Reducing opioid use disorder and overdose deaths in the United States: A dynamic modeling analysis.

Opioid overdose deaths remain a major public health crisis. We used a system dynamics simulation model of the U.S.

Modeling the evolution of the US opioid crisis for national policy development.

The opioid crisis is a major public health challenge in the United States, killing about 70,000 people in 2020 alone. Long delays and feedbacks between policy actions and their effects on drug-use behavior create dynamic complexity, complicating policy decision-making. In 2017, the National Academies of Sciences, Engineering, and Medicine called for a quantitative systems model to help understand and address this complexity and guide policy decisions.

A dynamic model of the opioid drug epidemic with implications for policy.

: The U.S. opioid epidemic has caused substantial harm for over 20 years.

Which Priorities for Health and Well-Being Stand Out After Accounting for Tangled Threats and Costs? Simulating Potential Intervention Portfolios in Large Urban Counties.

Unlabelled: 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.

Combined Regional Investments Could Substantially Enhance Health System Performance And Be Financially Affordable.

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.

Using simulation to compare established and emerging interventions to reduce cardiovascular disease risk in the United States.

Introduction: Computer simulation offers the ability to compare diverse interventions for reducing cardiovascular disease risks in a controlled and systematic way that cannot be done in the real world.

Methods: We used the Prevention Impacts Simulation Model (PRISM) to analyze the effect of 50 intervention levers, grouped into 6 (2 x 3) clusters on the basis of whether they were established or emerging and whether they acted in the policy domains of care (clinical, mental health, and behavioral services), air (smoking, secondhand smoke, and air pollution), or lifestyle (nutrition and physical activity). Uncertainty ranges were established through probabilistic sensitivity analysis.

Strategic planning to reduce the burden of stroke among veterans: using simulation modeling to inform decision making.

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.

Using simulation to compare 4 categories of intervention for reducing cardiovascular disease risks.

The Prevention Impacts Simulation Model (PRISM) projects the multiyear impacts of 22 different interventions aimed at reducing risk of cardiovascular disease. We grouped these into 4 categories: clinical, behavioral support, health promotion and access, and taxes and regulation. We simulated impacts for the United States overall and also for a less-advantaged county with a higher death rate.

Modelling the social determinants of health and simulating short-term and long-term intervention impacts for the city of Toronto, Canada.

There is a substantial body of evidence highlighting the importance of the social determinants of health in shaping the health of urban populations in Canada. The low socio-economic status of marginalized, disadvantaged, and precarious populations in urban settings has been linked to adverse health outcomes including chronic and infectious disease, negative health behaviours, barriers to accessing health care services, and overall mortality. Given the dynamic complexities and inter-relationships surrounding the underlying drivers of population health outcomes and inequities, it is difficult to assess program and policy intervention tradeoffs, particularly when such interventions are studied with static models.

A 'whole of system' approach to compare options for CVD interventions in Counties Manukau.

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.

Why behavioral and environmental interventions are needed to improve health at lower cost.

We used a dynamic simulation model of the US health system to test three proposed strategies to reduce deaths and improve the cost-effectiveness of interventions: expanding health insurance coverage, delivering better preventive and chronic care, and protecting health by enabling healthier behavior and improving environmental conditions. We found that each alone could save lives and provide good economic value, but they are likely to be more effective in combination. Although coverage and care save lives quickly, they tend to increase costs.

Analyzing national health reform strategies with a dynamic simulation model.

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 system dynamics model for planning cardiovascular disease interventions.

Planning programs for the prevention and treatment of cardiovascular disease (CVD) is a challenge to every community that wants to make the best use of its limited resources. Selecting programs that provide the greatest impact is difficult because of the complex set of causal pathways and delays that link risk factors to CVD. We describe a system dynamics simulation model developed for a county health department that incorporates and tracks the effects of those risk factors over time on both first-time and recurrent events.

Simulating and evaluating local interventions to improve cardiovascular health.

Numerous local interventions for cardiovascular disease are available, but resources to deliver them are limited. Identifying the most effective interventions is challenging because cardiovascular risks develop through causal pathways and gradual accumulations that defy simple calculation. We created a simulation model for evaluating multiple approaches to preventing and managing cardiovascular risks.

Charting plausible futures for diabetes prevalence in the United States: a role for system dynamics simulation modeling.

Introduction: Healthy People 2010 (HP 2010) objectives call for a 38% reduction in the prevalence of diagnosed diabetes mellitus, type 1 and type 2, by the year 2010. The process for setting this objective, however, did not focus on the achievability or the compatibility of this objective with other national public health objectives. We used a dynamic simulation model to explore plausible trajectories for diabetes prevalence in the wake of rising levels of obesity in the U.

System dynamics modeling for public health: background and opportunities.

The systems modeling methodology of system dynamics is well suited to address the dynamic complexity that characterizes many public health issues. The system dynamics approach involves the development of computer simulation models that portray processes of accumulation and feedback and that may be tested systematically to find effective policies for overcoming policy resistance. System dynamics modeling of chronic disease prevention should seek to incorporate all the basic elements of a modern ecological approach, including disease outcomes, health and risk behaviors, environmental factors, and health-related resources and delivery systems.

Understanding diabetes population dynamics through simulation modeling and experimentation.

Health planners in the Division of Diabetes Translation and others from the National Center for Chronic Disease Prevention and Health Promotion of the Centers for Disease Control and Prevention used system dynamics simulation modeling to gain a better understanding of diabetes population dynamics and to explore implications for public health strategy. A model was developed to explain the growth of diabetes since 1980 and portray possible futures through 2050. The model simulations suggest characteristic dynamics of the diabetes population, including unintended increases in diabetes prevalence due to diabetes control, the inability of diabetes control efforts alone to reduce diabetes-related deaths in the long term, and significant delays between primary prevention efforts and downstream improvements in diabetes outcomes.

Systems modeling in support of evidence-based disaster planning for rural areas.

The objective of this communication is to introduce a conceptual framework for a study that applies a rigorous systems approach to rural disaster preparedness and planning. System Dynamics is a well-established computer-based simulation modeling methodology for analyzing complex social systems that are difficult to change and predict. This approach has been applied for decades to a wide variety of issues of healthcare and other types of service capacity and delivery, and more recently, to some issues of disaster planning and mitigation.