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Physician workforce planning must address multiple concerns such as having sufficient numbers and adequate geographic distribution of physicians and pressures for physicians to adapt to new models of care and payment. Though there are national workforce planning tools, planning tools for local areas have been scarce. This article describes a dynamic simulation model developed as a pilot project to support physician workforce planning in 2 metropolitan areas, Cleveland and Albuquerque (February 2014-June 2016). This model serves as a prototype for planning tools that could be used by medical educators and local health systems to project the effect of different policies on physician supply and demand. System dynamics and group model building approaches were used to develop the model with the participation of local stakeholders to create the model's causal structure. The model included determinants of the demand for primary and specialty care for the local population and projected the effects of births and deaths, aging, level of chronic illness present, and migration on demand. Physician supply was disaggregated by primary versus specialty care, age, sex, and work setting and projected based on completions of local residency programs, physician migration in and out of the area, and retirements. Feedback relationships between supply and demand (e.g., adequacy of care affecting the distribution of chronic illnesses, demand for care influencing in- and out-migration of physicians) were also included and had important effects on the results produced by the model. Scenarios were simulated that projected increased demand for care (e.g., through expanded insurance coverage) and increased supply (e.g., through practice incentives to encourage in-migration) and a combination of these. An expanded advanced practice registered nurse and physician assistant capacity scenario was also simulated. In Albuquerque, the combination scenario yielded the greatest increases in local physician supply.

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The continuing HIV pandemic calls for broad, multi-sectoral responses that foster community control of local prevention and care services, with the goal of leveraging high quality treatment as a means of reducing HIV incidence. Service system improvements require stakeholder input from across the care continuum to identify gaps and to inform strategic plans that improve HIV service integration and delivery. System dynamics modeling offers a participatory research approach through which stakeholders learn about system complexity and about ways to achieve sustainable system-level improvements. Via an intensive group model building process with a task force of community stakeholders with diverse roles and responsibilities for HIV service implementation, delivery and surveillance, we designed and validated a multi-module system dynamics model of the HIV care continuum, in relation to local prevention and care service capacities. Multiple sources of data were used to calibrate the model for a three-county catchment area of central Connecticut. We feature a core module of the model for the purpose of illustrating its utility in understanding the dynamics of treatment as prevention at the community level. We also describe the methods used to validate the model and support its underlying assumptions to improve confidence in its use by stakeholders for systems understanding and decision making. The model's generalizability and implications of using it for future community-driven strategic planning and implementation efforts are discussed.

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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.

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BACKGROUND: Despite early childhood caries (ECC) being largely preventable, its repair accounts for a disproportionate share of Medicaid expenditures. In this study, the authors model disease reductions and cost savings from ECC management alternatives. METHODS: The authors apply system dynamics modeling to the New York State Medicaid population of young children to compare potential outcomes of 9 preventive interventions (water fluoridation, fluoride varnish, fluoride toothpaste, medical screening and fluoride varnish application, bacterial transmission reduction, motivational interviewing, dental prevention visits, secondary prevention, and combinations) and the effect of defluoridating New York City. RESULTS: Model simulations help project 10-year disease reductions and net savings from water fluoridation, motivational interviewing, and fluoride toothpaste. Interventions requiring health professionals cost more than they save. Interventions that target children at high risk, begin early, and combine multiple strategies hold greatest potential. Defluoridating New York City would increase disease and costs dramatically. CONCLUSIONS: The variety of population-level and individual-level interventions available to control ECC differ substantially in their capacity to improve children's oral health and reduce state Medicaid expenditures. PRACTICAL IMPLICATIONS: Using Medicaid and health department dollars to deliver ECC preventive and management interventions holds strong promise to improve children's oral health while reducing state dental expenditures in Medicaid.

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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. RESULTS: Results indicate that by 2040, all 6 intervention clusters combined could result in cumulative reductions of 49% to 54% in the cardiovascular risk-related death rate and of 13% to 21% in risk factor-attributable costs. A majority of the death reduction would come from Established interventions, but Emerging interventions would also contribute strongly. A slim majority of the cost reduction would come from Emerging interventions. CONCLUSION: PRISM allows public health officials to examine the potential influence of different types of interventions - both established and emerging - for reducing cardiovascular risks. Our modeling suggests that established interventions could still contribute much to reducing deaths and costs, especially through greater use of well-known approaches to preventive and acute clinical care, whereas emerging interventions have the potential to contribute significantly, especially through certain types of preventive care and improved nutrition.

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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. Of the 4 categories of intervention, taxes and regulation reduce costs the most in the short term (through 2020) and long term (through 2040) and reduce deaths the most in the long term; they are second to clinical interventions in reducing deaths in the short term. All 4 categories combined were required to bring costs and deaths in the less-advantaged county down to the national level.

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PURPOSE: The field of quality-of-life (QOL) research would benefit from learning about and integrating systems science approaches that model how social forces interact dynamically with health and affect the course of chronic illnesses. Our purpose is to describe the systems science mindset and to illustrate the utility of a system dynamics approach to promoting QOL research in chronic disease, using diabetes as an example. METHODS: We build a series of causal loop diagrams incrementally, introducing new variables and their dynamic relationships at each stage. RESULTS: These causal loop diagrams demonstrate how a common set of relationships among these variables can generate different disease and QOL trajectories for people with diabetes and also lead to a consideration of non-clinical (psychosocial and behavioral) factors that can have implications for program design and policy formulation. CONCLUSIONS: The policy implications of the causal loop diagrams are discussed, and empirical next steps to validate the diagrams and quantify the relationships are described.

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Carbon nanoparticles (CNPs) and halloysite nanoclay (HNC) modified carbon paste electrode (HNC-CNP-CPE) was developed for the determination of methyl parathion (MP) and ethyl parathion (EP). The electrochemical behavior of these molecules was investigated employing cyclic voltammetry (CV), chronocoulometry (CC), electrochemical impedance spectroscopy (EIS) and potentiometric stripping analysis (PSA). After optimization of analytical conditions employing this electrode at pH 5.0 in acetate buffer (0.1 M), the peak currents were found to vary linearly with its concentration in the range of 1.55×10(-9) to 3.67×10(-6) M and 1.21×10(-9) to 4.92×10(-6) M for MP and EP, respectively. The detection limits (S/N=3) of 4.70×10(-10) M and 3.67×10(-10) M were obtained for MP and EP, respectively, using PSA. The prepared modified electrode showed several advantages such as simple preparation method, high sensitivity, very low detection limits and excellent reproducibility. The proposed method was employed for the determination of MP and EP in fruits, vegetables, water and soil samples.

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INTRODUCTION: Early childhood caries (ECC)--tooth decay among children younger than 6 years--is prevalent and consequential, affecting nearly half of US 5-year-olds, despite being highly preventable. Various interventions have been explored to limit caries activity leading to cavities, but little is known about the long-term effects and costs of these interventions. We developed a system dynamics model to determine which interventions, singly and in combination, could have the greatest effect in reducing caries experience and cost in a population of children aged birth to 5 years. METHODS: System dynamics is a computer simulation technique useful to policy makers in choosing the most appropriate interventions for their populations. This study of Colorado preschool children models 6 categories of ECC intervention--applying fluorides, limiting cariogenic bacterial transmission from mothers to children, using xylitol directly with children, clinical treatment, motivational interviewing, and combinations of these--to compare their relative effect and cost. RESULTS: The model projects 10-year intervention costs ranging from $6 million to $245 million and relative reductions in cavity prevalence ranging from none to 79.1% from the baseline. Interventions targeting the youngest children take 2 to 4 years longer to affect the entire population of preschool-age children but ultimately exert a greater benefit in reducing ECC; interventions targeting the highest-risk children provide the greatest return on investment, and combined interventions that target ECC at several stages of its natural history have the greatest potential for cavity reduction. Some interventions save more in dental repair than their cost; all produce substantial reductions in repair cost. CONCLUSION: By using data relevant to any geographic area, this system model can provide policy makers with information to maximize the return on public health and clinical care investments.

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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.

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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. We also describe how the model was used to evaluate the potential impacts of various intervention strategies for reducing the county's CVD burden and present the results of those policy tests.

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Community psychologists have a long history of interest in understanding social systems and how to bring about enduring positive change in these systems. However, the methods that community psychologists use to anticipate and evaluate the changes that result from system change efforts are less well developed. In the current paper, we introduce readers to system dynamics modeling, an action research approach to studying complex systems and the consequences of system change. We illustrate this approach by describing a system dynamics model of educational reform. We provide readers with an introduction to system dynamics modeling, as well as describe the strengths and limitations of the approach for application to community psychology.

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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. System dynamics shows promise as a means of modeling multiple interacting diseases and risks, the interaction of delivery systems and diseased populations, and matters of national and state policy.

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