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Suggested Citation:"1 Introduction." Institute of Medicine. 2015. Assessing the Use of Agent-Based Models for Tobacco Regulation. Washington, DC: The National Academies Press. doi: 10.17226/19018.
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1

Introduction

Computational modeling of social processes has been used for many years in numerous disciplines for a variety of purposes, including assisting in the development of public policy decisions. A computational model can be used to inform the regulatory process and can “range from single parameter linear relationship models to models with thousands of separate components and many billions of calculations” (NRC, 2007, p. 36). A growing interest in systems science approaches to population health has led public health researchers, regulators, and others to turn to computational modeling. Many types of models have been used to forecast health effects associated with current and future risk behaviors, including tobacco use. For example, several population dynamics models have been used to simulate the dynamics of smoking use and smoking-attributed deaths in a state or nation and the effects of policies or policy changes on those outcomes (HHS, 2014). Since 2009 the U.S. Food and Drug Administration (FDA) has had broad regulatory authority over tobacco products and has used models as one tool to inform its policy decision-making activities. Recently, FDA has been exploring the usefulness of a particular computational modeling approach—agent-based modeling (ABM)—to inform its policy decisions. (See the section titled Computational Modeling of Tobacco Use for more information on ABMs.)

To that end, the Center for Tobacco Products (CTP) at FDA asked the Institute of Medicine (IOM) to review an ABM developed for use by FDA; to comment on its strengths, weaknesses, and usefulness for examining various tobacco regulatory policies; and to provide recommendations on strategies to improve the model and for using ABM in general in the future.

Suggested Citation:"1 Introduction." Institute of Medicine. 2015. Assessing the Use of Agent-Based Models for Tobacco Regulation. Washington, DC: The National Academies Press. doi: 10.17226/19018.
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To address that request, the IOM created the Committee on the Assessment of Agent-Based Models to Inform Tobacco Product Regulation (see Box 1-1 and below for a discussion of the committee’s statement of task).

At the committee’s first meeting, CTP expressed interest in exploring ABMs as a tool for tobacco control policy for several reasons. CTP noted that ABMs have been used to examine complex phenomena and may be particularly useful in providing insight into phenomena for which social interactions and population variation are important factors. CTP explained that ABMs are one tool that might allow CTP to learn more about the importance of individual-level factors that dictate tobacco use, as well as simulate potential use patterns in an evolving market (Fultz, 2014). CTP added that it is motivated by the potential of ABMs to simulate potential effects of policies for which there might be limited data. (See Chapters 3 and 6 for discussion of the limitations of modeling with incomplete data.) CTP also pointed out that ABMs can be helpful when addressing questions where there are ethical issues with using human subjects to conduct the research (Fultz, 2014).

BOX 1-1
Committee on the Assessment of Agent-Based Models to Inform Tobacco Product Regulation Statement of Task

The Institute of Medicine (IOM) shall convene a committee to assess the applicability of agent-based models of tobacco use and public health as a guide to inform regulators and improve the effect of tobacco regulation policies on public health. The committee shall:

  • comment on implications of using agent-based models to examine various tobacco regulatory policies
  • assess the strengths and weaknesses of an agent-based model developed for the U.S. Food and Drug Administration (FDA) (to be provided by the Center for Tobacco Products [CTP]) and models currently available in the literature that have been used for similar purposes (to be identified by CTP)
  • make recommendations on future directions and strategies to improve the usefulness of the model developed for or to be used by FDA, if needed
Suggested Citation:"1 Introduction." Institute of Medicine. 2015. Assessing the Use of Agent-Based Models for Tobacco Regulation. Washington, DC: The National Academies Press. doi: 10.17226/19018.
×

ADDRESSING THE STATEMENT OF TASK

The IOM committee was tasked with evaluating a specific ABM commissioned by FDA and to comment more broadly on the application of the ABM approach with respect to tobacco regulatory policy. The committee was also asked to review relevant ABMs in the literature to glean best practices.1 In addition, CTP asked the committee to identify research gaps related to using ABMs to inform policy (Fultz, 2014).

Because the committee was specifically requested by FDA to evaluate ABMs, that is the major focus of this report. This report addresses modeling techniques similar to ABMs (such as microsimulation), but other potentially useful modeling approaches (such as aggregate models or system dynamics models) are not discussed in detail, except when relevant to ABMs—for example, using ABMs to inform aggregate models. Additionally, it was beyond the scope of this report to discuss when ABMs versus other modeling approaches are suitable to address specific types of questions and contexts. Other reports, however, have compared and contrasted different types of models, including ABMs, and have proposed various ways to identify the appropriateness of using certain modeling approaches for specific situations (Chattoe et al., 2005; Irwin and Wrenn, 2014; NRC, 2014). It is important to note that although some of the discussions in this report are relevant to modeling in general, the assessment of the strengths and limitations of ABMs identified by the committee are not applicable to other modeling approaches unless specified in the report. Furthermore, the committee formally assessed only one ABM in this report as outlined in its statement of task, and although lessons from the development of that model may be applied to future development of ABMs, it is not indicative of the strengths or limitations of other tobacco control ABMs, or tobacco control models using other approaches.

Overview of the Study Process

The IOM convened a 12-member committee (see Appendix E for the committee biographies) with expertise in the fields of modeling, tobacco use behavior and epidemiology, economics, and policy application. To address its charge, the committee gathered information through a variety of means. The committee reviewed literature regarding ABMs, other computational modeling approaches, modeling for policy, and tobacco use behavior. Additionally, the committee heard from various experts in these fields, and

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1An ABM by Eppstein and colleagues (2011) was specifically identified by CTP for committee review and is discussed in Chapter 4.

Suggested Citation:"1 Introduction." Institute of Medicine. 2015. Assessing the Use of Agent-Based Models for Tobacco Regulation. Washington, DC: The National Academies Press. doi: 10.17226/19018.
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explored, learned from, and built on past National Research Council and IOM reports that discuss various modeling techniques, including ABMs.

The committee convened five times between February and November 2014, holding three open-to-the-public information-gathering sessions and two closed-session deliberation meetings. The agendas for the three public meetings can be found in Appendix D. During the first meeting, the committee was presented the charge by CTP as well as the model developed for CTP by Sandia National Laboratories. The second meeting focused on hearing from experts about individual components of the model to be reviewed by the committee, including both its technical and its social and behavioral features. In the third meeting, the committee heard from additional experts and reflected on lessons learned and best practices for using modeling, specifically for informing policy decisions.

The committee received public submissions of materials for its consideration at the meetings and throughout the course of the study.2 A website was created to provide information to the public about the committee’s work and to facilitate communication between the public and the committee.3 The committee commissioned three experts—Lawrence Blume, Ross Hammond, and Alan Sanstad—to write papers that identify varying views concerning ABM, the practice of and pitfalls associated with ABM, and lessons learned regarding the application of ABMs in health and energy policy. Given the multifaceted approaches to ABMs across disciplines, these papers enriched the committee’s discussion and understanding of ABMs from other fields of study and informed the committee’s conclusions. As with many fields, there are differences of opinion on how to approach and develop ABMs. These papers provide some of that context and begin to elucidate where there is agreement versus dissention regarding ABMs and identify best practices across varied fields of study. These papers are referenced throughout the report where relevant and are provided in Appendixes A, B, and C.

BACKGROUND

The Continuing Challenge of Tobacco Control

Tobacco consumption continues to be the leading cause of preventable death and disease in the United States (HHS, 2014). More than 42 million Americans, representing approximately 18 percent of the population, currently smoke cigarettes (Agaku et al., 2014; Jamal et al., 2014). Each day, more than 3,200 children under age 18 smoke their first cigarette, and

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2Public access materials can be requested from: https://www8.nationalacademies.org/cp/ManageRequest.aspx?key=49612.

3See http://iom.nationalacademies.org/Reports/2015/Tobacco-Policy-Agent-Based-Modeling.aspx.

Suggested Citation:"1 Introduction." Institute of Medicine. 2015. Assessing the Use of Agent-Based Models for Tobacco Regulation. Washington, DC: The National Academies Press. doi: 10.17226/19018.
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more than 700 children become daily cigarette smokers (SAMHSA, 2013). Many of these youth will become addicted and suffer from adverse health consequences. At the current smoking rate, 5.6 million children alive today will die prematurely from smoking-related illness (HHS, 2014).

Tobacco can lead to a wide range of consequences, from debilitating illnesses to severe economic costs. Each year, nearly half a million people in the United States die prematurely from diseases caused by smoking or secondhand smoke exposure, which equates to more than 1,300 deaths every day (HHS, 2014). Life expectancy for smokers is at least 10 years shorter than for nonsmokers (Jha et al., 2013). A 2009 study estimated that U.S. adults have about 14 million major medical conditions that are attributable to smoking (Rostron et al., 2014). Smoking is now associated with 13 types of cancers as well as numerous other diseases, including diabetes and rheumatoid arthritis (HHS, 2014). More than 87 percent of lung cancer deaths, 61 percent of pulmonary disease deaths, and 32 percent of deaths from coronary heart disease are attributable to smoking and exposure to secondhand smoke in the United States (HHS, 2014). In terms of economic burden, tobacco use costs the United States billions of dollars each year. More than $289 billion is incurred in medical expenses and lost productivity from smoking, and $5.6 billion is incurred from lost productivity caused by secondhand smoke (HHS, 2014).

Patterns of tobacco use are evolving (HHS, 2014). Although cigarettes and other combustible products (e.g., cigars, pipes, and hookahs) continue to be the most prevalent forms of adult tobacco use (Agaku et al., 2014), emerging tobacco products, such as electronic cigarettes (e-cigarettes), have been growing in prevalence (Arrazola et al., 2013; HHS, 2014; King et al., 2015). The portion of individuals who had ever used e-cigarettes grew from 3.3 percent to 8.5 percent from 2010 to 2013 for adults over 18 years of age (King et al., 2015), and from 3.3 percent to 6.8 percent from 2011 to 2012 for adolescents in grades 6–12 (Corey et al., 2013). Recent research indicates that the use of e-cigarettes among adolescents has now surpassed the use of traditional tobacco cigarettes or any other tobacco product (Johnston et al., 2015). Furthermore, the use of multiple tobacco products, such as using both smokeless tobacco and cigarettes, has expanded (Apelberg et al., 2014; HHS, 2014; Lee et al., 2014). As new tobacco products rapidly emerge and patterns of tobacco use evolve, the possibility of an increase in initiation and decreased or delayed cessation among youth and young adults is cause for concern. Simulation models have been used by FDA to help address the critical health and social concerns of the present smoking epidemic.

Overview of FDA’s Authority Over Tobacco Products

Until 2009, tobacco products were exempt from regulation under the nation’s federal health and safety laws. FDA had regulated food, drugs

Suggested Citation:"1 Introduction." Institute of Medicine. 2015. Assessing the Use of Agent-Based Models for Tobacco Regulation. Washington, DC: The National Academies Press. doi: 10.17226/19018.
×

(including nicotine replacements), and cosmetics for many decades, but not tobacco products. On June 22, 2009, the Family Smoking Prevention and Tobacco Control Act (Tobacco Control Act) gave FDA the authority to regulate the manufacture, distribution, and marketing of tobacco products to protect public health and reduce tobacco use in the United States. To oversee the implementation of the law, FDA established CTP. The goals of CTP are to prevent people from starting to use tobacco products, encourage current tobacco users to quit, and reduce the harm caused by tobacco use.

The Tobacco Control Act gives FDA the authority to regulate cigarettes, cigarette tobacco, roll-your-own tobacco, and smokeless tobacco. Additional tobacco products, such as e-cigarettes and cigars, are being considered through a deeming proposal.4 The Tobacco Control Act gives FDA the authority, through rule making, to adopt tobacco product standards appropriate for the protection of public health. FDA can adopt new product standard provisions to reduce addiction, reduce toxicity and carcinogenicity, reduce harmful constituents, restrict sale and distribution, and address the form and content of labeling for the proper use of tobacco products. Other authorities include restricting advertising and promotion and imposing the placement of health warnings on products. (See Chapter 2 for a detailed discussion of FDA’s regulatory authority.)

New tobacco policies and regulations must be based on available medical, scientific, and other technological evidence as appropriate for the protection of the public health. In particular, FDA reviews new tobacco products on the basis of a public health standard instead of the “safe and effective” standard that it uses to evaluate drugs. The public health standard requires FDA to consider scientific evidence concerning (1) the risks and benefits to the general public, including users and nonusers of tobacco products; (2) the increased or decreased likelihood that existing users of tobacco products will stop using the products; and (3) the increased or decreased likelihood that those who do not use tobacco products will start using the new products. FDA considers the net effect on tobacco-related behavior changes within the whole population for initiation, cessation, and relapse. Consequently, FDA is concerned with effectively forecasting the public health effects of potential changes in tobacco standards and other policies.

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4The Tobacco Control Act gives FDA the ability to regulate other tobacco products through rule making. In early 2014, FDA proposed a “deeming” rule that would extend the agency’s authority to cover other products that meet the definition of a tobacco product, such as e-cigarettes, cigars, pipe tobacco, waterpipe (hookah) tobacco, and nicotine gels and dissolvables. If passed, FDA would be able to regulate these newly deemed products in ways consistent with currently regulated tobacco products.

Suggested Citation:"1 Introduction." Institute of Medicine. 2015. Assessing the Use of Agent-Based Models for Tobacco Regulation. Washington, DC: The National Academies Press. doi: 10.17226/19018.
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COMPUTATIONAL MODELING OF TOBACCO USE

Computational modeling is among the many tools that can be used to inform and evaluate tobacco control policies. In the past, population-based aggregate models and microsimulations of tobacco control have been used to model the effect of tobacco control policies, trends in smoking prevalence, and health outcomes (HHS, 2014; Holford et al., 2014; Levy et al., 2006; Mendez et al., 1998; Moolgavkar et al., 2012; Orme et al., 2001; Tengs et al., 2001). (See Box 1-2 for a brief summary of tobacco control modeling efforts to date; for a more detailed overview see Appendix 15.1 of the 2014 Surgeon General’s report.) Currently, FDA is exploring other modeling approaches, including ABMs, to forecast effectively the public health effects of potential changes in tobacco standards and other policies. ABM is a type of computer simulation that studies complex systems by exploring how individual elements (agents) of a system behave as a function of individual characteristics, and interactions with each other, and with the environment. Each agent interacts with other agents based on a set of rules and within an environment specified by the modeler; these interactions lead to a set of specific outcomes, some of which may be unexpected. (See Chapter 3 and Appendix A for a detailed discussion of ABMs.) Because ABMs can be used to explore the potential impact of policies and interventions in dynamic social and physical environments, they may be a useful tool to aid in decision making among policy makers. ABMs have been used to examine other public health interventions and policies, such as for infectious diseases (Epstein et al., 2007; Lee et al., 2010) and obesity (Auchincloss et al., 2011; Orr et al., 2014; Zhang et al., 2014), but the use of ABMs has not been fully explored and considered in the tobacco regulatory space (Hammond, 2015).

It should not be inferred that the committee or FDA found that existing models are not useful. Researchers and policy makers have used existing tobacco control models extensively to inform policy decisions and those models continue to be a useful and important tool. This report is meant to grow on the large body of work on tobacco control modeling by exploring how ABMs might be a helpful tool to add to the existing modeling toolkit (see the section titled Why Use Agent-Based Models to Explore Tobacco Use? for a discussion on the role of ABMs for tobacco regulation).

Center for Tobacco Products and Agent-Based Modeling

Through an interagency agreement between CTP and the U.S. Department of Energy, CTP commissioned Sandia National Laboratories5 to

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5The modeling team is part of the Complex Adaptive System of Systems (CASoS) Engineering Initiative.

Suggested Citation:"1 Introduction." Institute of Medicine. 2015. Assessing the Use of Agent-Based Models for Tobacco Regulation. Washington, DC: The National Academies Press. doi: 10.17226/19018.
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BOX 1-2
Brief Overview of Tobacco Control Models

Over the past few decades, many types of models have been used to inform tobacco control research and policy (Chaloupka and Warner, 2000; HHS, 2014). For example, aggregate (also called compartmental or population) models have been used for over 15 years in tobacco control (Holford et al., 2014; Levy et al., 2006; Warner and Mendez, 2012). These models simulate the evolution of populations between non-overlapping categories (e.g., nonsmokers, current smokers, and former smokers). The evolution is dictated by rates either built in or used as inputs for the model, such as initiation and cessation rates and birth and death rates. The models can be used to predict outcomes assuming no change in current smoking rates and trends or else assuming estimated changes in smoking rates and trends. They are also useful for comparing rates and trends after a policy has been implemented with a set of counterfactual data (that is, a comparison between what actually happened and what would have happened in the absence of the intervention) (Holford et al., 2014).

Some tobacco control aggregate models have been used to examine the effects of policies on smoking prevalence, cessation, quit attempts, and other measures of tobacco use in a population. Several of these model scenarios were initially validated with national-level data and have since been adapted to look at individual states or other countries. Examples of tobacco control aggregate models include the University of Michigan Tobacco Prevalence and Health Effects Model and the SimSmoke model, which have been used to look at potential policies, such as an analysis carried out for FDA of the ramifications of a menthol ban. Other models have explored the cost-effectiveness of smoking interventions (BENESCO model), the health-related economic impact of smoking (SAMMEC model), and various health outcomes from smoking (CANSAVE, CISNET’s [Cancer Intervention and Surveillance Modeling Network’s] Yale Lung Cancer model) (HHS, 2014).

Microsimulations, which model at the individual level, have also been used to study tobacco control. These models have quantified the impact of tobacco control on lung cancer mortality and smoking- related mortality in the United States over the past few decades, contributing to the advancement of lung cancer screening strategies and public health research (de Koning et al., 2014; McMahon et al., 2014; Moolgavkar et al., 2012). For example, to model the natural history of lung cancer, six independent microsimulation models were developed as part of CISNET (McMahon et al., 2012).

develop an ABM that could help FDA understand the potential impacts of a variety of policies on population health. The modeling efforts by Sandia National Laboratories under this agreement include population health models that aim to help forecast potential long-term impacts on prevalence, morbidity, and mortality for the population in the United States and various other types of models, including an ABM.

Suggested Citation:"1 Introduction." Institute of Medicine. 2015. Assessing the Use of Agent-Based Models for Tobacco Regulation. Washington, DC: The National Academies Press. doi: 10.17226/19018.
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The ABM being developed by Sandia National Laboratories for FDA is called Social Network Analysis for Policy on Directed Graph Networks, or SnapDragon (Moore et al., in press a,b). The main purpose of the model is to explore the effects of various tobacco policies and interventions such as public education campaigns on opinion and tobacco use within social networks. At the time of this review, SnapDragon was still in an early development stage. The authors have presented the model at professional meetings, but as of publication of this report, no peer-reviewed papers on Snapdragon have been published. (See Chapter 5 for a detailed review of the SnapDragon model.)

Modeling and Policy

Models are used to inform regulatory policy for several reasons. They can better describe complex and poorly characterized problems, but are not “truth generating machines” (NRC, 2007, p. 182). Although policy-relevant computational models are incomplete representations of a small piece of the regulatory environment, this does not mean they lack value. They can provide “other assets to policymaking, such as providing a conceptual map of existing relationships, highlighting new interconnections, and elucidating important uncertainties, all of which significantly aid policy deliberation, but do not replace it” (Wagner et al., 2010, p. 295). These models can also build theory inductively or deductively, or both; guide future data collection by pinpointing unknowns and seeing which appear to matter; explicitly inform intervention design by anticipating consequences; and integrate data that are scattered across different sources and use the interaction of the data informatively (Epstein, 2008). Because of these capabilities, models can be used as one piece of the evidence base to inform the design of future policies, evaluate the effects of current or past policies, and identify key leverage points and opportunities for policy making.6 As will be discussed later in this report, to inform policy effectively, policy makers need to understand the level of model uncertainty, what the model does and does not forecast, to what extent the model is suitable for the question or process under study, and how the model fits within the body of available evidence.

Report Contents

To address its statement of task, the committee reviews and discusses the complex environment in which tobacco control policies are created and how ABMs could be a useful tool to assist in tobacco control policy deci-

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6See Appendix A for a comprehensive discussion on using ABMs to inform policy.

Suggested Citation:"1 Introduction." Institute of Medicine. 2015. Assessing the Use of Agent-Based Models for Tobacco Regulation. Washington, DC: The National Academies Press. doi: 10.17226/19018.
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sions (Chapter 2), reviews the structure and implications of using ABMs to inform policy decisions (Chapter 3), develops evaluation criteria for the review of ABMs (Chapter 4), and illustrates the evaluation criteria with an evaluation of the SnapDragon model (Chapter 5). Where applicable, parts of this evaluation framework are exemplified by reviewing other relevant models to illustrate how the framework is used. This report focuses on tobacco policies that fall within the realm of FDA’s purview, but it is not intended to discourage or ignore modeling or related policy efforts by others, such as other governmental and nongovernmental organizations, states, localities, or other policy scientists, but rather to best address the current needs of the report sponsor. In Chapter 6 the committee discusses inputs for ABMs for tobacco control, and makes recommendations for future implementation of ABMs at CTP.

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Suggested Citation:"1 Introduction." Institute of Medicine. 2015. Assessing the Use of Agent-Based Models for Tobacco Regulation. Washington, DC: The National Academies Press. doi: 10.17226/19018.
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Suggested Citation:"1 Introduction." Institute of Medicine. 2015. Assessing the Use of Agent-Based Models for Tobacco Regulation. Washington, DC: The National Academies Press. doi: 10.17226/19018.
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Suggested Citation:"1 Introduction." Institute of Medicine. 2015. Assessing the Use of Agent-Based Models for Tobacco Regulation. Washington, DC: The National Academies Press. doi: 10.17226/19018.
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Suggested Citation:"1 Introduction." Institute of Medicine. 2015. Assessing the Use of Agent-Based Models for Tobacco Regulation. Washington, DC: The National Academies Press. doi: 10.17226/19018.
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Suggested Citation:"1 Introduction." Institute of Medicine. 2015. Assessing the Use of Agent-Based Models for Tobacco Regulation. Washington, DC: The National Academies Press. doi: 10.17226/19018.
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Suggested Citation:"1 Introduction." Institute of Medicine. 2015. Assessing the Use of Agent-Based Models for Tobacco Regulation. Washington, DC: The National Academies Press. doi: 10.17226/19018.
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Suggested Citation:"1 Introduction." Institute of Medicine. 2015. Assessing the Use of Agent-Based Models for Tobacco Regulation. Washington, DC: The National Academies Press. doi: 10.17226/19018.
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Suggested Citation:"1 Introduction." Institute of Medicine. 2015. Assessing the Use of Agent-Based Models for Tobacco Regulation. Washington, DC: The National Academies Press. doi: 10.17226/19018.
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Suggested Citation:"1 Introduction." Institute of Medicine. 2015. Assessing the Use of Agent-Based Models for Tobacco Regulation. Washington, DC: The National Academies Press. doi: 10.17226/19018.
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Tobacco consumption continues to be the leading cause of preventable disease and death in the United States. The Food and Drug Administration (FDA) regulates the manufacture, distribution, and marketing of tobacco products - specifically cigarettes, cigarette tobacco, roll-your-own tobacco, and smokeless tobacco - to protect public health and reduce tobacco use in the United States. Given the strong social component inherent to tobacco use onset, cessation, and relapse, and given the heterogeneity of those social interactions, agent-based models have the potential to be an essential tool in assessing the effects of policies to control tobacco.

Assessing the Use of Agent-Based Models for Tobacco Regulation describes the complex tobacco environment; discusses the usefulness of agent-based models to inform tobacco policy and regulation; presents an evaluation framework for policy-relevant agent-based models; examines the role and type of data needed to develop agent-based models for tobacco regulation; provides an assessment of the agent-based model developed for FDA; and offers strategies for using agent-based models to inform decision making in the future.

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