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New Horizons in Modeling and Simulation for Social Epidemiology and Public Health
Daniel Kim

An introduction to state-of-the-art modeling and simulation approaches for social and economic determinants of population health

New Horizons in Modeling and Simulation for Social Epidemiology and Public Health offers a comprehensive introduction to modeling and simulation that addresses the many complex research questions in social epidemiology and public health. This book highlights a variety of practical applications and illustrative examples with a focus on modeling and simulation approaches for the social and economic determinants of population health.

The book contains classic case examples in agent-based modeling (ABM) as well as essential information on ABM applications to public health including for infectious disease modeling, obesity, and tobacco control. This book also surveys applications of microsimulation (MSM) including of tax-benefit policies to project impacts of the social determinants of health.

Specifically, this book:

Provides an overview of the social determinants of health and the public health significance of addressing the social determinants of health Gives a conceptual foundation for the application of ABM and MSM to study the social determinants of health Offers methodological introductions to both ABM and MSM approaches with illustrative examples Includes cutting-edge systematic reviews of empirical applications of ABM and MSM in the social sciences, social epidemiology, and public health Discusses future directions for empirical research using ABM and MSM, including integrating aspects of both ABM and MSM and implications for public health policies Written for a broad audience of policy analysts, public planners, and researchers and practitioners in public health and public policy including social epidemiologists, New Horizons in Modeling and Simulation for Social Epidemiology and Public Health offers a fundamental guide to the social determinants of health and state-of-the-art applications of ABM and MSM to studying the social and economic determinants of population health.

Table of Contents

1В  Cover (#u60b82a11-3ace-504f-a38b-11ccfd60bd6a)

2В  Title Page (#ud9fb4aea-04a7-5558-831f-3938cc08bb3a)

3В  Copyright Page (#u60548305-e1a8-5860-b6ad-bc4d83854876)

4В  List of Contributors (#u327a95b1-d126-51dc-9f2a-76d9b0abdb1f)

5В  Foreword (#u1a0f9779-0b6e-5dd2-9eed-1e9a465ea3b8)References (#ulink_2797f77e-39bf-5e67-b04c-834d82238687)

6В  Acknowledgements (#ue84251a3-c13d-5ef3-a7f0-76878497a955)

7В  List of Figures (#u6a4e9bd1-45d4-5115-ac93-aaf97e24b321)

8В  List of Tables (#u1ed17f67-32ca-56f4-8945-78ec90d39f23)

9В  Part I: Introduction (#ub17f08b8-6062-5e8f-b871-943c92a740da)1 A Primer on the Social Determinants of Health (#u9ffb5c9b-92b5-5040-a222-9c121bbb5039)1.1 Introduction (#ulink_e91216b0-a4b1-5996-9577-5cac571cbc2a)1.2 The Health Olympics: Winners and Losers (#ulink_913576f5-cc4b-5c4c-b38e-50751df3c3c8)1.3 What are the Social Determinants of Health? (#ulink_dc87e618-dd58-5863-bc8b-c3082423dc46)1.4 The 3 P’s (people, places, and policies) Population Health Triad (#ulink_47e6d04e-a67f-5e9d-b23c-21576ef97cc5)1.5 Conventional Approaches to Studying the Social Determinants of Health (#ulink_0147a975-ec5f-51b7-893e-ea6de270fd67)1.6 Novel Approaches to Strengthen Causal Inference in Studying the Social Determinants of Health (#ulink_53d48ffd-401d-57a8-b9ac-1426e740a323)1.7 What Do We Know About the Social Determinants of Health? (#ulink_2697a18c-e6e5-5f2f-b2b1-a8c5c6b5c80e)1.8 How Addressing the Social Determinants of Health Could Change Lives (#ulink_477fd53b-e1c5-571e-9bfe-cbeb603c3127)References (#ulink_e4697403-a137-5aa3-98d3-b3bc72e89b31)2 Rationale for New Modeling and Simulation Tools (#u10d506ba-86e8-5318-a99e-cf04a86e21d8)2.1 Advantages of Systems Science Approaches over Conventional Approaches (#ulink_e3cb2f3f-2548-5308-97f5-86ec7fafee98)2.2 Specific Advantages of Agent‐Based Modeling and Microsimulation Modeling (#ulink_1abb0752-0d91-58b7-b1e2-91f3c48db9b7)2.3 Comparison of Agent‐Based and Microsimulation Models (#ulink_3f008c18-1c08-5139-908c-c5edfde95805)2.4 Why ABM and MSM are Useful for Studying the Social Determinants of Health (#ulink_4e335be1-252f-54f8-a2ed-c0eed3f3d178)2.5 Structure of this Book (#ulink_c123a262-e6d5-591e-9d5b-002fb6999eaa)References (#ulink_9900018a-23f0-5cf8-bc53-3aead346e061)

10  Part II: Agent‐Based Modeling (#u7427f850-e5e3-5692-97a3-f84e594f264a)3 Overview of Current Concepts and Process for Agent‐Based Modeling (#ua79c3da0-fa4a-5ceb-b848-f93d438d88fa)3.1 The Components of an Agent‐Based Model: Key Terms (#ulink_fdce7b2b-0d2e-53a6-8fc4-f29f6d7d7f94)3.2 Steps in Designing and Deploying an Agent‐Based Model (#ulink_d0dae4b0-7d1b-5c08-bf2a-ddd0a8f7a89d)3.3 History of ABM Application and Categories of ABM Usage (#ulink_1a9bc1a8-8a8e-598a-8d8a-bad55929627e)References (#ulink_7bfc36ab-c27c-5623-8148-fe68a1491c7e)4 Agent‐Based Modeling in the Social Sciences (#ufbb211f1-dc5d-5bac-8e9f-71f2cfd053bc)4.1 Introduction (#ulink_9e3452ed-badd-5a41-9321-bcb683a213e0)4.2 Segregation (#ulink_c142f311-69d8-54ac-bbe1-0dc7aab7f9f6)4.3 Power Laws (#ulink_41143184-a6c1-502f-84be-b27a97149ee8)4.4 The Anasazi (#ulink_f7da336b-3c4d-54a9-b49a-1a72a405f6c9)4.5 Conclusions (#ulink_39681577-8a59-5a98-ba85-d0a7978953a3)References (#ulink_a742bdfb-77e8-5ea7-9ab8-ea917d5128e7)5 Agent‐Based Modeling in Public Health (#uf7f8b071-dbe0-5782-8331-7ac7bc49bd0e)5.1 Introduction (#ulink_f2b1b57a-0c3d-58c0-bcc0-666c0a6923c9)5.2 Scale of ABM Usage in Public Health (#ulink_d9b4fc2e-40d7-5f4b-88df-39e388949595)5.3 Example Models: Infectious Disease (#ulink_eb870bdd-e938-5444-b3e8-9bd833155782)5.4 Example Models: Obesity (#ulink_909fe6cc-24bd-5073-8b97-d261c9ab5f99)5.5 Example Models: Tobacco Control (#ulink_2f4f06cd-97f8-53af-b924-15c3df349875)5.6 Conclusions (#ulink_f94e6554-55c7-5700-ba20-2aa6d366ff12)References (#ulink_ca59b288-9915-5317-9ea1-81784b21d5b4)6 Section Summary (#u52d97053-b05d-532e-9e27-eb7adeb3ad57)6.1 Past Use of ABM for Public Policy Translation (#ulink_6bf55d8a-afa2-58d6-955a-dfc1a5c35a34)6.2 Bridging Gaps to Advance Agent‐Based Modeling of Social Determinants of Health (#ulink_c4634c01-faa7-53e6-8ed2-e9662de1ad2a)References (#ulink_c8d2a700-79e6-5419-afae-43fd3c480c42)

11В  Part III: Microsimulation Modeling (#u42551fe6-dfed-5dd6-be0f-73b9ce2fcdea)7 Concepts and Methods for Microsimulation Modeling in the Social Sciences (#uc360f5cb-2f3c-5f7b-a755-f677e655a2d0)7.1 Introduction (#ulink_25b997ce-6038-5b92-9845-1115c7bc1a4d)7.2 Methodological Choices (#ulink_e69bcebf-6848-50d7-9d89-23b3ed7b35bb)7.3 Population Scope (#ulink_5eced153-83ae-5cf0-bda3-28df4fe85a86)7.4 Policy Scope (#ulink_31bc180a-a09a-5354-8cfa-4efd126268bf)7.5 Building a Microsimulation Model (#ulink_e197920e-b7b3-5ad6-a0ee-6a721dda1909)7.6 Applications for Policy Making: Illustrations in the Domain of Health (#ulink_a47d7c88-cf1b-5fcc-90df-394e3ed0f3e2)7.7 Conclusions (#ulink_ad9e5cc1-18a9-575a-93cb-3511c86660b5)References (#ulink_7b456092-2eed-5574-87b5-dacd34b771a0)8 Empirical Evidence Using Microsimulation Models in the Social Sciences (#u71a9bf6c-4790-51c8-b1cc-170f4b5a25b6)8.1 Introduction (#ulink_228e4921-4578-534e-b19a-47ea3dda1ceb)8.2 Microsimulation and Economics (#ulink_14634732-4473-51e9-a647-0f51280fcd02)8.3 Microsimulation and the Prediction of Behavioral Changes (#ulink_43740e23-b0cf-501e-9e91-ad4d32eee053)8.4 Beyond Economics: Microsimulation and Other Social Sciences (#ulink_a0f03d4f-bf27-573e-bcd0-d5c72c38de70)8.5 Microsimulation and Geography (#ulink_fa6a361c-8603-5035-829d-e6c147048d6d)8.6 Microsimulation and Transports (#ulink_52db474f-9384-5ca3-a2ba-39c0d7a3aa61)8.7 Microsimulation and Environmental Sciences (#ulink_af01af64-0269-5cc6-8c36-0ed095a27dda)8.8 Conclusions (#ulink_f688e799-ac02-5b03-b4af-4a51bc4dda02)References (#ulink_d5e09c64-2b93-518c-8225-a55aaa8c4f19)9 Applications of Microsimulation Models to the Social Determinants of Health and Public Health (#u866bfbec-8a67-58d8-9bcf-1e02a416c484)9.1 Overview (#ulink_c0b65754-119c-5647-8ed5-25d99b3c600b)9.2 Direct Empirical Applications to the Study of the Social Determinants of Health (#ulink_410dc56e-4f83-57cf-834c-98868a6a7af7)9.3 Other Empirical Applications of Microsimulation Models to Medicine and Public Health (#ulink_f793b612-3473-5180-bd5e-aee3b66ad2aa)9.4 Chapter Summary (#ulink_e8b7af12-a6c3-5bb8-aa7b-d9783ce2c07b)References (#ulink_4569ed1b-815a-5b12-8282-3e396f316a6d)10 Section Summary (#u7122b96c-cf3f-5efd-8546-000ac6428661)10.1 Summary of Previous Chapters (#ulink_68d9c22b-19ac-5270-904b-5dbc31ef7c76)10.2 Direct Public Policy Relevance of Microsimulation (#ulink_673bc4a0-7882-502b-ad23-c6510b8031a9)10.3 Bridging Gaps to Advance Microsimulation Modeling of the Social Determinants of Health (#ulink_c5227749-59e4-5a8d-83f6-8d60eef8cbc4)References (#ulink_42dcce19-183c-5218-84e7-63ff17c94083)

12В  Part IV: Conclusions (#ucbad174c-46c7-509f-b793-e0c008689093)11 Future Directions (#ue545e4be-e97b-5e05-b9bb-c2c8dc674186)11.1 Avenues for Future Research (#ulink_ddac55bb-17a9-5bce-8380-649ff9c353a8)11.2 Conceptual Model and Empirical Examples of Integration of ABM and MSM (#ulink_4b4e8cd5-d3b9-5a33-bbe5-8dbe1ec6972c)11.3 Facilitators and Constraints in the Continued Emergence of Modeling and Simulation of the Social Determinants of Health (#ulink_eba48163-ca32-5b6f-a0ad-4e774a13af6d)11.4 Implications for Public Health (#ulink_2beee0c6-efda-54fd-81e6-964094703b85)References (#ulink_0dcf9d7f-0245-54d1-a431-7f6882c7f5ab)

13В  Index (#u6487f51c-e806-5e45-9015-a1d5f43d1916)

14В  Wiley Series in Modeling and Simulation (#u1f12dbe9-36b7-5086-bfc3-f3cdbb7a0520)

15В  End User License Agreement (#ud259c020-5e1a-5f89-b083-29d1c0757c0b)

List of Tables

1В Chapter 4Table 4.1 Selected subsequent papers related to Schelling’s original ABM pape…Table 4.2 Selected subsequent papers related to Axtell et al.’s original ABM …

2В Chapter 5Table 5.1 Number of hits from the literature search for peer‐reviewed publish…

3 Chapter 7Table 7.1 Selection of microsimulation health‐related models.

4 Chapter 8Table 8.1 Number of entries related to “microsimulation” in social sciences.Table 8.2 Incidence of indirect tax payments.

List of Illustrations

1В Chapter 1Figure 1.1 Life expectancy at birth for OECD countries. (#ulink_c8201e48-9e3c-5003-95e9-97d82bca7cfa)Figure 1.2 A social determinants of health conceptual framework. (#ulink_937b3456-c7bc-5a4d-b357-a976709dc255)Figure 1.3 The 3 P’s (people, places, and policies) Population Health Triad…. (#ulink_25f62623-04f9-5b33-8098-1fd6f23c06d9)Figure 1.4 Examples of multiple public sectors collectively adopting a Healt… (#ulink_975e3285-a634-5ad8-a180-9ae82c08fdfe)

2В Chapter 2Figure 2.1 Key differences between agent‐based modeling, microsimulation mod… (#ulink_5191cf9c-968d-5de3-aea4-3e4e5a78440e)

3В Chapter 3Figure 3.1 The PARTE framework. (#ulink_dd05338d-6672-5329-bd09-68790f5c09ca)

4В Chapter 4Figure 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) U…Figure 4.5 The Long House Valley in northeastern Arizona, present day.Figure 4.6 Dynamic landscape of potential maize production in Long House Val…Figure 4.7 Actual and simulated population of Long House Valley between 800 …

5В Chapter 7Figure 7.1 Building blocks of a microsimulation model.

6В Chapter 8Figure 8.1 Marginal effective tax rates (%) across the European Union, 2007….Figure 8.2 Total net child‐contingent payments vs. gross family/parental ben…Figure 8.3 Impact of fiscal consolidation measures by household income decil…Figure 8.4 Europe and the United States: own‐wage elasticities.

7В Chapter 9Figure 9.1 Published documents in Web of Science using combined keywords “mi…

8В Chapter 11Figure 11.1 Conceptual components of a potential ABM–MSM hybrid model and it…

Guide

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Wiley Series in

Modeling and Simulation

A complete list of the titles in this series appears at the end of this volume.

New Horizons in Modeling and Simulation for Social Epidemiology and Public Health

Daniel Kim

This edition first published 2021

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Library of Congress Cataloging‐in‐Publication Data

Names: Kim, Daniel (author).

Title: New horizons in modeling and simulation for social epidemiology and public health / Daniel Kim.

Other titles: Wiley series in modeling and simulation.

Description: Hoboken, NJ : Wiley, 2021. | Series: Wiley series in modeling and simulation | Includes bibliographical references and index.

Identifiers: LCCN 2020027470 (print) | LCCN 2020027471 (ebook) | ISBN 9781118589304 (hardback) | ISBN 9781118589427 (Adobe PDF) | ISBN 9781118589571 (epub)

Subjects: MESH: Social Determinants of Health | Public Health | Epidemiologic Methods | Models, Theoretical | Computer Simulation | Social Medicine

Classification: LCC RA418 (print) | LCC RA418 (ebook) | NLM WA 30 | DDC 362.1–dc23

LC record available at https://lccn.loc.gov/2020027470 (https://lccn.loc.gov/2020027470) LC ebook record available at https://lccn.loc.gov/2020027471 (https://lccn.loc.gov/2020027471)

Cover Design: Wiley

Cover Images: В© vectorfusionart / Shutterstock

List of Contributors

Francesco Figari Department of Economics University of Insubria Varese Italy

Ross A. Hammond Center on Social Dynamics & Policy The Brookings Institution Washington, DC USA; Brown School Washington University in St. Louis St. Louis, MO USA; and The Santa Fe Institute Santa Fe, NM USA

Daniel Kim BouvГ© College of Health Sciences Northeastern University Boston, MA USA; and School of Public Policy and Urban Affairs Northeastern University Boston, MA USA

Emanuela Lezzi Department of Economics University of Insubria Varese Italy

Joseph T. Ornstein Brown School Washington University in St. Louis St. Louis, MO USA

Hilde Philips Centre for General Practice University of Antwerp Antwerpen Belgium

Gerlinde Verbist Centre for Social Policy University of Antwerp Antwerpen Belgium

Foreword

I am well acquainted with the author of this book New Horizons in Modeling and Simulation for Social Epidemiology & Public Health, Dr. Daniel Kim, and the book’s contributors including Dr. Ross Hammond. As the former director of the Systems Science Program in the Office of Behavioral and Social Sciences Research (OBSSR) of the National Institutes of Health (NIH), I have had a bird’s eye view of this emerging area which I have written extensively about elsewhere (Mabry and Kaplan, 2013; Mabry et al., 2008, 2013).

The NIH and OBSSR have supported a variety of educational opportunities in systems science, such as the Symposia Series on Systems Science and Health (2007; https://www.preventionresearch.org/conferences/training/2007‐symposia‐series‐on‐systems‐science‐and‐health/ (https://www.preventionresearch.org/conferences/training/2007-symposia-series-on-systems-science-and-health/)) and the week‐long immersion course, Institute on Systems Science and Health( ISSH ), which ran annually from 2009 to 2012. More recently, the NIH has funded short courses such as Dynamic Systems Science Modeling for Public Health (PIs: Bruch, Hammond, Osgood; healthmodeling.org (http://healthmodeling.org)). However, one of the noteworthy gaps in the field is adequate instructional material in book form devoted to systems science applications to public health.

In 2012, Drs. Kim and Hammond both participated (Dr. Kim as a trainee and Dr. Hammond as the lead instructor) in the agent‐based modeling (ABM) track of the week‐long ISSH training course, at Washington University in St. Louis, hosted by Drs. Peter Hovmand and Doug Luke. As a cofounder and producer of ISSH (with Dr. Bobby Milstein, then at Centers for Disease Control and Prevention [CDC]), I had the privilege to witness the germ of this book. Dr. Kim has taken the best he has to offer in the social determinants of health and microsimulation modeling, complemented it with the expertise of Dr. Hammond in ABM and of other excellent book contributors with command of their subject areas, and produced 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 (#u866bfbec-8a67-58d8-9bcf-1e02a416c484)), as well as the potential of microsimulation to explore other questions in this vein (Chapter 10 (#u7122b96c-cf3f-5efd-8546-000ac6428661)). 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.

2В 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.

6В 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 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.

Part I Introduction

1 A Primer on the Social Determinants of Health

Daniel Kim

BouvГ© 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 (#ulink_c8201e48-9e3c-5003-95e9-97d82bca7cfa) 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 (#ulink_c8201e48-9e3c-5003-95e9-97d82bca7cfa)).

Figure 1.1 (#ulink_f09b14cf-3a40-5d33-9d49-e7b7726750df) 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 2017). These fatal overdoses played a role in declines in life expectancy among Americans for a second consecutive year in 2015 and 2016 (Kochanek et al. 2017), marking the first time this has happened in more than half a century. Gun deaths also rose in 2016 for a second consecutive year. Firearm‐related injuries contribute substantially to life expectancy, accounting for 7.1% of premature deaths or years of potential life lost before the age of 65 (Fowler et al. 2015).

Americans reach the age of 50 in worse health than their counterparts in other high‐income countries as older adults experience higher levels of morbidity and mortality from chronic diseases. Even socioeconomically advantaged (i.e. college educated or higher income) Americans fare worse than their counterparts in England and other countries (National Research Council and Committee on Population 2013). In offering potential explanations for these patterns, the panel referenced underlying societal factors—which we now commonly refer to as the social determinants of health—as possible root causes of the higher levels of morbidity and mortality and shorter life expectancies in the United States (National Research Council and Committee on Population 2013). For instance, despite its vast economy, the United States possesses considerably higher poverty rates and levels of income inequality than most high‐income countries. In addition, although the United States once led the world in educational performance, students in many other countries now routinely outperform US students; these findings are analogous to the relative standings of these countries in the Health Olympics. Finally, in contrast to the United States, a number of other countries such as Sweden and Norway in Scandinavia offer larger public welfare and other social safety net programs. Such programs and services could conceivably help residents to better weather the storm of adverse effects on health caused by poor economic and social conditions (Adema et al. 2011; Kim 2016).

1.3 What are the Social Determinants of Health?

In 2005, the World Health Organization (WHO) established a Commission on the Social Determinants of Health that was tasked with the job of supporting countries to address the upstream social factors that shape population health and health inequities (WHO Commission on the Social Determinants of Health 2008). The overall goal of the Commission was to draw the attention of governments and society to the social determinants of health and to create better social conditions for health, particularly amongst the most vulnerable populations. The commission delivered its final report to the WHO in 2008 (WHO Commission on the Social Determinants of Health 2008).

As defined by the WHO Commission, the social determinants of health are “the conditions in which people are born, grow, live, work, and age” (WHO Commission on the Social Determinants of Health 2008). These social determinants extend well beyond the confines of the health care system and include aspects of our neighborhood and workplace environments (e.g. the food, built, and social environments) and the social and economic policies (e.g. tax policies) that govern the regions in which we live. It is these “upstream” nonmedical social determinants that are increasingly understood as the root causes of population health inequalities, even within rich nations (Marmot and Bell 2009; Woolf and Braveman 2011). Such social determinants offer a critical lens to explain why the average life expectancy in America has lagged well behind other nations, despite the fact that the United States remains one of the richest nations in the world and spends more on a per‐capita basis on health care than all other developed nations globally (Marmot and Bell 2009). Identifying what impacts various social determinants have on population health is now the central focus of the growing public health field known as social epidemiology.

The WHO Commission on the Social Determinants of Health developed a conceptual framework of the social determinants of health (Solar and Irwin 2007; WHO Commission on the Social Determinants of Health 2008). Figure 1.2 (#ulink_937b3456-c7bc-5a4d-b357-a976709dc255) shows an adaptation of this conceptual framework. As illustrated in this figure, the social determinants of health are composed of the material living and working conditions and social environmental conditions in which people are born, live, work, and age, along with the structural drivers of these conditions. These structural drivers include individual‐ and area‐level socioeconomic status (SES), race/ethnicity, residential segregation, gender, social capital/cohesion, and the macroeconomic and macrosocial contexts, e.g. macroeconomic and social policies including labor market regulations (Muntaner et al. 2012), political factors including governance and political rights (Chung and Muntaner 2006; Bezo et al. 2012), and cultural factors. Examples of macroeconomic determinants include the gross domestic product (GDP) per capita and income inequality—the gap between the rich and the poor within societies.

Figure 1.2 (#ulink_ba598b4f-5efa-5c4c-8b30-8fca1af12149) A social determinants of health conceptual framework.

Source: Adapted from Kim and Saada (2013) and Solar and Irwin (2007).

The broader macroeconomic and social context generates social stratification, that is, the sorting of people into dominant and subordinate SES, racial/ethnic, and gender groups (Figure 1.2 (#ulink_937b3456-c7bc-5a4d-b357-a976709dc255)). Through social stratification and differential exposures of individuals to levels of material factors/social resources, social determinants such as individual/area‐level SES, race/ethnicity, and social capital shape individual‐level intermediary determinants, including behavioral factors (e.g. maternal smoking), biological factors, and psychosocial factors (e.g. social support), which in turn produce differential risks of, and inequities in, health outcomes (Figure 1.2 (#ulink_937b3456-c7bc-5a4d-b357-a976709dc255)). Access to health care and the quality of health care are also determinants of these outcomes, yet health care factors are believed to play lesser roles compared to societal factors (Figure 1.2 (#ulink_937b3456-c7bc-5a4d-b357-a976709dc255)). This is supported by cross‐national evidence on health care spending and life expectancy. Moreover, even in societies with a national health system in place (e.g. Canada and the United Kingdom), socioeconomic disparities and gradients in health are salient and well established.

1.4 The 3 P’s (people, places, and policies) Population Health Triad

Implicit in this conceptualization of the social determinants of health is that more upstream population characteristics, places, and policies matter to population health. Jointly, we can refer to these three factors that are pivotal to population health as the “3 P’s” (people, places, and policies) Population Health Triad (Figure 1.3 (#ulink_25f62623-04f9-5b33-8098-1fd6f23c06d9)). The classic Host–Agent–Environment epidemiologic triad posits that a susceptible host, an external agent, and an environment are needed to produce disease. Similarly, both places and policies interact with populations to manifest disease. For example, neighborhoods where we live can influence our health through physical and material characteristics such as air quality, access to nutritious foods and opportunities for leisure and exercise, health services, and education/schools and employment opportunities (Braveman et al. 2011). Policies in nonhealth sectors (e.g. transportation, education, and housing) can also intersect with and shape health. Social policies such as those that affect levels of welfare spending and tax policies that determine the rich–poor gap have plausible linkages to the social environment, health behaviors, and individual health and disease endpoints. Reciprocal interactions are also possible, with populations being able to shape both policies and places, such as by mobilizing together through social capital (e.g. political activism) to effect change (Figure 1.2 (#ulink_937b3456-c7bc-5a4d-b357-a976709dc255)).

Figure 1.3 (#ulink_c6ff4c45-90f3-5ea4-85ae-3264ab24678c) The 3 P’s (people, places, and policies) Population Health Triad.

To help address the social determinants of health at a government level, in 2010, the WHO and the Government of South Australia (2010) developed the HiAP approach through the Adelaide Statement on HiAP. In this comprehensive population health strategy, health considerations in policymaking permeate and encompass multiple public sectors that may influence health, such as transportation, agriculture, housing and urban development, and education (Figure 1.4 (#ulink_975e3285-a634-5ad8-a180-9ae82c08fdfe)). The HiAP approach was founded on the notion that many social determinants of health are outside the purview of public health agencies. The roots of this radical approach can be traced back to the seminal ideas put forth in the Alma Ata Declaration on Primary Health Care (1978) and the Ottawa Charter for Health Promotion (1986). The HiAP approach became reinforced in the 2011 Rio Political Declaration on Social Determinants of Health (World Health Organization 2016a).

Figure 1.4 (#ulink_3e061256-3fcf-591d-97bf-be3a95d8cd29) Examples of multiple public sectors collectively adopting a Health in All Policies (HiAP) approach.

The HiAP approach has been increasingly adopted in jurisdictions around the world. For example, the Department of Housing and Urban Development (HUD) in the United States has embraced a HiAP approach and is collaborating with the U.S. Department of Health and Human Services (HHS) to ensure the integration of the elderly and disabled into the community via housing and human service agencies to enable them to live as long and as healthily as possible (Bostic et al. 2012). HUD further encourages applicants to regional planning and neighborhood initiative grants to incorporate health metrics into their baseline assessments of neighborhoods and asks them to indicate how they will support regional planning efforts that consider public health impacts (Bostic et al. 2012). Moreover, to attain objectives on the social determinants of health, the HiAP approach has been encouraged by Healthy People 2020 (2010), the U.S. Centers for Disease Control and Prevention initiative that establishes national goals and objectives for policy, programs, and activities to address the major health challenges facing our country today. The Secretary’s Advisory Committee on Healthy People Objectives for 2020 (Office of Disease Prevention and Health Promotion 2010) has further advised that all federal agencies (e.g. the Departments of Education, Transportation, and HUD) should be required to include Healthy People in their strategic plans.

In 2010, the US state of California created a HiAP Task Force, with representation of 19 state agencies, offices, and departments. Employing a HiAP framework, this statewide effort brought policymakers together to identify and recommend programs, policies, and strategies to improve health, including multiagency initiatives addressing transportation, housing, affordable healthy foods, safe neighborhoods, and green spaces. Additional recommendations included the development of health criteria in the discretionary funding review process and incorporating health issues into statewide data collection and survey efforts (Health in All Policies Task Force 2010).

The region of South Australia has also implemented the HiAP approach. Its HiAP model is based on the twin pillars of central governance and accountability and a “health lens” analysis process, which aims to identify key interactions and synergies between South Australia’s Strategic Plan (SASP) targets, policies, and population health (Kickbusch and Buckett 2010). Notably, it was in Adelaide, the capital of South Australia, that the 2010 Adelaide Statement of HiAP was first developed. The South Australian Public Health Act was developed during the early implementation stages of HiAP and provided a legislative mandate to allow HiAP approaches to be systematically adopted across state and local governments within the region (Delany et al. 2015).

To strengthen the overall accountability for the HiAP pledges made by countries in the 2011 Rio Political Declaration on Social Determinants of Health, the WHO is currently developing a global monitoring system for intersectoral interventions on the social determinants of health to improve health equity (World Health Organization 2016b).

1.5 Conventional Approaches to Studying the Social Determinants of Health

Randomized experiments are the gold standard of study designs to establish cause‐and‐effect relationships. Yet, it is often neither feasible nor ethical to conduct experiments that randomly assign people or places to different levels of social determinants of health. As a result, evidence on the impacts of the social determinants of health has been largely based on observational studies, i.e. ecological, cohort, case–control, and cross‐sectional studies. Within such observational studies, traditional epidemiological approaches for studying the impacts of social determinants of health include multivariate analysis, which controls for factors that predict both the social determinants and health outcomes, i.e. so‐called potential “confounders.”

In addition, studies have explored these relationships by testing for single or multiple factors as potential mediators of the population health impacts of social determinants that could lend plausibility to the presence of causal associations. Because such social determinants are often contextual or area‐based factors (e.g. factors at the neighborhood or regional level), multilevel models that incorporate the hierarchical structure of data—such as individuals living within neighborhoods or states—are used to account for similarities and statistical nonindependence of individuals living within the same geographical areas (Goldstein et al. 2002).

1.6 Novel Approaches to Strengthen Causal Inference in Studying the Social Determinants of Health

A growing body of literature is attempting to reduce alternative explanations and other sources of bias in nonexperimental studies on the social determinants of health and more generally within public health. These novel approaches to strengthen causal inference include but are not limited to instrumental variable (IV) analysis, fixed effects analysis, propensity score analysis, inverse probability weighting, and natural experiments. By isolating random variation in the exposure, IV analysis can yield unbiased estimates of the causal association between an exposure and outcome, including through reducing attenuation bias due to measurement error and confounding bias due to both observed and unobserved factors (Kim 2016). Such approaches are increasingly being used to evaluate the causal roles of risk factors in public health, including obesity, neighborhood conditions, the social environment, and state policies (Davey Smith et al. 2009; Fish et al. 2010; Kim et al. 2011; Mojtabai and Crum 2013; Hawkins and Baum 2014; Kim 2016).

Similar to multivariable regression, propensity score analysis can control for imbalances between comparison groups and can thereby control for confounding. It has the advantage of being more efficient than traditional regression when there are relatively fewer events (Cepeda et al. 2003). However, like multivariable regression, propensity score analysis cannot control for unobserved or unmeasured confounders. Inverse probability weighting has also been used as an approach to estimate the counterfactual or potential outcome if all subjects were assigned to either exposure/treatment (Mansournia and Altman 2016). Finally, natural experiments or other quasi‐experimental designs such as regression discontinuity designs (Moscoe et al. 2015) can exploit random variation in exposures as in an experimental study and can thereby minimize confounding due to both observed and unobserved factors as a source of bias.

Results from individual studies can also be qualitatively reviewed in aggregate to identify existing gaps in methodological approaches, potential sources of bias, and similarities/differences in their results. Results across studies can be quantitatively summarized in meta‐analyses that yield overall point estimates of exposure–outcome associations, although, importantly, such estimates are only as good as the quality of the studies that are included in the meta‐analyses (Egger et al. 2001).

1.7 What Do We Know About the Social Determinants of Health?

As Bambra et al. (2010) have noted, there are clear limitations to the existing evidence based on the social determinants of health. First, observational studies that dominate the literature can only hint at possible interventions and their associated health effects; causal inference is an inherent limitation. Second, there is still only sparse evidence on the impacts of interventions on the social determinants of health. Bambra et al. (2010) conducted an “umbrella review” of the existing systematic reviews of the evidence on specific interventions on the social determinants of health spanning housing/living environment, work environment, transportation, health and social care services, agriculture and food, and water and sanitation. They identified some suggestive evidence that certain categories of interventions may impact inequalities regarding the health of specific disadvantaged groups, particularly in the fields of housing and work environment. Yet in other areas, such as evidence on policies in education, the health system, food and agriculture, and more generally on the influences of macro‐level policies on health inequalities, the empirical literature on interventions was more limited (Bambra et al. 2010).

In a more recent umbrella review, Thomson et al. (2017) adopted a systematic review approach to summarize the state of knowledge on how public health policy interventions (e.g. taxation and educational campaigns) may impact health inequalities such as differential effects across socioeconomic groups or effects of interventions targeted at disadvantaged groups. After searching studies published up to May 2017 within 20 databases (e.g. Medline, EMBASE, CINAHL, PsycINFO, Social Science Citation Index, Sociological Abstracts, and the Cochrane Library), the authors identified 24 systematic reviews reporting 128 relevant primary studies. They then summarized the evidence on policies (fiscal, regulation, education, preventive treatment, and screening) across eight public health domains (tobacco; food and nutrition; the control of infectious diseases; screening; road traffic injuries; air, land, and water pollution; built environment; and workplace regulations). The systematic reviews were mixed in quality, and the results were mixed across public health domains. For the tobacco, food and nutrition, and control of infectious diseases domains, the authors found evidence to suggest that fiscal and regulation policies were more beneficial for reducing or preventing health inequalities than educational campaigns (Thomson et al. 2017).

1.8 How Addressing the Social Determinants of Health Could Change Lives

In principle, intervening on the social determinants of health should have profound effects on population health outcomes and health equity. These outcomes include the numbers of lives saved and the occurrence of disease and other morbidity outcomes such as Disability‐Adjusted Life Years (DALYs) (Murray et al. 2015). If we consider the distal nature of these social determinants in Figure 1.2 (#ulink_937b3456-c7bc-5a4d-b357-a976709dc255), the impacts of these determinants on population health may in fact be stronger than those of proximal biological and behavioral factors at the individual level (such as smoking and high cholesterol), because upstream social determinants likely shape many of these biological and behavioral factors.

Yet what does the empirical evidence show about the impacts of social determinants of health at a population level? Drawing on studies from the public health literature, the numbers of adult deaths attributable to six social determinants of health have been estimated (Galea et al., 2011): low education, poverty, low social support, area‐level poverty, income inequality, and racial segregation. The investigators calculated summary relative risk estimates of mortality, and used prevalence estimates for each of these social determinants to estimate the associated population attributable risks (PARs, the percentage of deaths attributed to each factor), and then project the total number of deaths attributable to each social determinant in the United States. Through this approach, the authors estimated that 245 000 deaths would have taken place among Americans in the year 2000 due to low education, 176 000 deaths to racial segregation, 162 000 deaths to low social support, 133 000 deaths to individual poverty, 119 000 deaths to income inequality, and 39 000 deaths to area‐level poverty. These estimates due to social determinants of health were comparable to the total numbers of deaths due to the leading pathophysiological causes such as heart attacks (192 898 deaths), strokes (167 661 deaths), and lung cancer (155 521 deaths) (Galea et al. 2011). To further put the size of these numbers into perspective, in the year 2000, it was estimated that smoking resulted in 269 655 deaths among men and 173 940 deaths among women in the United States (Centers for Disease Control and Prevention (CDC) 2008).

In another study, Krueger et al. (2015) estimated the mortality attributable to education under three hypothetical scenarios: (i) individuals having less than a high school degree, (ii) individuals having some college education but not completing a bachelor’s degree, and (iii) individuals having any level of education but not completing a bachelor’s degree. The authors used National Health Interview Survey data (1986–2004) linked to prospective mortality through 2006 and discrete‐time survival models to derive annual attributable mortality estimates. The estimated numbers of attributable deaths were striking: 45 243 deaths in the 2010 US population were attributed to individuals having less than a high school degree rather than a high school degree; 110 068 deaths were due to individuals having some college education; and 554 525 deaths were attributed to individuals having anything less than a bachelor’s degree but not a bachelor’s degree (Krueger et al. 2015). The total numbers of deaths due to having less than a high school degree was similar among women and men and among non‐Hispanic Blacks and Whites and was greater for cardiovascular disease than for cancer. Overall, these estimates point to the substantial impacts that policies that increase educational opportunities could have on reducing the burden of adult mortality (Krueger et al. 2015).

Using nationally‐representative data, Kim (2016) estimated the impacts of state and local spending on welfare and education on the risks of dying from major causes. Each additional $250 per capita spent on welfare predicted a 3‐percentage point lower probability of dying from any cause, and each additional $250 per capita spent on welfare and education predicted a 1.6‐percentage point lower probability and a nearly 1‐percentage point lower probability of dying from coronary heart disease (CHD). To put such numbers into context, these changes are on the order of reductions achieved through treating a patient with high blood pressure or cholesterol—representing clinically meaningful changes (Kim 2016).

In a cross‐national study that implemented IV analysis to enhance causal inference, Kim et al. (2011) further estimated the population health impacts of raising social capital across 40 countries. Among those aged 15–74 years in 40 nations with at least 40% of the country trusting of others, raising country percentages of social trust by 20 percentage points in countries with at least 30% of a country’s citizens trusting of others and by 10 percentage points in countries with 30–40% average country trust was predicted to avert nearly 287 000 deaths per year.

Finally, Kondo et al. (2009) conducted a meta‐analysis of cohort studies including roughly 60 million participants in which people living in regions with high‐income inequality had an excess risk for premature mortality independent of their SES, age, and sex. The estimated excess mortality risk was 8% for each 0.05 unit increase in the Gini coefficient (a common measure of income inequality theoretically ranging from 0, representing perfect equality, to 1, corresponding to perfect inequality). While this excess risk appears modest, all of society is exposed to income inequality, such that the aggregate effects can be significant (Kondo et al. 2009). The authors estimated that if the inequality–mortality relation is truly causal, more than 1.5 million deaths (9.6% of total adult mortality in the 15–60 age group) could be averted in 30 OECD countries by reducing the Gini coefficient to below the threshold value of 0.3 (Kondo et al. 2009).

Notably, according to Figure 1.2 (#ulink_937b3456-c7bc-5a4d-b357-a976709dc255), there should also be substantial impacts of intervening on the social determinants of health on health inequities across population groups, as defined along social axes such as gender, race/ethnicity, and SES. For example, government spending on public assistance programs (e.g. Aid to Families with Dependent Children) and tax credit programs (e.g. the Earned Income Tax Credit) should reduce income disparities between the rich and the poor and thereby reduce associated gaps in health, since income is a strong determinant of health and disease.

As the U.S. National Academy of Sciences panel concluded in its report, if the United States fails to address its growing health disadvantage in the near future, it will lag even further behind comparable countries in life expectancy and across a wide range of other population health outcomes. By adversely affecting the productivity of the workforce through worse population health, the economy of the United States would also continue to suffer, whereas other countries would continue to reap the economic benefits of having healthier populations. Because of how much is at stake, the panel concluded that it would hence be at the United States’ peril that it continue to ignore its growing health disadvantage (National Research Council and Committee on Population 2013). Meanwhile, other countries will still need to maintain their efforts on addressing the social determinants of health if they wish to sustain and/or improve their relative standings in the Health Olympics.

Overall, the findings summarized in this chapter make a strong case for intervening at the policy level on social determinants to improve population health and reduce population health inequities. It is also clear that much more empirical evidence is needed if we wish to establish the population health impacts of the social determinants of health. These evidence gaps include estimates of the effects of social determinants of health on the incidence of diseases and on morbidity outcomes such as DALYs; the estimated population‐wide health impacts of intervening on the social determinants of health through scaled‐up interventions and policies; and economic evaluations (e.g. cost‐effectiveness) of such interventions.

In the next chapter, we move beyond traditional analytic approaches to provide a rationale for the use of systems science methods. In particular, we introduce two major sets of analytical tools for modeling and simulating impacts of the social determinants of health: agent‐based modeling and microsimulation models. These two novel system science tools and their growing applications in social epidemiology and public health form the primary substance of this book.

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2 Rationale for New Modeling and Simulation Tools : Agent‐Based Modeling and Microsimulation

Daniel Kim1,2 and Ross A. Hammond3,4,5

1 BouvГ© College of Health Sciences, Northeastern University, Boston, MA, USA

2 School of Public Policy and Urban Affairs, Northeastern University, Boston, MA, USA

3 Center on Social Dynamics & Policy, The Brookings Institution, Washington, DC, USA

4 Brown School, Washington University in St. Louis, St. Louis, MO, USA

5 The Santa Fe Institute, Santa Fe, NM, USA

2.1 Advantages of Systems Science Approaches over Conventional Approaches

The real world is made up of a series of complex systems. As we have seen in Chapter 1 (#u9ffb5c9b-92b5-5040-a222-9c121bbb5039), health and disease are products of causal factors operating through multiple pathways at multiple levels. Such complex systems are not simply linear—they are characterized by causal feedback loops and complex interactions between actors at multiple levels and are inherently dynamic. Traditional multivariable models adopt a more reductionist approach and lack the ability to capture such features. In general, they implement static or discretely longitudinal analyses, do not incorporate potential nonlinearities such as feedback loops, and do not capture behavioral responses of individuals (Luke and Stamatakis 2012). By contrast, systems science approaches were explicitly developed to account for such features.

Although variation in the relationship between exposures and outcomes that is “exogenous” or “as if random” is the primary objective of advanced methods used to strengthen causal inference, the real world is filled with endogeneity. Endogenous factors are those found within the same system, meaning that they may bias the association between an exposure and an outcome. Notably, systems science approaches do not regard the endogeneity of the real world as nuisances; rather, through a more holistic approach, they model the presence of such complex pathways and mechanisms to better understand them (Luke and Stamatakis 2012).

Systems science approaches represent innovative sets of tools that can model and simulate the real world with enough complexity to be useful. Yet importantly, like their traditional model cousins, they reflect simplified versions of reality. Ideally, systems models retain enough of the salient characteristics of complexity to enhance our understanding of the problem under study, without being so complex themselves that they are opaque and as impenetrable to our understanding as reality itself. Moreover, systems science approaches enable virtual conduct of experiments that are often not feasible, whether due to cost, ethical reasons, or the simple fact that there is no way to explore the impact of an intervention (e.g. policy) and also go back in time and intervene differently to compare outcomes. With simulation models, it is straightforward to compare a wide array of hypothetical scenarios in silico. For further exposition of the virtues of modeling, see Epstein (2008) and Mabry et al. (2010).

2.2 Specific Advantages of Agent‐Based Modeling and Microsimulation Modeling

Agent‐Based Modeling

Agent‐based modeling (ABM) offers four specific advantages for public health research. First, because each actor in the system under study can be explicitly represented, no aggregation or statistical summary is required in treatment of either individual characteristics or outcomes. As a result, ABM is a powerful tool for considering heterogeneity—whether in biology, cognition, demography, or context. This is especially important for topics such as health disparities (Kaplan et al. 2017). Second, ABM offers an effective way to consider adaptation—processes of learning, evolution, or bidirectional interaction between individuals over time. This means that not only can we consider short‐run impacts of policies or interventions, but we can also explore potential impacts over very long time horizons. Topics such as obesity, antibiotic resistance, and developmental origins of health and disease often benefit from such considerations. Third, ABM is able to incorporate very sophisticated representations of structure and space, including social network data, physical space data from geographic information systems (GISs) or light detection and ranging (LIDAR), and biological space (e.g. physiology). Rather than either assuming away spatial elements or reducing them to summary statistics (for example, zip code‐level density of retailers), agent‐based models can carry a full accounting of spatial exposure and interaction throughout the dynamic simulation. Recent efforts to consider “precision prevention” in communities (Gillman and Hammond 2016; Economos and Hammond 2017) and retailer‐oriented tobacco control policies (Luke et al. 2017) leverage this facility of ABM. Finally, ABM is well suited for multilevel modeling. Each individual agent can contain detailed representations of “below‐the‐skin” processes such as energy balance, cognition, decision‐making, or disease progression; at the same time, the agents can interact with each other, with physical environments, and with population‐level signals (Hammond 2009; Hammond and Ornstein 2014). Although arguably essential to full understanding of many chronic disease challenges, crossing the “skin barrier” remains rare in social epidemiology.

Microsimulation Models

Microsimulation models (MSM) enable simulations of policies on samples of economic agents (individual, households, and firms) at the individual level (Bourguignon and Spadaro 2006). These simulations allow for the projection of the consequences of modifying economic conditions for each individual agent in the sample. Through such projections, we can estimate the overall aggregate impacts of a policy as well as the distributional consequences of the policy in terms of “winners” and “losers.” These could be population subgroups as defined by social axes including age, gender, race/ethnicity, and socioeconomic status. Such policies may be expensive and not readily feasible to undertake in the real world. For example, changing the income tax structure can alter what absolute income individuals in a population receive and influence the distribution of income (i.e. levels of income inequality) within the population. Through tax microsimulation, we can hence project the absolute and relative income impacts without actually implementing these changes in the real world. MSM can thus offer a convenient and inexpensive means to estimate the overall population impacts of social policies.

Other Complex Systems Modeling Tools

Other key systems science approaches (not reviewed in this book) include system dynamics models and social network 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 (#ulink_5191cf9c-968d-5de3-aea4-3e4e5a78440e) 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 (#ulink_8cb3f070-d6c1-55e8-82c5-e87648d8ac81) 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 (#ub17f08b8-6062-5e8f-b871-943c92a740da)), Chapter 1 (#u9ffb5c9b-92b5-5040-a222-9c121bbb5039) 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 (#u7427f850-e5e3-5692-97a3-f84e594f264a) (Chapters 3 (#ua79c3da0-fa4a-5ceb-b848-f93d438d88fa)–6 (#u52d97053-b05d-532e-9e27-eb7adeb3ad57)), 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 (#ua79c3da0-fa4a-5ceb-b848-f93d438d88fa) 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 (#ufbb211f1-dc5d-5bac-8e9f-71f2cfd053bc) gives detailed examples of empirically powerful applications of ABM in the social sciences, including a foundation to inform the more recent ABM evidence on the social determinants of health that are reviewed in the subsequent chapter. This body of evidence is explored and grouped into three key areas: neighborhood research (e.g. residential preferences), research on scaling laws in social systems, and anthropological models. Chapter 5 (#uf7f8b071-dbe0-5782-8331-7ac7bc49bd0e) reviews the current evidence on the applications of ABM in social epidemiology and public health to better understand the impacts of the social determinants of health. This evidence is explored and grouped into three key areas: health disparities, obesity, and tobacco control. Several examples of public health applications of ABM including for modeling obesity and tobacco use are introduced. These include models that explore the effects of social influence on obesity dynamics (Hammond and Ornstein 2014), models of residential segregation and obesity disparities (Auchincloss et al. 2011), and a model examining retail point‐of‐sale interventions for tobacco control (Luke et al. 2017).

Chapter 6 (#u52d97053-b05d-532e-9e27-eb7adeb3ad57) summarizes the evidence highlighted in Chapters 4 (#ufbb211f1-dc5d-5bac-8e9f-71f2cfd053bc) and 5 (#uf7f8b071-dbe0-5782-8331-7ac7bc49bd0e) and past public policy translation of this evidence and discusses the apparent evidence gaps that, if addressed, would advance the ABM field of inquiry for modeling and “unpacking” the social determinants of health.

Part III (#u42551fe6-dfed-5dd6-be0f-73b9ce2fcdea) (Chapters 7 (#uc360f5cb-2f3c-5f7b-a755-f677e655a2d0)–10 (#u7122b96c-cf3f-5efd-8546-000ac6428661)) analogously focuses on conceptual and empirical applications of MSM to simulate and thus enhance our understanding of the impacts of the social determinants of health. It provides an overview of the current concepts and methods used for MSM and gives a rich synthesis of the MSM evidence base both in the social sciences and in the field of public health on the social determinants of health.

Chapter 7 (#uc360f5cb-2f3c-5f7b-a755-f677e655a2d0) reviews the key terms using MSM in practitioner language, highlights microsimulation modeling 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 8 (#u71a9bf6c-4790-51c8-b1cc-170f4b5a25b6) reviews the current evidence on the applications of MSM in the social sciences and gives a foundation to inform the more recent MSM evidence on the social determinants of health reviewed in the subsequent chapter. Chapter 9 (#u866bfbec-8a67-58d8-9bcf-1e02a416c484) systematically reviews the current evidence on the applications of MSM to better understand the impacts of the social determinants of health. For example, the chapter describes published examples of applications of MSM including projections of the economic cost savings and population health benefits that would occur if the WHO’s recommendations on the social determinants of health were to be adopted in Australia and the impacts on mortality burden of modifying US federal income tax policies based on recent proposals. This chapter also highlights some MSM applications to the study of health care policies, disease microsimulation, and health behavior‐related policies.

Chapter 10 (#u7122b96c-cf3f-5efd-8546-000ac6428661) summarizes the evidence presented in Chapters 8 (#u71a9bf6c-4790-51c8-b1cc-170f4b5a25b6) and 9 (#u866bfbec-8a67-58d8-9bcf-1e02a416c484), comments on public policy translation of some of this evidence, and discusses current evidence gaps that, if filled, could move the MSM field toward a richer understanding of the social determinants of health.

We conclude the book with Part IV (#ucbad174c-46c7-509f-b793-e0c008689093), Chapter 11 (#ue545e4be-e97b-5e05-b9bb-c2c8dc674186), by discussing the future directions for research using ABM and microsimulation modeling and simulation, including the convergence of aspects of both ABM and MSM into the same analyses. It also comments on facilitators and constraints for the continued emergence of these forms of modeling and simulation and casts light on the potential policy implications of findings from these more complex and integrative models.

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Part II Agent‐Based Modeling

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