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Opioid overdose deaths remain a major public health crisis. We used a system dynamics simulation model of the U.S. opioid-using population age 12 and older to explore the impacts of 11 strategies on the prevalence of opioid use disorder (OUD) and fatal opioid overdoses from 2022 to 2032. These strategies spanned opioid misuse and OUD prevention, buprenorphine capacity, recovery support, and overdose harm reduction. By 2032, three strategies saved the most lives: (i) reducing the risk of opioid overdose involving fentanyl use, which may be achieved through fentanyl-focused harm reduction services; (ii) increasing naloxone distribution to people who use opioids; and (iii) recovery support for people in remission, which reduced deaths by reducing OUD. Increasing buprenorphine providers’ capacity to treat more people decreased fatal overdose, but only in the short term. Our analysis provides insight into the kinds of multifaceted approaches needed to save lives.

The U.S. opioid epidemic has caused substantial harm for over 20 years. Policy interventions have had limited impact and sometimes backfired. Experts recommend a systems modeling approach to addres...

Policy Points Interventions in a regional system with intertwined threats and costs should address those threats that have the strongest, quickest, and most pervasive cross-impacts. Instead of focusing on an individual county's apparent shortcomings, a regional intervention portfolio can yield greater results when it is designed to counter those systemic threats, especially poverty and inadequate social support, that most undermine health and well-being virtually everywhere. Likewise, efforts to reduce smoking, addiction, and violent crime and to improve routine care, health insurance, and youth education are important for most counties to unlock both short- and long-term potential. CONTEXT Counties across the United States must contend with multiple, intertwined threats and costs that defy simple solutions. Decision makers face the necessary but difficult task of prioritizing those interventions with the greatest potential to produce equitable health and well-being. METHODS Using County Health Rankings data for a predefined peer group of 39 urban US counties, we performed statistical regressions to identify 37 cross-impacts among 15 threats to health and well-being. Adding appropriate time delays, we then developed a dynamic model of these cross-impacts and simulated each of the counties over 20 years to assess the likely impact of 12 potential interventions-individually and in a combined portfolio-for three outcomes: (1) years of potential life lost, (2) fraction of adults in fair or poor health, and (3) total spending on urgent services. FINDINGS The combined portfolio yielded improvements by year 20 that are considerably greater than those at year 5, indicating that the time delays have a major effect. Despite the wide variation in threat levels across counties, the list of top-ranked interventions is strikingly similar. Poverty reduction and social support were the most highly ranked interventions, even in the shorter term, for all outcomes in all counties. Interventions affecting smoking, addiction, routine care, health insurance, violent crime, and youth education also were important contributors to some outcomes. CONCLUSIONS To safeguard health and well-being in a system dominated by tangled threats and costs, the most important priorities for a county cannot be simply inferred from a profile of its relative strengths and weaknesses. Two interventions stood out as the top priorities for almost all the counties in this study, and six others also were important contributors. Interventions directed toward these priority areas are likely to yield the greatest impact, irrespective of the county's specifics. A significant concentration of resources in a regional portfolio therefore ought to go to these strongest contributors for equitable health and well-being.

Adolescent overweight and obesity (AdOWOB) in Europe has proven to be a persistent and complex problem, and appropriate systems methods may help in evaluating potential policy options. This paper describes the development of a system dynamics model of AdOWOB as part of the EU-funded CO-CREATE project. The model was developed using literature and data from the Health Behavior in School-Aged Children (HBSC) study across 31 European countries. We identified 10 HBSC variables that were included as direct or indirect drivers of AdOWOB in the dynamic model, seven at the level of the individual, and three related to the social environment. The model was calibrated to 24 separate cases based on four gender and perceived wealth segments for each of the five CO-CREATE countries (The Netherlands, Norway, Poland, Portugal, and the UK) and for Europe overall. Out of 10 possible intervention points tested, exercise, fruit, life dissatisfaction, school pressure, and skipping breakfast were identified as the top five most influential ones across the 24 cases. These model-based priorities can be compared with the policy ideas suggested by the CO-CREATE adolescents.

We present a practical guide and step-by-step flowchart for establishing uncertainty intervals for key model outcomes in a simulation model in the face of uncertain parameters. The process starts with Powell optimization to find a set of uncertain parameters (the optimum parameter set or OPS) that minimizes the model fitness error relative to historical data. Optimization also helps in refinement of parameter uncertainty ranges. Next, traditional Monte Carlo (TMC) randomization or Markov Chain Monte Carlo (MCMC) is used to create a sample of parameter sets that fit the reference behavior data nearly as well as the OPS. Under the TMC method, the entire parameter space is explored broadly with a large number of runs, and the results are sorted for selection of qualifying parameter sets (QPS) to ensure good fit and parameter distributions that are centrally located within the uncertainty ranges. In addition, the QPS outputs are graphed as sensitivity graphs or box-and-whisker plots for comparison with the historical data. Finally, alternative policies and scenarios are run against the OPS and all QPS, and uncertainty intervals are found for projected model outcomes. We illustrate the full parameter uncertainty approach with a (previously published) system dynamics model of the U.S. opioid epidemic, and demonstrate how it can enrich policy modeling results.

Integrated assessment models (IAMs) have advanced scientific understanding of mitigation pathways, yet most are not directly accessible to policymakers and other leaders. Here we describe En-ROADS, a publicly available, fully documented online policy simulation model designed to complement IAMs in public outreach. En-ROADS represents the energy-economy-climate system at a globally aggregated level. It has been carefully grounded in the best available science and is calibrated to fit historical data and projections from multiple IAMs across all SSP scenarios. Through an intuitive interface, users choose assumptions, policies, and actions to mitigate GHG emissions, receiving immediate feedback on likely energy, emissions, and climate pathways through 2100, enabling users to explore policies and uncertainties for themselves.

Simple climate models enable not only rapid simulation of a large number of climate scenarios, especially in connection with the integrated assessment models of economy and environment, but also provide chances for outreach and education. En-ROADS, (Energy Rapid Overview and Decision Support)[1], is a publicly available, online policy simulation model designed to complement integrated assessment models for rapid simulation of climate solutions. En-ROADS is a globally aggregated energy-economy-climate model based on a simple climate model, and supports outreach and education about the causes and effects of climate change. It has an intuitive user interface and runs essentially instantly on ordinary laptops and tablets, providing policymakers, other leaders, educators, and the public with the ability to learn for themselves about the likely consequences of energy and climate policies and uncertainties. En-ROADS is a behavioral system dynamics model consisting of a system of nonlinear ordinary differential equations solved numerically from 1990-2100, with a time step of one-eighth year. En-ROADS extends the C-ROADS model, which has been used extensively by officials and policymakers around the world to inform positions of parties to the UNFCCC[2][3]. In En-ROADS’ climate module, the resulting emissions from the energy system, from forestry and land use, and carbon removal technologies, determine the atmospheric concentrations of each GHG, radiative forcing, and climate impacts including global surface temperature anomaly, heat and carbon transfer between the surface and deep ocean, sea level rise, and ocean acidification. It is calibrated to fit historical data of temperature change and carbon cycle elements, as well as the projections within the RCP-SSP framework. Both En-ROADS and C-ROADS are further developed to account for the details of the terrestrial carbon cycle. [1] https://en-roads.climateinteractive.org/scenario.html. [2] Sterman J, Fiddaman T, Franck TR, Jones A, McCauley S, Rice P, et al. Climate interactive: the C-ROADS climate policy model. System Dynamics Review 2013 28 (3): 295–305 [3] Sterman JD, Fiddaman T, Franck T, Jones A, McCauley S, Rice P, et al. Management flight simulators to support climate negotiations. Environmental Modelling & Software 2013, 44: 122-135.

The Rethink Health Dynamics Model represents the complex dynamics of a regional health system in the United States and has been calibrated for more than 10 regions using nationwide and local data. When testing single interventions, the simulated improvement in system performance is often less than desired. By experimenting with combinations of interventions, the authors have identified recurring reasons for underperformance or intervention pitfalls. Here they discuss four common pitfalls and possible ways to overcome each with additional intervention. The pitfalls include (a) trying to cut costs without changing payment incentives; (b) depleting available funds without securing sustainable financing; (c) trying to achieve greater equity through service delivery without building capacity to meet greater demand; and (d) missing the opportunity to achieve multiple goals simultaneously through the use of mutually supporting interventions. The chapter illustrates each pitfall and proposed solution with causal feedback diagrams and simulation output graphs.

In 2014, NASPAA (Network of Schools of Public Policy, Affairs, and Administration) partnered with the Rippel Foundation to hold the first NASPAA Student Simulation Competition, built on the ReThink...

Background and Purpose— Reducing the burden of stroke is a priority for the Veterans Affairs Health System, reflected by the creation of the Veterans Affairs Stroke Quality Enhancement Research Initiative. To inform the initiative’s strategic planning, we estimated the relative population-level impact and efficiency of distinct approaches to improving stroke care in the US Veteran population to inform policy and practice. Methods— A System Dynamics stroke model of the Veteran population was constructed to evaluate the relative impact of 15 intervention scenarios including both broad and targeted primary and secondary prevention and acute care/rehabilitation on cumulative (20 years) outcomes including quality-adjusted life years (QALYs) gained, strokes prevented, stroke fatalities prevented, and the number-needed-to-treat per QALY gained. Results— At the population level, a broad hypertension control effort yielded the largest increase in QALYs (35 517), followed by targeted prevention addressing hypertension and anticoagulation among Veterans with prior cardiovascular disease (27 856) and hypertension control among diabetics (23 100). Adjusting QALYs gained by the number of Veterans needed to treat, thrombolytic therapy with tissue-type plasminogen activator was most efficient, needing 3.1 Veterans to be treated per QALY gained. This was followed by rehabilitation (3.9) and targeted prevention addressing hypertension and anticoagulation among those with prior cardiovascular disease (5.1). Probabilistic sensitivity analysis showed that the ranking of interventions was robust to uncertainty in input parameter values. Conclusions— Prevention strategies tend to have larger population impacts, though interventions targeting specific high-risk groups tend to be more efficient in terms of number-needed-to-treat per QALY gained.

In 2007, Jay (Forrester, 2007) surprised many of us by declaring the field, upon its 50th anniversary, to be on an aimless plateau of superficial and mediocre work. This dismal situation, he said, could be corrected only by breaking away from conventional institutions and doing things on our own. I wrote a rejoinder (Homer, 2007) stating that such a radical break was not necessary or advisable. I stated that if we only concentrated on doing “good, diligent work” within the existing system, often alongside our colleagues in other disciplines, things would work out fine. I gave as an example the progress a group of us had made at the Centers for Disease Control and Prevention (CDC) persuading leaders there that SD was a useful tool for public health planning and strategy. It’s six years later now, and, although good, diligent work is certainly being done, I now too have come to feel some sense of frustration with the field’s lack of progress and success. For one thing, growth of the SD Society has come nearly to a halt, with membership increasing by only 3 percent overall from 2005 to 2012. We see many fresh faces at the annual conference, but with Society attrition running at 22–26 percent per year (2010–2013) most of them are gone within a few years. Moreover, the quality of work presented at the annual conference remains generally sub-par, despite various efforts to address this problem. Two of the award committees I sit on have had difficulty in recent years finding work good enough to honor. It is similarly sad to observe how the large strategic consulting firms, so interested in SD in the 1990s and early 2000s, mostly seem to have backed away in recent years. We are also perennially at risk of losing traction in the public sector. For example, my team working at the CDC has hit roadblocks in the last couple of years. Distrust of SD in some quarters of the agency seems to have gained the upper hand and blocked our ability to publish and pursue new work. It is possible that in a few years our footprints within the institution, which were once deep, will be completely gone. What is happening to our field, and what should we do about it? Some might say that there is no fundamental problem and we’re basically on the right track. Maybe we’re simply observing the inevitable market ups and downs of any analytic field, especially one as subtle and nuanced as ours. If we want to prolong the ups and counteract the downs, we should simply do a better, more vigorous job of promoting our work. Under this view, we should keep quiet about our concerns and be cheerleaders rather than naysayers; we should be bold rather than shy or self-critical.

Leaders across the United States face a difficult challenge choosing among possible approaches to transform health system performance in their regions. The ReThink Health Dynamics Model simulates how alternative scenarios could unfold through 2040. This article compares the likely consequences if four interventions were enacted in layered combinations in a prototypical midsize US city. We estimated the effects of efforts to deliver higher-value care; reinvest savings and expand global payment; enable healthier behaviors; and expand socioeconomic opportunities. Results suggest that there may be an effective and affordable way to unlock much greater health and economic potential, ultimately reducing severe illness by 20 percent, lowering health care costs by 14 percent, and improving economic productivity by 9 percent. This would require combined investments in clinical and population-level initiatives, coupled with financial agreements that reduce incentives for costly care and reinvest a share of the savings to ensure adequate long-term financing.

System dynamicists have long emphasized the use of multiple data sources and multiple methods to estimate parameters and test models. Ideally, one should estimate parameters using data " below the level of aggregation of the model" . For example, in a model of capital investment, one could estimate the length and order of the construction delay directly from data on the construction times for a large sample of relevant projects. Often, however, the needed data are not available. At the other end of the spectrum one can use " whole model estimation" in which the parameters are found by fitting the behavior of the full model to the available aggregate time series data. Whole model estimation, however, often suffers from identification problems. In this 1983 paper Jack Homer describes partial model testing, in which parameters are estimated within a subset of model structure rather than by calibration of the entire model. Homer illustrates with an example from his research on the adoption of new medical innovations, specifically, the cardiac pacemaker. He illustrates the partial model testing process with an important formulation representing how the clinical and research communities carry out and report follow-up data on the safety and efficacy of a medical innovation as it evolves. The paper also illustrates the necessity of careful empirical work to collect new data. Debates over methods to estimate parameters are sterile without the data to implement them. In the context of medical innovation, it would have been plausible to assume that follow-up data evaluating pacemaker efficacy would grow smoothly over time as use expanded, though perhaps with a lag. Not content with such easy assumptions, however, Homer examined every article on pacing published in every issue of the relevant cardiology journals, from the creation of the pacemaker through the (then) present. The work, done years before the advent of the Internet and online databases, was painstaking and time consuming—the journals had to be searched and articles coded by hand. The payoff was a unique dataset documenting the actual dynamics of evaluative reporting. Rather than smooth growth, the data showed a pronounced oscillation in the publication of evaluative studies, even though other data, which Homer also assembled from original sources, showed smooth growth in adoption, clinical indications and pacemaker use. Homer's model generates the same oscillation endogenously, and the partial model tests provide robust estimates of the parameters governing the institutional processes (such as research and publication delays) and behavioral decision rules (such as the decision by researchers and clinicians to initiate new follow-up studies) that determine the existence, period, and amplitude of the cycle. The structure identified through this process was not only important to accurately model the evolution of the pacemaker, but has important policy implications still relevant today. All modelers should follow Homer's example and put in the hard work to generate, from primary sources, the data needed to estimate the important parameters and relationships in our models. John Sterman Homer J. 1983. Partial-model testing as a validation tool for system dynamics. In Proceedings of the 1983 International System Dynamics Conference. System Dynamics Society, Chestnut Hill, MA; 919–932. Abstract This paper discusses an approach to model refinement that involves testing the behavior of individual pieces of a model in response to empirical input data for comparison with empirical output data. Partial-model tests should be used for selecting formulations or estimating parameters only when appropriate case-specific or logical information is not available for this purpose. The smaller the model components used for partial-model testing, the more likely it is that the model will prove useful for anticipating events outside historical experience and the less likely it is that observed behavior will be incorrectly attributed to certain relationships or parameters. Thus, from the standpoint of structural validity, partial-model testing is an improvement over whole-model testing for the purpose of structural adjustment. The paper presents a detailed example of partial-model testing in the context of a generic model of the evolving use of a new medical technology. Specifically, the technique is used for adjusting and validating a model subsystem that can explain why the reporting of clinical information on cardiac pacemakers has been marked by regular oscillations over time. Copyright © 2012 System Dynamics Society.

Objective: To assess the usefulness of a national and a local system dynamics model of cardiovascular disease to planning and funding decision makers. Methods: In an iterative process, an existing national model was populated with local data and presented to stakeholders in Counties Manukau, New Zealand. They explored the model's plausibility, usefulness and implications. Data were collected from 30 people using questionnaires, and from field notes and interviews; both were thematically analysed. Results: Potential users readily understood the model and actively engaged in discussing it. None disputed the overall model structure, but most wanted extensions to elaborate areas of specific interest to them. Local data made little qualitative difference to data interpretation but were nevertheless considered a necessary step to support confident local decisions. Conclusion: Some limitations to the model and its use were recognised, but users could allow for these and still derive use from the model to qualitatively compare decision options. Implications: The system dynamics modelling process is useful in complex systems and is likely to become established as part of the routinely used suite of tools used to support complex decisions in Counties Manukau District Health Board.

The primary goal of this workshop was to identify the most appropriate method to estimate the potential effect of reduction in sodium consumption on mortality. Difficulty controlling hypertension at the individual level has motivated international, federal, state, and local efforts to identify and implement population-wide strategies to better control this problem; reduction of sodium intake is one such strategy. Published estimates of the impact of sodium consumption on mortality have used different modeling approaches, effect sizes, and levels of sodium consumption, and thus their estimates of preventable deaths averted vary widely, and are not comparable. In response to this problem, the Centers for Disease Control and Prevention’s Division for Heart Disease and Stroke Prevention (DHDSP) convened and facilitated a workshop to examine different methods of estimating the effect of sodium reduction on mortality. The panelists agreed that any of the methodologies presented could provide reasonable estimates, and therefore discussion focused on challenges faced by all methods. The panel concluded that future sodium modeling efforts should generate multiple estimates employing the same scenarios and effect sizes while using different modeling techniques; in addition, future efforts should include outcomes other than mortality (morbidity, costs, and quality of life). Varying reductions in sodium should be modeled at the population level over different time intervals. In an effort to better address some of the uncertainties highlighted by this workshop, the panelists are currently considering developing multiple estimates in a collaborative manner to clarify the potential impact of population-based interventions to reduce sodium consumption.

Proposals to improve the US health system are commonly supported by models that have only a few variables and overlook certain processes that may delay, dilute, or defeat intervention effects. We use an evidence-based dynamic simulation model with a broad national scope to analyze 5 policy proposals. Our results suggest that expanding insurance coverage and improving health care quality would likely improve health status but would also raise costs and worsen health inequity, whereas a strategy that also strengthens primary care capacity and emphasizes health protection would improve health status, reduce inequities, and lower costs. A software interface allows diverse stakeholders to interact with the model through a policy simulation game called HealthBound.