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A special issue of Systems (ISSN 2079-8954). This special issue belongs to the section "Systems Practice in Social Science".

Deadline for manuscript submissions: closed (28 February 2023) | Viewed by 44484

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Special Issue Editors

Dr. Jack Homer

E-Mail Website
Guest Editor

1. Director, Homer Consulting, Barrytown, NY 12507, USA
2. Research Affiliate, MIT Sloan School of Management, Cambridge, MA 02139, USA
Interests: system dynamics; public health; healthcare; climate change; sustainable development; model validation

Gary B. Hirsch

E-Mail Website
Co-Guest Editor

Independent Consultant and Creator of Learning Environments, Wayland, MA 01778-3017, USA
Interests: system dynamics; population health; social determinants of health; health care delivery systems; social challenges such as youth homelessness; income inequality; creation of simulation-based learning environments

Special Issue Information

Dear Colleagues,

The system dynamics (SD) approach has been applied to issues of public health and health care since the 1970s, with an increasing publication rate since 2000, including hundreds of articles as well as a number of books. A recent systematic review (Darabi and Hosseinichimeh 2020) found a roughly even split between studies of specific diseases (both infectious and non-infectious), health care organizations, and regional population health.

Most of these published studies involve simulation modeling and quantification, but some stop at the development of causal loop diagrams (or system maps) based on the literature and expert knowledge. In either case, SD studies attempt to explain a problem—and its potential solution—in terms of its inherent stock–flow structures and behavioral feedback loops that operate continuously and shape trends over years or decades.

COVID-19 has been an important recent focus, of course. SD makes it possible to model the behavioral dynamics responsible for multiple waves of disease over time, as well as asking what types of policies can best balance COVID-19’s various health and social repercussions. We hope to publish more such important work on COVID-19 in our Special Issue.

However, COVID-19 is not our sole focus, and we hope to publish exciting new SD application work on a variety of topics, such as:

Health disparities;
Social determinants of health;
Community health and well-being;
Public health and health care in developing nations;
Health insurance and payment approaches;
Comparative health systems;
Mental health issues in adolescents and adults;
Alzheimer’s dementia;
Cancer prevention and treatment;
Overuse or undersupply of medical technologies.

We also welcome papers that discuss modeling methodology, such as improved methods for data use and model testing, and their practical application to health-related issues.

Papers should communicate clearly and concisely and present strong evidence supporting a model and its findings. Even qualitative mapping studies must rest on some numerical evidence demonstrating the magnitude and history of the problem being addressed.

Dr. Jack Homer
Gary B. Hirsch
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Systems is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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Published Papers (10 papers)

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Research

18 pages, 3666 KiB

Open AccessArticle

Dynamics of Medical Screening: A Simulation Model of PSA Screening for Early Detection of Prostate Cancer

by Özge Karanfil

Systems 2023, 11(5), 252; https://doi.org/10.3390/systems11050252 - 16 May 2023

Cited by 3 | Viewed by 2395

Abstract

In this study, we present a novel simulation model and case study to explore the long-term dynamics of early detection of disease, also known as routine population screening. We introduce a realistic and portable modeling framework that can be used for most cases of cancer, including a natural disease history and a realistic yet generic structure that allows keeping track of critical stocks that have been generally overlooked in previous modeling studies. Our model is specific to prostate-specific antigen (PSA) screening for prostate cancer (PCa), including the natural progression of the disease, respective changes in population size and composition, clinical detection, adoption of the PSA screening test by medical professionals, and the dissemination of the screening test. The key outcome measures for the model are selected to show the fundamental tradeoff between the main harms and benefits of screening, with the main harms including (i) overdiagnosis, (ii) unnecessary biopsies, and (iii) false positives. The focus of this study is on building the most reliable and flexible model structure for medical screening and keeping track of its main harms and benefits. We show the importance of some metrics which are not readily measured or considered by existing medical literature and modeling studies. While the model is not primarily designed for making inferences about optimal screening policies or scenarios, we aim to inform modelers and policymakers about potential levers in the system and provide a reliable model structure for medical screening that may complement other modeling studies designed for cancer interventions. Our simulation model can offer a formal means to improve the development and implementation of evidence-based screening, and its future iterations can be employed to design policy recommendations to address important policy areas, such as the increasing pool of cancer survivors or healthcare spending in the U.S. Full article

(This article belongs to the Special Issue System Dynamics Models for Public Health and Health Care Policy)

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17 pages, 3380 KiB

Open AccessArticle

Use of System Dynamics Modelling for Evidence-Based Decision Making in Public Health Practice

by Abraham George, Padmanabhan Badrinath, Peter Lacey, Chris Harwood, Alex Gray, Paul Turner and Davinia Springer

Systems 2023, 11(5), 247; https://doi.org/10.3390/systems11050247 - 14 May 2023

Cited by 4 | Viewed by 4591

Abstract

In public health, the routine use of linear forecasting, which restricts our ability to understand the combined effects of different interventions, demographic changes and wider health determinants, and the lack of reliable estimates for intervention impacts have limited our ability to effectively model population needs. Hence, we adopted system dynamics modelling to forecast health and care needs, assuming no change in population behaviour or determinants, then generated a “Better Health” scenario to simulate the combined impact of thirteen interventions across cohorts defined by age groups and diagnosable conditions, including “no conditions”. Risk factors for the incidence of single conditions, progression toward complex needs and levels of morbidity including frailty were used to create the dynamics of the model. Incidence, prevalence and mortality for each cohort were projected over 25 years with “do nothing” and “Better Health” scenarios. The size of the “no conditions” cohort increased, and the other cohorts decreased in size. The impact of the interventions on life expectancy at birth and healthy life expectancy is significant, adding 5.1 and 5.0 years, respectively. We demonstrate the feasibility, applicability and utility of using system dynamics modelling to develop a robust case for change to invest in prevention that is acceptable to wider partners. Full article

(This article belongs to the Special Issue System Dynamics Models for Public Health and Health Care Policy)

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20 pages, 3569 KiB

Open AccessArticle

Using a System Dynamics Simulation Model to Identify Leverage Points for Reducing Youth Homelessness in Connecticut

by Gary B. Hirsch and Heather I. Mosher

Systems 2023, 11(3), 163; https://doi.org/10.3390/systems11030163 - 22 Mar 2023

Cited by 2 | Viewed by 3162

Abstract

Youth homelessness is a significant problem in most United States communities. Health problems are both a contributor to and a consequence of homelessness. Responses to youth homelessness are typically fragmentary. Different agencies deal with various causes and consequences of the problem. Stakeholders in Connecticut sought a more coherent approach. This article describes the development and use of a system dynamics simulation model as a decision-support tool that: (1) brings stakeholders together from diverse service sectors and allows them to see the system as a whole, (2) enables them to explore how delivery systems interact to affect homeless and unstably housed youth, (3) lets them test the impact of different intervention alternatives on reducing the problem, and (4) helps develop insights about coherent approaches to youth homelessness. The model’s development is described as a phased process including stakeholder engagement, causal mapping, and creation of the quantitative simulation model. The resulting model is presented along with an interface that enables stakeholders to use the model in a Learning Lab setting. Results of an initial set of Learning Labs are presented, including types of insights gained by participants from using the simulation model. Conclusions include limitations of the model and plans for its future use. Full article

(This article belongs to the Special Issue System Dynamics Models for Public Health and Health Care Policy)

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27 pages, 5924 KiB

Open AccessArticle

Enabling Mobility: A Simulation Model of the Health Care System for Major Lower-Limb Amputees to Assess the Impact of Digital Prosthetics Services

by Jefferson K. Rajah, William Chernicoff, Christopher J. Hutchison, Paulo Gonçalves and Birgit Kopainsky

Systems 2023, 11(1), 22; https://doi.org/10.3390/systems11010022 - 3 Jan 2023

Cited by 2 | Viewed by 8734

Abstract

The World Health Organization estimates that 5 to 15% of amputees in any given population have access to a prosthesis. This figure is likely to worsen as the amputee population is expected to double by 2050, straining the limited capacity of prosthetics services. Without proper and timely prosthetic interventions, amputees with major lower-limb loss experience adverse mobility outcomes, including the loss of independence, lowered quality of life, and decreased life expectancy. Presently, the use of digital technology in prosthetics (e.g., 3D imaging, digital processing, and 3D printed sockets) is contended as a viable solution to this problem. This paper uses system dynamics modeling to assess the impact of digital prosthetics service provision. Our simulation model represents the patient-care continuum and digital prosthetics market system, providing a feedback-rich causal theory of how digital prosthetics impacts amputee mobility and the corollary socio-health-economic outcomes over time. With sufficient resources for market formation and capacity expansion for digital prosthetics services, our work suggests an increased proportion of prosthesis usage and improved associated health-economic outcomes. Accordingly, our findings could provide decision support for health policy to better mitigate the accessibility problem and bolster the social impact of prosthesis usage. Full article

(This article belongs to the Special Issue System Dynamics Models for Public Health and Health Care Policy)

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17 pages, 6109 KiB

Open AccessArticle

Addressing Parameter Uncertainty in a Health Policy Simulation Model Using Monte Carlo Sensitivity Methods

by Wayne Wakeland and Jack Homer

Systems 2022, 10(6), 225; https://doi.org/10.3390/systems10060225 - 18 Nov 2022

Cited by 2 | Viewed by 2143

Abstract

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. Full article

(This article belongs to the Special Issue System Dynamics Models for Public Health and Health Care Policy)

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29 pages, 2611 KiB

Open AccessArticle

Resilience Development in Multiple Shocks: Lessons in Mental Health and Well-Being Deterioration during COVID-19

by Ke Zhou and Mengru Zhang

Systems 2022, 10(5), 183; https://doi.org/10.3390/systems10050183 - 10 Oct 2022

Viewed by 5331

Abstract

Resilience describes individuals’ and organizations’ recovery from crises and adaptation to disturbances and adversities. Emerging research shows the deterioration of the population’s mental health and well-being during the multiple waves of the COVID-19 pandemic, suggesting that the resilience developed is insufficient to address the system’s persistent shocks. Drawing on the findings on mental health and well-being during the COVID-19 pandemic and the psychological and organizational resilience theories, we developed a system dynamics theory model exploring how the presence of multiple shocks to the system challenges the population’s health and well-being. We initiated the model with three shocks with the same intensities and durations, and then experimented with scenarios in which the strength of multiple shocks (duration and intensity) was attenuated and amplified. The model showed that temporary environmental adjustments with limited long-term stabilized solutions and a lack of health service provision can increase the accumulative risks of health and well-being deterioration. We highlight the role of essential health service sectors’ resilience and individuals’ and organizations’ tolerance of adversities and disturbances in providing sustainable resilience. We conclude by discussing critical factors in organizational and psychological resilience development in crises with multiple shocks to the system. Full article

(This article belongs to the Special Issue System Dynamics Models for Public Health and Health Care Policy)

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11 pages, 1792 KiB

Open AccessArticle

How Can a Community Pursue Equitable Health and Well-Being after a Severe Shock? Ideas from an Exploratory Simulation Model

by Bobby Milstein, Jack Homer and Chris Soderquist

Systems 2022, 10(5), 158; https://doi.org/10.3390/systems10050158 - 19 Sep 2022

Cited by 3 | Viewed by 3875

Abstract

Local communities sometimes face severe shocks, such as the COVID-19 pandemic or economic recession, which inflict widespread harm, intensify injustice and test the ties that bind people together. A recent “Springboard” theory proposes a way to spring forward toward an equitable, thriving future by altering priorities among four structural drivers of population well-being: the extent of vital conditions, equity, urgent services capacity, and belonging and civic muscle. To explore the strategic implications of the Springboard theory, we developed the Thriving Together Model, a system dynamics simulation model that lets users play out alternative investment priorities and track changes over a decade as they try to maximize the number of people thriving and minimize suffering. The prototype model is exploratory, subject to further refinement and empirical support, but it has already sparked creative conversations among hundreds of changemakers who have interacted with it through an interactive theater. This paper presents the model’s structure, illustrative results, and tentative insights. The Thriving Together Model extends Ostrom’s Nobel Prize-winning work on shared stewardship by offering a general explanation about how stewards of a divided community can heal through a traumatic shock and spring forward toward a future with greater well-being and justice. Full article

(This article belongs to the Special Issue System Dynamics Models for Public Health and Health Care Policy)

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15 pages, 3162 KiB

Open AccessArticle

Using Cascaded and Interlocking Generic System Archetypes to Communicate Policy Insights—The Case for Justifying Integrated Health Care Systems in Terms of Reducing Hospital Congestion

by Eric Frank Wolstenholme

Systems 2022, 10(5), 135; https://doi.org/10.3390/systems10050135 - 1 Sep 2022

Cited by 3 | Viewed by 3975

Abstract

A persistent problem in UK hospitals is that of delayed discharges, where patients who are fit for discharge continue to occupy beds whilst awaiting care packages from Social Care. Integrated Care Systems (ICSs) in which Health and Social Care collaborate are now a major NHS initiative, the thinking being that such spending will have direct cost savings to health by freeing up expensive beds. The premise of this paper is that the benefits to health of assisting Social Care could also reduce a number of serious indirect costs and provide wide-ranging benefits to hospital patients, staff and budgets. This is accomplished by reducing the congestion arising from the use of many painful internal coping strategies and unintended consequences, which hospitals have to resort to when constrained by a lack of discharge solutions. The paper explores new and novel ways of using generic systems archetypes to create a hypothesis linking general Integrated Care Systems to congestion reduction throughout hospitals. Rather than use archetypes individually, they are applied here collectively in tandem. These are named ‘cascaded archetypes’, where the unintended consequence of one archetype becomes the driver for the next and are useful where fundamental solutions to problems are difficult to implement and unintended consequences must be dealt with. Full article

(This article belongs to the Special Issue System Dynamics Models for Public Health and Health Care Policy)

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18 pages, 4995 KiB

Open AccessArticle

Exploring the Impacts of COVID-19 on Coastal Tourism to Inform Recovery Strategies in Nelson Mandela Bay, South Africa

by Estee Vermeulen-Miltz, Jai Kumar Clifford-Holmes, Bernadette Snow and Amanda Talita Lombard

Systems 2022, 10(4), 120; https://doi.org/10.3390/systems10040120 - 12 Aug 2022

Cited by 8 | Viewed by 4305

Abstract

Globally, the COVID-19 pandemic bought devastating impacts to multiple economic sectors, with a major downfall observed in the tourism sector owing to explicit travel bans on foreign and domestic tourism. In Nelson Mandela Bay (NMB), South Africa, tourism plays an important role; however, negative effects from the pandemic and resulting restrictions has left the sector dwindling and in need of a path to recovery. Working together with local government and stakeholders, this study applied system dynamics modelling to investigate the impacts of COVID-19 on coastal tourism in NMB to provide decision-support and inform tourism recovery strategies. Through model analysis, a suite of management interventions was tested under two ‘what-if’ scenarios, with reference to the business-as-usual governance response scenario. Scenario one specifically aimed to investigate a desirable tourism recovery strategy assuming governance control, whereas scenario two investigated a scenario where the effects of governance responses were impeded on by the exogenous effects from the virus. Results suggest that uncertainty remained prevalent in the trajectory of the infection rate as well as in associated trends in tourism; however, through the lifting of travel restrictions and the continual administration of vaccines, a path to recovery was shown to be evident. Full article

(This article belongs to the Special Issue System Dynamics Models for Public Health and Health Care Policy)

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13 pages, 1101 KiB

Open AccessArticle

Evaluating Public Health Efforts to Prevent and Control Chronic Disease: A Systems Modeling Approach

by Morgan Clennin, Jack Homer, Alex Erkenbeck and Cheryl Kelly

Systems 2022, 10(4), 89; https://doi.org/10.3390/systems10040089 - 28 Jun 2022

Cited by 2 | Viewed by 3080

Abstract

The growing burden of chronic disease represents a complex challenge to public health. Innovative approaches, such as system dynamics simulation modeling, can aid public health professionals in understanding such complex issues and identifying effective solutions. This paper describes a system dynamics model and its application in projecting the impacts of evidence-based interventions on chronic disease for the state of Colorado. The development of the model was guided by data and input from subject matter expertise, peer-reviewed literature, and surveillance data. The model includes 28 intervention levers for chronic disease prevention, screening, and management. Interventions were simulated from 2020 to 2050 to project their impact on ten preventable causes of death. The simulations indicated the 6 most impactful interventions by 2050 to be adult smoking prevention, diabetes prevention, smoking cessation, blood pressure management, adult physical activity promotion, and colorectal cancer screening. Together, these 6 interventions could reduce preventable deaths by 7.1%, or 74% of the 9.6% reduction from all 28 interventions combined. This system dynamics model is a flexible tool that could be adapted or extended to include other populations or preventable chronic diseases. Prioritization and wide-scale implementation of the most impactful interventions could significantly reduce preventable deaths resulting from chronic disease. Full article

(This article belongs to the Special Issue System Dynamics Models for Public Health and Health Care Policy)

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