Systems Science Approach Research Articles

Simulation modelling as a tool for knowledge mobilisation in health policy settings: a case study protocol.

BackgroundEvidence-informed decision-making is essential to ensure that health programs and services are effective and offer value for money; however, barriers to the use of evidence persist. Emerging systems science approaches and advances in technology are providing new methods and tools to facilitate evidence-based decision-making. Simulation modelling offers a unique tool for synthesising and leveraging existing evidence, data and expert local knowledge to examine, in a robust, low risk and low cost way, the likely impact of alternative policy and service provision scenarios. This case study will evaluate participatory simulation modelling to inform the prevention and management of gestational diabetes mellitus (GDM). The risks associated with GDM are well recognised; however, debate remains regarding diagnostic thresholds and whether screening and treatment to reduce maternal glucose levels reduce the associated risks. A diagnosis of GDM may provide a leverage point for multidisciplinary lifestyle modification interventions. This research will apply and evaluate a simulation modelling approach to understand the complex interrelation of factors that drive GDM rates, test options for screening and interventions, and optimise the use of evidence to inform policy and program decision-making.Methods/DesignThe study design will use mixed methods to achieve the objectives. Policy, clinical practice and research experts will work collaboratively to develop, test and validate a simulation model of GDM in the Australian Capital Territory (ACT). The model will be applied to support evidence-informed policy dialogues with diverse stakeholders for the management of GDM in the ACT. Qualitative methods will be used to evaluate simulation modelling as an evidence synthesis tool to support evidence-based decision-making. Interviews and analysis of workshop recordings will focus on the participants’ engagement in the modelling process; perceived value of the participatory process, perceived commitment, influence and confidence of stakeholders in implementing policy and program decisions identified in the modelling process; and the impact of the process in terms of policy and program change.DiscussionThe study will generate empirical evidence on the feasibility and potential value of simulation modelling to support knowledge mobilisation and consensus building in health settings.

Inside-Out-Outside-In: A dual approach process model to developing work happiness

Abstract: This conceptual paper presents the Inside-Out-Outside-In (IO-OI) model, a dual process positive systems science approach to developing work happiness. The model suggests that work happiness of employees is influenced by individual level personal resources developed through positive employee development and positive attitudes (inside-out factors), and social resources developed at the organizational level through positive strategies and positive organizational culture (outside-in factors). The model further specifies three processes that connect outside-in and inside-out factors (attitude re-evaluation, selective exposure and confirmation bias), and a series of feedback loops that support upward spirals of positive development at the individual and organizational levels. We suggest ways in which the IO-OI model can be used and tested. The IO-OI model integrates the fields of attitudes, positive organizational scholarship, and positive organizational behavior, and provides a foundation for understanding how to best foster positive attitudes, create virtuous organizations and foster employee work happiness.

"Omics" Prospective Monitoring of Bariatric Surgery: Roux-En-Y Gastric Bypass Outcomes Using Mixed-Meal Tolerance Test and Time-Resolved (1)H NMR-Based Metabolomics.

Roux-en-Y gastric bypass (RYGB) surgery goes beyond weight loss to induce early beneficial hormonal changes that favor glycemic control. In this prospective study, ten obese subjects diagnosed with type 2 diabetes underwent bariatric surgery. Mixed-meal tolerance test was performed before and 12 months after RYGB, and the outcomes were investigated by a time-resolved hydrogen nuclear magnetic resonance ((1)H NMR)-based metabolomics. To the best of our knowledge, no previous omics-driven study has used time-resolved (1)H NMR-based metabolomics to investigate bariatric surgery outcomes. Our results presented here show a significant decrease in glucose levels after bariatric surgery (from 159.80 ± 61.43 to 100.00 ± 22.94 mg/dL), demonstrating type 2 diabetes remission (p < 0.05). The metabolic profile indicated lower levels of lactate, alanine, and branched chain amino acids for the operated subject at fasting state after the surgery. However, soon after food ingestion, the levels of these metabolites increased faster in operated than in nonoperated subjects. The lipoprotein profile achieved before and after RYGB at fasting was also significantly different, but converging 180 min after food ingestion. For example, the very low-density lipoprotein, low-density lipoprotein, N-acetyl-glycoproteins, and unsaturated lipid levels decreased after RYGB, while phosphatidylcholine and high-density lipoprotein increased. This study provides important insights on RYGB surgery and attendant type 2 diabetes outcomes using an "omics" systems science approach. Further research on metabolomic correlates of RYGB surgery in larger study samples is called for.

Agent-Based Modeling of Chronic Diseases: A Narrative Review and Future Research Directions

The United States is experiencing an epidemic of chronic disease. As the US population ages, health care providers and policy makers urgently need decision models that provide systematic, credible prediction regarding the prevention and treatment of chronic diseases to improve population health management and medical decision-making. Agent-based modeling is a promising systems science approach that can model complex interactions and processes related to chronic health conditions, such as adaptive behaviors, feedback loops, and contextual effects. This article introduces agent-based modeling by providing a narrative review of agent-based models of chronic disease and identifying the characteristics of various chronic health conditions that must be taken into account to build effective clinical- and policy-relevant models. We also identify barriers to adopting agent-based models to study chronic diseases. Finally, we discuss future research directions of agent-based modeling applied to problems related to specific chronic health conditions.

Connections Matter: Social Networks and Lifespan Health in Primate Translational Models.

Humans live in societies full of rich and complex relationships that influence health. The ability to improve human health requires a detailed understanding of the complex interplay of biological systems that contribute to disease processes, including the mechanisms underlying the influence of social contexts on these biological systems. A longitudinal computational systems science approach provides methods uniquely suited to elucidate the mechanisms by which social systems influence health and well-being by investigating how they modulate the interplay among biological systems across the lifespan. In the present report, we argue that nonhuman primate social systems are sufficiently complex to serve as model systems allowing for the development and refinement of both analytical and theoretical frameworks linking social life to health. Ultimately, developing systems science frameworks in nonhuman primate models will speed discovery of the mechanisms that subserve the relationship between social life and human health.

Neighbourhood food, physical activity, and educational environments and black/white disparities in obesity: a complex systems simulation analysis

BackgroundMultiple approaches that can contribute to reducing obesity have been proposed. These policies may share overlapping pathways, and may have unanticipated consequences, creating considerable complexity. Aiming to illuminate the use...

WITHDRAWN: Revealing the role of factors shaping Americans’ objective well-being: A systems science approach with network analysis

WITHDRAWN: Revealing the role of factors shaping Americans’ objective well-being: A systems science approach with network analysis

Factors shaping Americans’ objective well-being: A systems science approach with network analysis

Despite the introduction of multiple factors, multidimensional approaches cannot represent the complexity of the objective determinants of well-being (ODW). This paper proposes an alternative OWD model that adds a systemic approach, by using Bayesian networks algorithms to discover a directed two level network of variables nested in subnetworks of determinants. The network was inferred by using subsamples of the 2013 version of the American Community Survey. Network analysis methods applied to the model provided new insights concerning single ODW relevance and the roles that are useful to focus selective welfare interventions; they also offered a big picture that is fundamental to reason about the unpremeditated universal character of the selective US welfare policies.

Social Determinants of Population Health: A Systems Sciences Approach.

Population distributions of health emerge from the complex interplay of health-related factors at multiple levels, from the biological to the societal level. Individuals are aggregated within social networks, affected by their locations, and influenced differently across time. From aggregations of individuals, group properties can emerge, including some exposures that are ubiquitous within populations but variant across populations. By combining a focus on social determinants of health with a conceptual framework for understanding how genetics, biology, behavior, psychology, society, and environment interact, a systems science approach can inform our understanding of the underlying causes of the unequal distribution of health across generations and populations, and can help us identify promising approaches to reduce such inequalities. In this paper, we discuss how systems science approaches have already made several substantive and methodological contributions to the study of population health from a social epidemiology perspective.

Promoting comprehensive disaster policy through interdisciplinary collaboration.

Systems science is a field dedicated to the study of systems within society or nature.1 It routinely draws on disciplines including engineering, medicine, economics and the environmental sciences. Analyses that use a systems science approach typically consider interactions between a system (eg, public transportation within a city) and the environment in which it functions (eg, weather conditions), and account for dynamic behaviour (eg, individuals’ responses to a severe weather event).2 Although public policy provides an additional layer of societal context for any given system, legal and policy factors are rarely integrated into systems science analyses. Because policy plays such an important role in injury prevention,3 incorporating policy considerations into systems science analyses holds great promise as an emerging methodological approach to improve population health. This is especially true for natural disasters, such as hurricanes, floods and earthquakes, and human-caused disasters, such as terrorist acts,4 as these scenarios pose challenges within and across systems and jurisdictions. While each type of disaster raises unique concerns for planning, response and recovery, they share the potential to produce wide-ranging physical injuries. For example, disasters are associated with drownings, falls, lacerations, amputations and long-term disability.5–8 As described in detail below, by partnering with engineers to apply methods within the systems science framework, lawyers and policy analysts can bring an important—and often missing—dimension to models intended to inform preparedness plans and reduce injuries. These collaborations can occur both concurrently and serially, allowing multiple disciplines to contribute throughout relevant analyses. This article first describes the separate contributions of law and engineering within emergency preparedness efforts, and then explores their synergy when combined within a systems science approach. Laws provide an infrastructure for emergency planning, response and recovery. In the USA, every state government, along with the federal government, has the authority to …

Atlas of knowledge: anyone can map

The power of mapping: principles for visualizing knowledge, illustrated by many stunning large-scale, full-color maps. Maps of physical spaces locate us in the world and help us navigate unfamiliar routes. Maps of topical spaces help us visualize the extent and structure of our collective knowledge; they reveal bursts of activity, pathways of ideas, and borders that beg to be crossed. This book, from the author of Atlas of Science, describes the power of topical maps, providing readers with principles for visualizing knowledge and offering as examples forty large-scale and more than 100 small-scale full-color maps. Today, data literacy is becoming as important as language literacy. Well-designed visualizations can rescue us from a sea of data, helping us to make sense of information, connect ideas, and make better decisions in real time. In Atlas of Knowledge, leading visualization expert Katy Boerner makes the case for a systems science approach to science and technology studies and explains different types and levels of analysis. Drawing on fifteen years of teaching and tool development, she introduces a theoretical framework meant to guide readers through user and task analysis; data preparation, analysis, and visualization; visualization deployment; and the interpretation of science maps. To exemplify the framework, the Atlas features striking and enlightening new maps from the popular Places & Spaces: Mapping Science exhibit that range from Key Events in the Development of the Video Tape Recorder to Mobile Landscapes: Location Data from Cell Phones for Urban Analysis to Literary Empires: Mapping Temporal and Spatial Settings of Victorian Poetry to Seeing Standards: A Visualization of the Metadata Universe. She also discusses the possible effect of science maps on the practice of science.

The Challenge of Cardiovascular Diseases and Diabetes to Public Health: A Study Based on Qualitative Systemic Approach.

The most common modeling approaches to understanding incidence, prevalence and control of chronic diseases in populations, such as statistical regression models, are limited when it comes to dealing with the complexity of those problems. Those complex adaptive systems have characteristics such as emerging properties, self-organization and feedbacks, which structure the system stability and resistance to changes. Recently, system science approaches have been proposed to deal with the range, complexity, and multifactor nature of those public health problems. In this paper we applied a multilevel systemic approach to create an integrated, coherent, and increasingly precise conceptual framework, capable of aggregating different partial or specialized studies, based on the challenges of the Longitudinal Study of Adult Health – ELSA-Brasil. The failure to control blood pressure found in several of the study's subjects was discussed, based on the proposed model, analyzing different loops, time lags, and feedback that influence this outcome in a population with high educational level, with reasonably good health services access. We were able to identify the internal circularities and cycles that generate the system’s resistance to change. We believe that this study can contribute to propose some new possibilities of the research agenda and to the discussion of integrated actions in the field of public health.

Synthesizing Marketing, Community Engagement, and Systems Science Approaches for Advancing Translational Research.

The adoption and implementation of evidence-based interventions (EBIs) are the goals of translational research; however, potential end-users' perceptions of an EBI value have contributed to low rates of adoption. In this article, we describe our application of emerging dissemination and implementation science theoretical perspectives, community engagement, and systems science principles to develop a novel EBI dissemination approach. Using consumer-driven, graphics-rich simulation, the approach demonstrates predicted implementation effects on health and employment outcomes for socioeconomically disadvantaged women at the local level and is designed to increase adoption interest of county program managers accountable for improving these outcomes in their communities.

Resilience within Ultrascale Computing System: Challenges and Opportunities from Nesus Project

Although resilience is already an established field in system science and many methodologies and approaches are available to deal with it, the unprecedented scales of computing, of the massive data to be managed, new network technologies, and drastically new forms of massive scale applications bring new challenges that need to be addressed. This paper reviews the challenges and approaches of resilience in ultrascale computing systems from multiple perspectives involving and addressing the resilience aspects of hardware-software co-design for ultrascale systems, resilience against security attacks, new approaches and methodologies to resilience in ultrascale systems, applications and case studies.

The role of paradigms in engineering practice and education for sustainable development

Engineers have always had to deal with complex challenges. However, a profound change has occurred over the last two decades with a realization of the need to transition from a focus on technical issues to sustainability problems that require an integrated, adaptive and participatory approach. Such an engineering approach does not only necessitate new methods and tools, but also the consideration of epistemology to deal with different kinds of knowledge and high uncertainties. The concept of paradigms can support the case-specific analysis of concrete solution strategies based upon an understanding of the epistemological dimension of sustainability issues. A systematic and integrated discussion of paradigms and their interactions in engineering practice is currently lacking in the scientific literature. This paper examines the role of paradigms in engineering practice and presents a system science approach for the analysis of paradigms. A case study on sustainable flood management and a literature analysis are provided to show the relevance of multiple paradigms in sustainable development issues. Engineers should be aware of paradigms and their respective application context, as well as the particular role of the “community involvement” paradigm for sustainable development. We propose an iterative learning approach to continuously deepen students' understanding of participatory processes and develop their ability to facilitate stakeholder processes. An overview and some reflections on the experiences of the authors in the teaching of these new paradigms at McGill University, Canada, and the University of Osnabrueck, Germany, are provided. In particular, group model building exercises were found to provide students with important experiences regarding stakeholder interaction in the safe space of the classroom.

Challenging students' perceptions of sustainability using an Earth Systems Science approach

This study investigated whether an Earth Systems-based course focused on raising postgraduate students' awareness of sustainability, from a systems-thinking perspective, would produce graduates with commitment to drive the sustainability agenda forward with a broad perspective. It investigated students' pre and post-course perceptions of sustainability using a pre- and post-course survey, and by analysing the students' reflective journal entries. The findings confirmed those of previous studies in that initially these students did have an enviro-centric view. The study showed that while the students' perceptions had indeed moved towards a broader view of sustainability, many still leaned towards an environmentally focused perspective.

Investigating combination HIV prevention: isolated interventions or complex system.

IntroductionTreatment as prevention has mobilized new opportunities in preventing HIV transmission and has led to bold new UNAIDS targets in testing, treatment coverage and transmission reduction. These will require not only an increase in investment but also a deeper understanding of the dynamics of combining behavioural, biomedical and structural HIV prevention interventions. High-income countries are making substantial investments in combination HIV prevention, but is this investment leading to a deeper understanding of how to combine interventions? The combining of interventions involves complexity, with many strategies interacting with non-linear and multiplying rather than additive effects.DiscussionDrawing on a recent scoping study of the published research evidence in HIV prevention in high-income countries, this paper argues that there is a gap between the evidence currently available and the evidence needed to guide the achieving of these bold targets. The emphasis of HIV prevention intervention research continues to look at one intervention at a time in isolation from its interactions with other interventions, the community and the socio-political context of their implementation. To understand and evaluate the role of a combination of interventions, we need to understand not only what works, but in what circumstances, what role the parts need to play in their relationship with each other, when the combination needs to adapt and identify emergent effects of any resulting synergies. There is little development of evidence-based indicators on how interventions in combination should achieve that strategic advantage and synergy. This commentary discusses the implications of this ongoing situation for future research and the required investment in partnership. We suggest that systems science approaches, which are being increasingly applied in other areas of public health, could provide an expanded vocabulary and analytic tools for understanding these complex interactions, relationships and emergent effects.ConclusionsRelying on the current linear but disconnected approaches to intervention research and evidence we will miss the potential to achieve and understand system-level synergies. Given the challenges in sustaining public health and HIV prevention investment, meeting the bold UNAIDS targets that have been set is likely to be dependent on achieving systems level synergies.

Synthesizing Marketing, Community Engagement, and Systems Science Approaches for Advancing Translational Research

Synthesizing Marketing, Community Engagement, and Systems Science Approaches for Advancing Translational Research

Marshall and Galea respond to "data theory in epidemiology".

Over the past decade, the use of agent-based models (ABMs) and the development of causal inference methods have proceeded rather independently in epidemiology. In our article (1), we aimed to provide an initial foundation to bridge the two. We argued for a more rigorous causal inference framework in epidemiologic applications of ABMs and sought to generate discussion about how best to accomplish this. We therefore wish to thank Diez Roux (2) and Hernan (3) for extending this discussion and note here broad points of unanimity and motivating next steps. Centrally, we support Hernan's call for research and consensus on acceptable methodological standards for agent-based modeling in epidemiology. Our paper was a first shot across the conceptual bow. However, there is much work that remains to be done in identifying specific agent-based modeling “best practices” that are relevant to epidemiology. These include, but are not limited to, methods for ABM construction and calibration (including the incorporation of data from different populations and various sources); model validation procedures (including reproducibility of observed real-world phenomena); and acceptable analytic practices (including sufficient sensitivity analyses to evaluate model robustness). Such standards have been developed in other disciplines (4, 5) but remain in their infancy throughout epidemiology. It is our hope that the debate from which our work was motivated continues to inspire the development and adoption of rigorous standards for agent-based modeling in our field. We also agree with Hernan that agent-based modeling requires a shift away from causal inference pursuits that are exclusively data driven. However, we do not advocate for the application of ABMs that are entirely suppositional and void of empirical foundation. In addition to theory and prior knowledge, the construction and calibration of valid simulation models rest on the availability of relevant and unbiased data. We concur with Diez Roux that systems modeling will not replace experimental or observational research—in fact, “best practice” ABMs are wholly reliant on it. We have learned from our past work (6, 7) that the utility of agent-based modeling is significantly improved when relevant data are readily accessible. Ideally, modeling and primary data collection should explicitly inform each other and proceed in tandem. Successful examples of iterative, multimethod approaches to epidemiologic research are currently limited and should be supported. Although elucidation of the dynamics and evolution of complex health systems are worthy avenues of investigation in their own right, epidemiology has identified, and should continue to identify, tangible disease causes that are both modifiable and amenable to public health intervention. As Hernan and Diez Roux discuss, agent-based modeling allows us to move away from questions of causal isolation with downstream foci to a more systems-based and life course–oriented epidemiology. We add to the challenges noted by each author that there is a clear risk of building ABMs so complex that meaningful causal inference (and subsequent public health action) is obfuscated. ABMs informed by all relevant theory and all available data tend to be cumbersome and of limited scientific utility (8). The process of simplification—noted by Diez Roux to be a prerequisite for empirical science—is therefore equally essential in complex systems science. In observational studies, we select confounders based on prior knowledge (informed by data) and conceptual models (e.g., directed acyclic graphs). So too should the construction, calibration, and analysis of ABMs be informed by prior knowledge, valid data, and grounded theory. The charge for epidemiologists, then, is to identify public health questions that are sufficiently complex to warrant a systems science approach and about which relevant data and adequate understanding of the system's subcomponents exist to construct and calibrate valid models.

Profilin-1 overexpression in MDA-MB-231 breast cancer cells is associated with alterations in proteomics biomarkers of cell proliferation, survival, and motility as revealed by global proteomics analyses.

Despite early screening programs and new therapeutic strategies, metastatic breast cancer is still the leading cause of cancer death in women in industrialized countries and regions. There is a need for novel biomarkers of susceptibility, progression, and therapeutic response. Global analyses or systems science approaches with omics technologies offer concrete ways forward in biomarker discovery for breast cancer. Previous studies have shown that expression of profilin-1 (PFN1), a ubiquitously expressed actin-binding protein, is downregulated in invasive and metastatic breast cancer. It has also been reported that PFN1 overexpression can suppress tumorigenic ability and motility/invasiveness of breast cancer cells. To obtain insights into the underlying molecular mechanisms of how elevating PFN1 level induces these phenotypic changes in breast cancer cells, we investigated the alteration in global protein expression profiles of breast cancer cells upon stable overexpression of PFN1 by a combination of three different proteome analysis methods (2-DE, iTRAQ, label-free). Using MDA-MB-231 as a model breast cancer cell line, we provide evidence that PFN1 overexpression is associated with alterations in the expression of proteins that have been functionally linked to cell proliferation (FKPB1A, HDGF, MIF, PRDX1, TXNRD1, LGALS1, STMN1, LASP1, S100A11, S100A6), survival (HSPE1, HSPB1, HSPD1, HSPA5 and PPIA, YWHAZ, CFL1, NME1) and motility (CFL1, CORO1B, PFN2, PLS3, FLNA, FLNB, NME2, ARHGDIB). In view of the pleotropic effects of PFN1 overexpression in breast cancer cells as suggested by these new findings, we propose that PFN1-induced phenotypic changes in cancer cells involve multiple mechanisms. Our data reported here might also offer innovative strategies for identification and validation of novel therapeutic targets and companion diagnostics for persons with, or susceptibility to, breast cancer.