience, it is sure to become a staple on the bookshelf of not only students but professors in public health and many other health‐relevant disciplines. The book provides an excellent introduction to social epidemiology followed by classic case examples in ABM (Schelling model and study of the historic Anasazi population; also within the book's covers the reader will find essential background on ABM use in public health including an overview of ABM for infectious disease modeling, obesity, and tobacco control) and highlights some of the seminal contributions Dr. Hammond has made to the field. The book contains a comprehensive introduction to microsimulation including how to make informed choices regarding time and space, data, policy rules and scope, population structure, validation, and more. An application section illustrates microsimulation models used in population health. A chapter is devoted to educating the reader about various microsimulation models in the social sciences (economics, demography, geography, transportation, and environmental sciences). The book never loses its focus on the social determinants of health, and there are valuable chapters devoted to reviewing the literature on microsimulation models and social determinants of health including important recent contributions by Dr. Kim (Chapter 9), as well as the potential of microsimulation to explore other questions in this vein (Chapter 10). Finally, the book lays out a conceptual model and empirical examples of how ABM and microsimulation can be integrated for additional impact.
With so much information packed into this readable book, it will quickly become a go‐to reference and primer of choice for anyone interested in modeling for public health and/or interested in studying the social determinants of health.
Patricia L. Mabry, PhD
Research Investigator
HealthPartners Institute
Former Senior Advisor and Acting Deputy Director, National Institutes of Health (NIH) Office of Behavioral and Social Sciences Research (OBSSR)
References
1 El‐Sayed, A.M. and Galea, S. (eds.) (2017). Systems Science and Population Health. Oxford University Press.
2 Kaplan, G.A., Diez Roux, A.V., and Simon, C.P. (eds.) (2017). Growing Inequality: Bridging Complex Systems, Population Health, and Health Disparities. Washington, D.C.: Westphalia Press.
3 Mabry, P.L. and Kaplan, R.M. (2013). Systems science: a good investment for the public’s health. Health Education and Behavior 40 (1 suppl): S9–S12.
4 Mabry, P.L., Milstein, B., Abraido‐Lanza, A.F. et al. (2013). Opening a window on systems science research in health promotion and public health. Health Education and Behavior 40 (1 suppl): 5S–8S.
5 Mabry, P.L., Olster, D.H., Morgan, G.D., and Abrams, D.B. (2008). Interdisciplinarity and systems science to improve population health: a view from the NIH office of behavioral and social sciences research. American Journal of Preventive Medicine 35 (2 suppl): S211–S224.
6 Valente, T.W. (2010). Social Networks and Health: Models, Methods, and Applications, vol. 1. New York: Oxford University Press.
Acknowledgements
I am grateful to the National Library of Medicine at the United States National Institutes of Health for awarding me a Grant for Scholarly Works in Biomedicine and Health to support the writing of this book (grant number G13 LM012056). I also express my appreciation to Kyle Oddis for editorial assistance. I dedicate this book to my father, Sung Gyum Kim, and to all those in the fields of social epidemiology and public health—past, present and future—who have devoted or will devote their lives to improving population health and health equity for all.
— Daniel Kim, MD, DrPH, Boston, Massachusetts
List of Figures
Figure 1.1 Life expectancy at birth for OECD countries. Source: From OECD (2018).
Figure 1.2 A social determinants of health conceptual framework.Source: Adapted from Kim and Saada (2013) and 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
