FRAMING HEALTH MATTERS

Mainstreaming Modeling and Simulation to Accelerate Public Health Innovation Paul P. Maglio, PhD, Martin-J. Sepulveda, MD, and Patricia L. Mabry, PhD

Dynamic modeling and simulation are systems science tools that examine behaviors and outcomes resulting from interactions among multiple system components over time. Although there are excellent examples of their application, they have not been adopted as mainstream tools in population health planning and policymaking. Impediments to their use include the legacy and ease of use of statistical approaches that produce estimates with confidence intervals, the difficulty of multidisciplinary collaboration for modeling and simulation, systems scientists’ inability to communicate effectively the added value of the tools, and low funding for population health systems science. Proposed remedies include aggregation of diverse data sets, systems science training for public health and other health professionals, changing research incentives toward collaboration, and increased funding for population health systems science projects. (Am J Public Health. 2014;104:1181–1186. doi:10.2105/ AJPH.2014.301873)

A recent analysis reveals that the global burden of disease in terms of global disability adjusted life years has not improved significantly over the past 2 decades.1 The same analysis shows that there has been a shift in the global burden of disease from communicable to noncommunicable diseases and from premature death to years lived with disability. The burden of disease and the factors contributing to it vary greatly among countries and within countries, regions, and cities. Local interventions and practices are required to address the needs and underlying determinants within particular communities. Although there is a large and ever-growing body of evidence on effective population health strategies, that literature often does not systematically take into account the dynamic socioecological and cultural context of a specific community or the policies that are already in place. Moreover, resources are always limited, which frequently causes strategies that have been effective in one environment to be unaffordable or unsustainable in another. Nor does it make sense to randomly pull strategies from a grab bag of evidence-based policies and interventions. From a scientific viewpoint, conducting experiments to see what would be most effective

in a particular community, especially the combined effects of different policies and their impact over time, is preferable. Yet the real world presents practical barriers that impede an experimental approach. For example, it is impossible to examine the counterfactual. That is, we cannot implement a new policy, wait to see what the results were, then go back to a time before the policy was implemented and try a different policy to see how it would stack up against the first one. However, this exact type of experiment can be done by working in a virtual environment (i.e., a computersimulated model of the real world). In this essay, we invite readers to explore the utility of dynamic modeling and simulation approaches to addressing population health challenges and we lay out a roadmap for facilitating the uptake of these methods. By “dynamic modeling and simulation” we are referring to a variety of mathematical and computational modeling methods including system dynamics modeling, agent-based modeling, Markov modeling, discrete event modeling, microsimulation, and more. These methods share the ability to quantitatively represent, in silico, the behavior of a system and its components over time. Major population health challenges, such as cardiovascular diseases, diabetes, and cancer,

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are rooted in complex interactions among multiple variables, including people, genes, and social, economic, and physical environments.2,3 In fact, the variables are often subsystems containing a whole level of complexity themselves. For example, each of the following factors can be thought of as a system within a broader interdependent system of human health: food, agriculture, transportation, education, and health care delivery systems. Their interactions can result in outcomes that are difficult to understand or predict, and that often cannot be traced reliably to the behavior of any single component. Prevalent approaches to solving complex problems often involve decomposing them into component parts and analyzing some subset individually to identify candidate interventions. For example, many proposed solutions to noncommunicable diseases in high-income countries have focused primarily on the health care delivery system. In the United States, population health challenges resulting from noncommunicable diseases are largely being addressed by increasing access to health services through government-subsidized health insurance, delivery system expansion of primary care, improvements in the quality of clinical practice, and new performance-sensitive payment mechanisms.4 Although health care delivery system reforms are essential and may appear poised to be cost-effective, it is simply not known whether the reforms will improve population health. For example, essential components of health maintenance, such as exercise, better nutrition and weight management, medication effectiveness, and stress management, can be thwarted by outdoor public safety issues in neighborhoods, prohibitive costs of fresh fruits and vegetables, housing with smokers in indoor environments, and time demands on patients from work or elder- or child-care responsibilities. Modeling and simulation-based insight into population health scenarios resulting from

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multisectoral changes could improve decisionmaking in policy development, planning, and implementation. One way of improving decision-making is by identifying unintended consequences of a program or policy (e.g., the impact on the health sector resulting from interventions in the transportation sector). A second way is by revealing the likely dynamics of the system and how impact on variables of interest plays out over time (e.g., in some cases an intervention produces a worsening of circumstances before improvement). A third way is by exposing the individual and the combined effects of programs or policies (e.g., clean indoor air laws might prompt more people to quit smoking, and simultaneously offering medication and other cessation services might help more people to successfully quit). Dynamic modeling and simulation methods are well suited to discovering potential outcomes from significant system disruptions. Standard econometric, epidemiological, and biostatistical methods have demonstrated value in assessing effects of social determinants of health, but these methods have inherent limitations that dynamic modeling and simulations can help address. For example, they often assume unrealistic constraints on relationships among variables (i.e., statistical independence), on samples (representativeness), and on distributions (i.e., Gaussian). In addition, such approaches are not well equipped to measure nonlinear and bidirectional relationships, or relationships with delayed effects. Modeling and simulation methods may be combined with more traditional and prevalent methods to gain insights when new laws and policies create real-world experiments. For example, these methods can help disclose interactions among many parties likely to be affected by changing payment schemes in different types of health care models,5 such as health organizations that assume risk for cost and clinical outcomes (so-called “accountable care organizations”). Modeling and simulation could be used to identify optimum payment approaches for accountable care organizations and other care delivery systems by using models for patient, provider, supplier, and payer behavior, along with models for key medical conditions such as diabetes.6,7 The need for improved intelligence in complex systems is also increasing in cities and

states. Plagued by the impact of increasing health care costs on state and municipal budgets, decreasing family incomes, and increasing labor cost for employers, state and local governments are seeking to mitigate causes of poor health in schools, neighborhoods, and housing, and from food sources and pollution.8---10 In addressing upstream determinants of health, city officials have learned to take account of system dynamics to understand potential outcomes of interventions. For example, the impact of changes to the nutritional content of school food sources is dependent on the price and availability of low-value food sources and snacks in surrounding food outlets. These outlets in turn are incentivized to stock packaged low-value foods because of their lower cost per unit of space compared with fresh fruits and vegetables.11 Cities and states are interested in better understanding city systems dynamics affecting health to identify leverage points for improved efficiency and effectiveness.12 Dynamic modeling and simulation are essential tools to achieve these objectives. Complex system problems in health will often require cross-sector solutions. Modeling and simulation can help decision-making for such problems by capturing salient aspects of a problem, using data of different types and from multiple domains, handling the computationally intensive task of calculating the impact of multiple interacting relationships, and playing out the potential outcomes in policy scenarios or potential interventions. Modeling and simulation can also help examine trade-offs between policy options and assess the time horizon over which policies may have an impact.13 The current state of modeling and simulation is such that they are most useful for computationally intensive tasks that far exceed human capabilities. However, models are not good at nuance and judgment. Rather, models are best used to help inform decision-making, leaving the actual decisions to people.

OBESITY AS AN EXAMPLE OF A COMPLEX HEALTH SYSTEM PROBLEM Obesity and overweight affected nearly one third of the world’s population in 2008, including 2 billion adults and 170 million children.14 Obesity is a risk factor for many adverse

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health conditions including diabetes, cardiovascular disease, cancer, obstructive sleep apnea, and osteoarthritis.15 It is a leading cause of premature morbidity and mortality and follows a social gradient in which those with the highest socioeconomic status are least affected and those with the lowest socioeconomic status are the most overweight and obese.16 Differences across race and gender are observed in the United States, with non-Hispanic Black women older than 40 years having the highest prevalence of overweight or obesity at 80%.17 Obesity is a good example of a complex health system problem because its occurrence involves many different interdependent but autonomous systems (biological, behavioral, social, environmental) with multiple different mechanisms (e.g., genetics, childhood and prenatal influences, social networks), linkages, and feedback.18 Obesity occurs within a complex web of biological, social, economic, and environmental influences, as evidenced by strong associations with diverse factors beyond energy consumption and expenditure,19 including birth weight and breastfeeding in infancy,20 “food deserts” in communities,21 school food policies,22 agricultural policies and production practices,23 and neighborhood characteristics such as walkability and commercial zoning.24 Recognition of obesity as a complex systems issue has prompted multisectoral initiatives to change unhealthy, obesity-promoting environments into healthy ones.19 For example, in 2010, the City of Philadelphia, Pennsylvania, launched Get Healthy Philly, a multisectoral initiative designed to create synergistic changes promoting healthy behaviors where residents live, work, learn, shop, and play.25 Get Healthy Philly targets community food access and affordability, tobacco control, work sites, schools and afterschool settings, walkability and bikeability changes to the built environment, and partnerships with retailers, employers, the media, health care, and community groups. Public policies support programs such as tobacco control taxation and retail access, food quality in schools, and pedestrian- and bicycle-friendly development in new zoning regulations. Process outcomes reported by Philadelphia for the initiative are noteworthy, such as providing 100 000 low-income residents with new access to affordable fresh produce at corner stores and markets, achieving

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the highest percentage of bicycle commuters (1.8%) of the 10 largest US cities, and increasing healthy breakfast participation in 97 schools by 35 000.25 Dynamic modeling and simulation were not part of the planning and are not yet part of the ongoing assessment of this bold initiative in Philadelphia. These would have been helpful in determining which combination of the policies considered would be the most useful and cost-effective for reducing obesity prevalence over the desired timeframe. That is, even if the results demonstrate that the combination of policies and interventions used succeeded, without modeling there is no way to know which policies are actually producing a significant effect and which are having minimal or no effect. In addition, modeling and simulation could be used to identify data gaps to address that would improve confidence in the model’s output. Funding, data availability and quality, computational and analytic capacity, and familiarity with the methods and their utility have affected the ability to use modeling and simulation in Philadelphia and in other communities in the past. Technological, social, and regulatory changes will help to remove many of these barriers to broader deployment of these methods.

PROMOTERS AND INHIBITORS OF MODELING AND SIMULATION USE There are excellent examples of the application of dynamic modeling and simulation to population health challenges such as infectious disease transmission,26 chronic illness,27---29 drug abuse,30 obesity,31 diabetes,32 tobacco control,33,34 and health systems research.35,36 Nonetheless, these methods have yet to be broadly adopted as mainstream tools in population health planning and policymaking. Several recent trends have the potential to accelerate the use of modeling and simulation in health policy and planning. First, technical obstacles to the generation and use of very large and heterogeneous data sets are being overcome. Mobile and digital technologies are reducing barriers to data collection by substituting sensors (e.g., location, motion, video, chemical), including embedding them in smart phones and other personal digital devices, for more expensive labor, reducing the time required for data gathering. Standards for

creating and sharing data are reducing complexities related to data collected in disparate formats or using different terms or vocabularies. Better technologies for storing, maintaining, and archiving data are improving the ability to access, select, and use multiple or large data sets. Advances in computing speed and power provide new abilities to model large, complex systems ranging from proteins37 to brains38 to social systems.39 Second, a positive trend promoting the need for greater use of modeling and simulation is increased recognition that these methods have an important role in population health management and the development of cities. There is a limited but growing history of effective use of modeling and simulation in the health policy context. Researchers modeled how proactive policies (e.g., vaccination) and reactive policies (e.g., school closings) would influence the timeline and severity of a simulated H1N1 influenza epidemic.40 The tobacco control strategic plan put forth by the US Department of Health and Human Services relied on modeling to make effective recommendations.41,42 Modeling and simulation helped the World Health Organization shift its policy on polio from control to eradication.43 Modeling and simulation of the severe acute respiratory syndrome (SARS) epidemic helped show the importance of social and behavioral factors (such as the role of fear and self-imposed quarantines on travel) in the spread of the disease, leading to better policies to reduce the impact of epidemics and avert unintended consequences, such as disruption of commerce.44 Third, another factor fueling interest in modeling and simulation is the increasing urbanization of the world’s population and the complex multisectoral challenges it presents to governments, communities, and the private sector. In 2011, 52% of the world’s population was estimated to live in cities and this proportion is projected to grow to 72% by 2050.45 Municipal and other sectoral leaders are increasingly realizing that the management of infrastructure, human and social services, economic development, and resource allocation are linked and interdependent.46 Cities are seeking improved insights into the dynamics of these interactions and relationships to make better decisions.47 Modeling and simulation

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are critical tools for supporting this need for better intelligence for urban planning and management. Fourth, a trend conducive to greater diffusion of modeling and simulation methods is the growing interest in deriving new value from the sheer amount of data collected today and projected to be collected in the near term.48 It is estimated that today as much information is collected in 2 days as was collected in all human history up to the year 2003, and that the generation of new information will grow by 40% or more per year.49 There is growing interest in funding development of new modeling and simulation techniques for management and analysis of huge amounts and types of data (“big data”) to solve scientific and social problems.50 These uses of massive data will be challenged by technical, methodological, social, and legal considerations. Massive data analytics requires innovation in storage and computing systems design; methodological issues such as sampling and aggregation bias, data standardization, and data measurement error will need to be addressed; care will be required to ensure that a systems view of problems drives data collection and not the reverse; and data security, privacy, and propagation issues remain to be resolved. Broader application of modeling and simulation in health must overcome important inhibitors to its diffusion and use. First, awareness of the benefits and limitations of these tools must be established to create realistic expectations. Like any mathematical technique, modeling and simulation has data quality limitations. The value of dynamic modeling and simulation is not to produce specific predictions, but to enable forecasting that produces a range of likely outcomes and insight into what dynamics and factors drives them. Modeling and simulation arrays the “plausible futures” or scenarios (“what ifs”) that may result from changes introduced into complex systems. These forecasts come with some degree of uncertainty, which can be quantified, with near-term forecasts generally more accurate than long-term forecasts. Related to lack of understanding of modeling and simulation on the part of the end user is the difficulty modelers often have communicating with policymakers. One reason may be that policymakers are more familiar with other

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methods and another may be that multiple parties often must collaborate to build a model to address a specific policy question. The need for effective interdisciplinary collaboration is essential in modeling and simulation. Its absence is a formidable inhibitor to effective use. Interdisciplinary research and cross-domain communication are difficult. Bringing together disparate groups of modelers, content area experts, policymakers, and community groups can be challenging because of differences in theoretical and conceptual orientation, terminology, and disciplinary practices. Furthermore, with the possible exception of public health, science related to health is largely organized to reward and incent deep-domain expertise much more than multidisciplinary collaborations. Incentives for interdisciplinary research, including the creation of multisectoral data repositories for population health systems research, federal funding for interdisciplinary research, publication in journals outside one’s home discipline, and interdisciplinary career paths, are weak. For these reasons, a recent Institute of Medicine report recommended that the National Center for Health Statistics be recommissioned and funded to modernize national data sets to include information related to social and environmental determinants of health and to develop population health analytic tools including modeling and simulation.51 Modeling and simulation has been used effectively in the study of important population health issues involving infectious diseases such as influenza and HIV/AIDS, tobacco control, diabetes, and obesity.52 However, awareness, use, and funding of modeling and simulation in population health has been limited. There are no national data on funding for modeling and simulation in population health and exposure to these tools is not a routine component of the education of health professionals. In contrast, these methods have an established history in many other disciplines, including engineering, climate-related sciences, operations research, physical sciences, biological sciences, and increasingly in medical technologies.52 At the national level in the United States, there is some movement to advance the support and use of these technologies for population health. For example, the National Institutes of Health supports training in systems science

for young scientists in the Institute for Systems Science and Health,53 and has invested in modeling and simulation in select areas, including cancer,54 infectious diseases,26 and obesity.55 It has also collaborated with other federal agencies to support multilevel modeling approaches and funding across scientific and policy domains in the Interagency Modeling and Analytics Group.56 Notwithstanding these efforts and that of others at the Food and Drug Administration and the Centers for Disease Control and Prevention, insufficient funding and training are inhibitors to greater development and use of modeling and simulation for population health.

ADVANCING MODELING AND SIMULATION IN POPULATION HEALTH There are several challenges unrelated to technology requirements that must be overcome for dynamic modeling and simulation to become a core component of the toolkits of population health strategists, planners, and policymakers. First, compelling value must be demonstrated to decision-makers and users from appropriate use of these tools. Application of dynamic modeling and simulation to acute complex problems confronted by these parties provides opportunities for demonstrating value. Some examples are modeling and simulation applied to crisis and emergency response systems, traffic and transportation systems optimization, or to health care system inefficiencies such as patient throughput bottlenecks or waste from overuse of medical technologies. Good models directed at painful problems for leaders and enterprises provide a low-risk, short cycle-time mechanism for testing different designs in a virtual world with few negative consequences. Second, systematic efforts involving multiple parties are needed to eliminate the major inhibitors to the broader application of modeling and simulation in population health. We must increase exposure to modeling and simulation methods in education and training of scientists, business leaders, urban planners, administrators, and population health professionals. Modeling and simulation requires cross-disciplinary collaboration, and effective team skills must also be developed in institutions of higher learning, government, and

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research organizations. Funding for modeling and simulation must be increased both by government and the private sector, particularly the health care industry. Public---private partnership models for funding modeling and simulation can be modified for use in population health. For example, the State of Virginia formed a multisectoral collaborative for modeling and simulation directed at economic development that includes state agencies, academic institutions, and industry. Virginia used modeling and simulation to address important problems ranging from storm-surge management involving the Chesapeake Bay to housing market dynamics in down economies to medical technology development.57 Such multisectoral partnerships could be extended to examine approaches to population health challenges. Third, concrete steps could be taken to implement recommendations from previous studies regarding modeling and simulation in population health, including 1. Implement the 2010 Institute of Medicine recommendation that the US Department of Health and Human Services recommission and fund the National Center for Health Statistics to (1) create a data infrastructure for health including social and environmental determinants that can be used for systems science research including modeling and simulation, and (2) create “free access” modeling and simulation tools for population health research.51 2. Federal government action should be taken on the International Council for Science’s plan for using systems science approaches, such as modeling and simulation, to improve urban planning and decision-making by (1) funding research on multifactorial determinants and manifestations of health and well-being in urban environments, (2) developing new methodologies and identifying data needs and knowledge gaps, (3) building and strengthening scientific capacity, and (4) facilitating communication and outreach.58 Fourth, modeling and simulation should be supported and incentivized by the Agency for Healthcare Research and Policy and the Patient-Centered Outcomes Research Institute created by the 2010 Affordable Care Act. Modeling and simulation are appropriate tools

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for pursuing quality improvement in care processes and outcomes as well as innovation in the complex health care delivery system. For example, modeling and simulation have applicability to numerous Patient-Centered Outcomes Research Institute priorities such as new models of care, services organization and delivery, operations innovation for improved productivity and quality, and approaches to payment innovation.59

CONCLUSIONS Modeling and simulation are effective tools of complex systems science but are underutilized in population health. They can be useful in helping to discover and understand potential outcomes that may result from interactions of autonomous but interdependent components of a system. Population health problems are often the result of such multisectoral dynamics involving individual biology, social, economic, and environmental systems. Modeling and simulation techniques complement more standard quantitative analytic techniques. When used together, the combination of methods can provide policymakers and other decisionmakers with a deeper understanding of the problems they confront and the effectiveness of the options they have. j

About the Authors Paul P. Maglio is with the School of Engineering, University of California, Merced, and IBM Research, Almaden, CA. Martin-J. Sepulveda is with Health Systems and Policy Research, IBM Research, Yorktown, NY. At the time of the study, Patricia L. Mabry was with the Office of Behavioral and Social Sciences Research, National Institutes of Health, Bethesda, MD and is now with the Office of Disease Prevention, National Institutes of Health, Rockville, MD. Patricia L. Mabry is also a guest editor for this theme issue. Correspondence should be sent to Paul P. Maglio, School of Engineering, University of California, Merced, 5200 N Lake Rd, Merced, CA 95343 (e-mail: pmaglio@ucmerced. edu). Reprints can be ordered at http://www.ajph.org by clicking the “Reprints” link. This article was accepted December 26, 2013. Note. The content of this article is solely the responsibility of the authors and does not necessarily represent the official views of the University of California, IBM, the Office of Behavioral and Social Sciences Research, or the National Institutes of Health.

Contributors All authors contributed equally to this work.

Human Participant Protection Human participation protection was not required because no studies with human or animal participation were done for the work reported this article.

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1186 | Framing Health Matters | Peer Reviewed | Maglio et al.

American Journal of Public Health | July 2014, Vol 104, No. 7

Mainstreaming modeling and simulation to accelerate public health innovation.

Dynamic modeling and simulation are systems science tools that examine behaviors and outcomes resulting from interactions among multiple system compon...
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