Turning conceptual systems maps into dynamic simulation models: An Australian case study for diabetes in pregnancy

Considerations for using participatory systems modeling as a tool for implementation mapping in chronic disease prevention

Effective chronic disease prevention requires a systems approach to the design, implementation, and refinement of interventions that account for the complexity and interdependence of factors influencing health outcomes. This paper proposes the Participatory Implementation Systems Mapping (PISM) process, which combines participatory systems modeling with implementation strategy development to enhance intervention design and implementation planning. PISM leverages the collaborative efforts of researchers and community partners to analyze complex health systems, identify key determinants, and develop tailored interventions and strategies that are both adaptive and contextually relevant. The phases of the PISM process include strategize, innovate, operationalize, and assess. We describe and demonstrate how each phase contributes to the overall goal of effective and sustainable intervention implementation. We also address the challenges of data availability, model complexity, and resource constraints. We offer solutions such as innovative data collection methods and participatory model development to enhance the robustness and applicability of systems models. Through a case study on the development of a chronic disease prevention intervention, the paper illustrates the practical application of PISM and highlights its potential to guide epidemiologists and implementation scientists in developing interventions that are responsive to the complexities of real-world health systems. The conclusion calls for further research to refine participatory systems modeling techniques, overcome existing challenges in data availability, and expand the use of PISM in diverse public health contexts.

Insights from a codesigned dynamic modelling study of child and adolescent obesity in Australia

Introduction

Child and adolescent obesity is associated with a range of immediate health issues and influences obesity in adulthood. The complex nature of health determinants that contribute to obesity makes it challenging to deliver effective public health interventions. This research presents insights from a system dynamics model of childhood and adolescent obesity aimed at supporting evidence-based decision-making.

Methods

A system dynamics model was developed using the best available evidence and data, with input from research and industry experts to map the hypothetical causal structure of the factors contributing to childhood and adolescent obesity in Australia. The model was calibrated to fit the historical prevalence of obesity (R 2 =0.97, mean squared error (MSE)=4.94E-04). Intervention-based scenarios were simulated to examine how changes in environmental factors and health-related behaviours may affect the prevalence of obesity. The potential economic benefits of the scenarios were estimated from changes in population healthcare spending and quality of life compared with base model projections.

Results

A series of interventions were explored in the model, including changes in early childhood behaviours, changes to diet and physical activity in childcare and school settings, financial support for organised sports and sugar-sweetened beverage taxation. The most promising individually implemented intervention scenario for reducing the prevalence of childhood and adolescent obesity was a sugar-sweetened beverage tax (0.57 percentage points and 0.61 percentage points, respectively) and government funding of organised sports (0.42 percentage points and 0.63 percentage points, respectively). When all interventions were implemented in combination, childhood obesity was reduced by 1.43 percentage points and 1.81 percentage points in adolescents.

Conclusions

The findings highlight the challenges faced by policy-makers and public health practitioners working to reduce childhood and adolescent obesity. Insights from the model emphasise the value of public health programmes over the life course. Implementing initiatives with broad reach that support healthy choices may reduce obesity, resulting in a healthier Australian population.

Early COVID-19 Pandemic Preparedness: Informing Public Health Interventions and Hospital Capacity Planning Through Participatory Hybrid Simulation Modeling

We engaged with health sector stakeholders and public health professionals within the health system through a participatory modeling approach to support policy-making in the early COVID-19 pandemic in Saskatchewan, Canada. The objective was to use simulation modeling to guide the implementation of public health measures and short-term hospital capacity planning to mitigate the disease burden from March to June 2020. We developed a hybrid simulation model combining System Dynamics (SD), discrete-event simulation (DES), and agent-based modeling (ABM). SD models the population-level transmission of COVID-19, ABM simulates individual-level disease progression and contact tracing intervention, and DES captures COVID-19-related hospital patient flow. We examined the impact of mixed mitigation strategies-physical distancing, testing, conventional and digital contact tracing-on COVID-19 transmission and hospital capacity for a worst-case scenario. Modeling results showed that enhanced contact tracing with mass testing in the early pandemic could significantly reduce transmission, mortality, and the peak census of hospital beds and intensive care beds. Using a participatory modeling approach, we not only directly informed policy-making on contact tracing interventions and hospital surge capacity planning for COVID-19 but also helped validate the effectiveness of the interventions adopted by the provincial government. We conclude with a discussion on lessons learned and the novelty of our hybrid approach.

A Holistic Way to Understand the Determinants of Physical Activity in Urban New South Wales, Australia: A Codesigned Systems Mapping Project

BACKGROUND

To meet the World Health Organization goal of reducing physical inactivity by 15% by 2030, a multisectoral system approach is urgently needed to promote physical activity (PA). We report the process of and findings from a codesigned systems mapping project to present determinants of PA in the context of urban New South Wales, Australia.

METHODS

A participatory conceptual mapping workshop was held in May 2023 with 19 participants working in education, transportation, urban planning, community, health, and sport and recreation. Initial maps were developed and refined using online feedback from the participants. Interviews were conducted with 10 additional policymakers from relevant sectors to further refine the maps.

RESULTS

Two systems maps were cocreated, identifying over 100 variables influencing PA and their interconnections. Five settings emerged from the adults' map-social and community, policy, built environment and transportation, health care, and workplace-and 4 for the young people's map-family, school, transportation, and community and environment. The maps share similarities, such as regarding potential drivers within the transportation, community, and built environment sectors; however, the young people's map has a specific focus on the school setting and the adults' map on workplace and health care settings. Interviews with policymakers provided further unique insights into understanding and intervening in the PA system.

CONCLUSIONS

This codesigned participatory systems mapping process, supplemented by stakeholder interviews, provided a unique opportunity to bring together stakeholders across sectors to understand the complexity within the PA system and begin to identify leverage points for tackling physical inactivity in New South Wales.

Participatory Modeling for High Complexity, Multi-System Issues: Challenges and Recommendations for Balancing Qualitative Understanding and Quantitative Questions

Community stakeholder participation can be incredibly valuable for the qualitative model development process. However, modelers often encounter challenges for participatory modeling projects focusing on high-complexity, synergistic interactions between multiple issues, systems, and granularity. The diverse stakeholder perspectives and volumes of information necessary for developing such models can yield qualitative models that are difficult to translate into quantitative simulation or clear insight for informed decision-making. There are few reccomended best practices for developing high-complexity, participatory models. We use an ongoing project as a case study to highlight three practical challenges for tackling high-complexity, multi-system issues with system dynamics tools. These challenges include balanced and respectful stakeholder engagement, defining boundaries and levels of variable aggregation, and timing and processes for qualitative/quantitative model integration. Our five recommendations to address these challenges serve as a foundation for further research on methods for developing translatable qualitative multi-system models for informing actions for systemic change.

Health economic analysis of polygenic risk score use in primary prevention of coronary artery disease - A system dynamics model

Background

Primary prevention programs utilising traditional risk scores fail to identify all individuals who suffer acute cardiovascular events. We aimed to model the impact and cost effectiveness of incorporating a Polygenic risk scores (PRS) into the cardiovascular disease CVD primary prevention program in Australia, using a whole-of-system model.

Methods

System dynamics models, encompassing acute and chronic CVD care in the Australian healthcare setting, assessing the cost-effectiveness of incorporating a CAD-PRS in the primary prevention setting. The time horizon was 10-years.

Results

Pragmatically incorporating a CAD-PRS in the Australian primary prevention setting in middle-aged individuals already attending a Heart Health Check (HHC) who are determined to be at low or moderate risk based on the 5-year Framingham risk score (FRS), with conservative assumptions regarding uptake of PRS, could have prevented 2, 052 deaths over 10-years, and resulted in 24, 085 QALYs gained at a cost of $19, 945 per QALY with a net benefit of $724 million. If all Australians overs the age of 35 years old had their FRS and PRS performed, and acted upon, 12, 374 deaths and 60, 284 acute coronary events would be prevented, with 183, 682 QALYs gained at a cost of $18, 531 per QALY, with a net benefit of $5, 780 million.

Conclusions

Incorporating a CAD-PRS in a contemporary primary prevention setting in Australia would result in substantial health and societal benefits and is cost-effective. The broader the uptake of CAD-PRS in the primary prevention setting in middle-aged Australians, the greater the impact and the more cost-effective the strategy.

A competency framework on simulation modelling-supported decision-making for Master of Public Health graduates

BACKGROUND

Simulation models are increasingly important for supporting decision-making in public health. However, due to lack of training, many public health professionals remain unfamiliar with constructing simulation models and using their outputs for decision-making. This study contributes to filling this gap by developing a competency framework on simulation model-supported decision-making targeting Master of Public Health education.

METHODS

The study combined a literature review, a two-stage online Delphi survey and an online consensus workshop. A draft competency framework was developed based on 28 peer-reviewed publications. A two-stage online Delphi survey involving 15 experts was conducted to refine the framework. Finally, an online consensus workshop, including six experts, evaluated the competency framework and discussed its implementation.

RESULTS

The competency framework identified 20 competencies related to stakeholder engagement, problem definition, evidence identification, participatory system mapping, model creation and calibration and the interpretation and dissemination of model results. The expert evaluation recommended differentiating professional profiles and levels of expertise and synergizing with existing course contents to support its implementation.

CONCLUSIONS

The competency framework developed in this study is instrumental to including simulation model-supported decision-making in public health training. Future research is required to differentiate expertise levels and develop implementation strategies.

Conceptualising the value of simulation modelling for public engagement with policy: a critical literature review

As we face complex and dynamically changing public health and environmental challenges, simulation modelling has come to occupy an increasingly central role in public engagements with policy. Shifts are occurring not only in terms of wider public understandings of modelling, but also in how the value of modelling is conceptualised within scientific modelling communities. We undertook a critical literature review to synthesise the underlying epistemic, theoretical and methodological assumptions about the role and value of simulation modelling within the literature across a range of fields (e.g., health, social science and environmental management) that engage with participatory modelling approaches. We identified four cross-cutting narrative conceptualisations of the value of modelling across different research traditions: (1) models simulate and help solve complex problems; (2) models as tools for community engagement; (3) models as tools for consensus building; (4) models as volatile technologies that generate social effects. Exploring how these ideas of 'value' overlap and what they offer one another has implications for how participatory simulation modelling approaches are designed, evaluated and communicated to diverse audiences. Deeper appreciation of the conditions under which simulation modelling can catalyse multiple social effects is recommended.

Can participatory processes lead to changes in the configuration of local mental health networks? A social network analysis

Systems modeling offers a valuable tool to support strategic decision-making for complex problems because it considers the causal inter-relationships that drive population health outcomes. This tool can be used to simulate policies and initiatives to determine which combinations are likely to deliver the greatest impacts and returns on investment. Systems modeling benefits from participatory approaches where a multidisciplinary stakeholder group actively engages in mapping and contextualizing causal mechanisms driving complex system behaviors. Such approaches can have significant advantages, including that they may improve connection and coordination of the network of stakeholders operating across the system; however, these are often observed in practice as colloquial anecdotes and seldom formally assessed. We used a basic social network analysis to explore the impact on the configuration of the network of mental health providers, decision-makers, and other stakeholders in Bogota, Colombia active in a series of three workshops throughout 2021 and 2022. Overall, our analysis suggests that the participatory process of the systems dynamics exercise impacts the social network's structure, relationships, and dynamics.

The use of participatory systems mapping as a research method in the context of non-communicable diseases and risk factors: a scoping review

CONTEXT

Participatory systems mapping is increasingly used to gain insight into the complex systems surrounding non-communicable diseases (NCDs) and their risk factors.

OBJECTIVES

To identify and synthesize studies that used participatory systems mapping in the context of non-communicable diseases.

DESIGN

Scoping review.

ELIGIBILITY CRITERIA

Peer-reviewed studies published between 2000 and 2022.

STUDY SELECTION

Studies that focused on NCDs and/or related risk factors, and included participants at any stage of their system's mapping process, were included.

CATEGORIES FOR ANALYSIS

The main categories for analysis were: (1) problem definition and goal-setting, (2) participant involvement, (3) structure of the mapping process, (4) validation of the systems map, and (5) evaluation of the mapping process.

RESULTS

We identified 57 studies that used participatory systems mapping for a variety of purposes, including to inform or evaluate policies or interventions and to identify potential leverage points within a system. The number of participants ranged from 6 to 590. While policymakers and professionals were the stakeholder groups most often included, some studies described significant added value from including marginalized communities. There was a general lack of formal evaluation in most studies. However, reported benefits related mostly to individual and group learning, whereas limitations described included a lack of concrete actions following from systems mapping exercises.

CONCLUSIONS

Based on the findings of this review, we argue that research using participatory systems mapping would benefit from considering three different but intertwined actions: explicitly considering how different participants and the power imbalances between them may influence the participatory process, considering how the results from a systems mapping exercise may effectively inform policy or translate into action, and including and reporting on evaluation and outcomes of the process, wherever possible.

Systems Thinking and Complexity Science Methods and the Policy Process in Non-communicable Disease Prevention: A Systematic Scoping Review

BACKGROUND

Given the complex determinants of non-communicable diseases (NCDs), and the dynamic policy landscape, researchers and policymakers are exploring the use of systems thinking and complexity science (STCS) in developing effective policies. The aim of this review is to systematically identify and analyse existing applications of STCS-informed methods in NCD prevention policy.

METHODS

Systematic scoping review: We searched academic databases (Medline, Scopus, Web of Science, EMBASE) for all publications indexed by 13 October 2020, screening titles, abstracts and full texts and extracting data according to published guidelines. We summarised key data from each study, mapping applications of methods informed by STCS to policy process domains. We conducted a thematic analysis to identify advantages, limitations, barriers and facilitators to using STCS.

RESULTS

4681 papers were screened and 112 papers were included in this review. The most common policy areas were tobacco control, obesity prevention and physical activity promotion. Methods applied included system dynamics modelling, agent-based modelling and concept mapping. Advantages included supporting evidence-informed decision-making; modelling complex systems and addressing multi-sectoral problems. Limitations included the abstraction of reality by STCS methods, despite aims of encompassing greater complexity. Challenges included resource-intensiveness; lack of stakeholder trust in models; and results that were too complex to be comprehensible to stakeholders. Ensuring stakeholder ownership and presenting findings in a user-friendly way facilitated STCS use.

CONCLUSION

This review maps the proliferating applications of STCS methods in NCD prevention policy. STCS methods have the potential to generate tailored and dynamic evidence, adding robustness to evidence-informed policymaking, but must be accessible to policy stakeholders and have strong stakeholder ownership to build consensus and change stakeholder perspectives. Evaluations of whether, and under what circumstances, STCS methods lead to more effective policies compared to conventional methods are lacking, and would enable more targeted and constructive use of these methods.

Participatory Methods for Systems Modeling of Youth Mental Health: Implementation Protocol

Background

Despite significant investment, mental health issues remain a leading cause of death among young people globally. Sophisticated decision analysis methods are needed to better understand the dynamic and multisector drivers of youth mental health. System modeling can help explore complex issues such as youth mental health and inform strategies to effectively respond to local needs and achieve lasting improvements. The advantages of engaging stakeholders in model development processes have long been recognized; however, the methods for doing so are often not well-described.

Objective

This paper aims to describe the participatory procedures that will be used to support systems modeling for national multisite implementation. The Right Care, First Time, Where You Live research program will focus on regional youth mental health applications of systems modeling in 8 different sites across Australia.

Methods

The participatory model development approach involves an iterative process of engaging with a range of participants, including people with lived experience of mental health issues. Their knowledge of the local systems, pathways, and drivers is combined with the academic literature and data to populate the models and validate their structure. The process centers around 3 workshops where participants interact and actively engage in group model-building activities to define, refine, and validate the systems models. This paper provides a detailed blueprint for the implementation of this process for mental health applications.

Results

The participatory modeling methods described in this paper will be implemented at 2 sites per year from 2022 to 2025. The 8 selected sites have been chosen to capture variations in important factors, including determinants of mental health issues and access to services. Site engagement commenced in August 2021, and the first modeling workshops are scheduled to commence in February 2022.

Conclusions

Mental health system decision makers require tools to help navigate complex environments and leverage interdisciplinary problem-solving. Systems modeling can mobilize data from diverse sources to explore a range of scenarios, including the impact of interventions in different combinations and contexts. Involving stakeholders in the model development process ensures that the model findings are context-relevant and fit-for-purpose to inform decision-making.

International Registered Report Identifier (IRRID)

PRR1-10.2196/32988

Computational Simulation Is a Vital Resource for Navigating the COVID-19 Pandemic

INTRODUCTION

COVID-19 has prompted the extensive use of computational models to understand the trajectory of the pandemic. This article surveys the kinds of dynamic simulation models that have been used as decision support tools and to forecast the potential impacts of nonpharmaceutical interventions (NPIs). We developed the Values in Viral Dispersion model, which emphasizes the role of human factors and social networks in viral spread and presents scenarios to guide policy responses.

METHODS

An agent-based model of COVID-19 was developed with individual agents able to move between 3 states (susceptible, infectious, or recovered), with each agent placed in 1 of 7 social network types and assigned a propensity to comply with NPIs (quarantine, contact tracing, and physical distancing). A series of policy questions were tested to illustrate the impact of social networks and NPI compliance on viral spread among (1) populations, (2) specific at-risk subgroups, and (3) individual trajectories.

RESULTS

Simulation outcomes showed large impacts of physical distancing policies on number of infections, with substantial modification by type of social network and level of compliance. In addition, outcomes on metrics that sought to maximize those never infected (or recovered) and minimize infections and deaths showed significantly different epidemic trajectories by social network type and among higher or lower at-risk age cohorts.

CONCLUSIONS

Although dynamic simulation models have important limitations, which are discussed, these decision support tools should be a key resource for navigating the ongoing impacts of the COVID-19 pandemic and can help local and national decision makers determine where, when, and how to invest resources.

Community-based Systems Dynamics for Reproductive Health: An Example from Urban Ohio

BACKGROUND

Health outcomes, risk factors, and policies are complexly related to the reproductive health system. Systems-level frameworks for understanding and acting within communities through community-engaged research are needed to mitigate adverse reproductive health outcomes more effectively within the community.

OBJECTIVES

To describe and share lessons learned from an ongoing application of a participatory modeling approach (community-based system dynamics) that aims to eliminate racial inequities in Black-White reproductive health outcomes.

METHODS

The community-based system dynamics approach involves conducting complementary activities, workshops, modeling, and dissemination. We organized workshops, co-developed a causal loop diagram of the reproductive health system with participants from the community, and created materials to disseminate workshop findings and preliminary models.

LESSONS LEARNED

Many opportunities exist for cross-fertilization of best practices between community-based system dynamics and community-based participatory research. Shared learning environments offer benefits for modelers and domain experts alike. Additionally, identifying local champions from the community helps manage group dynamics.

CONCLUSIONS

Community-based system dynamics is well-suited for understanding complexity in the reproductive health system. It allows participants from diverse perspectives to identify strategies to eliminate racial inequities in reproductive health outcomes.

Gazing through time and beyond the health sector: Insights from a system dynamics model of cardiovascular disease in Australia

OBJECTIVE

To construct a whole-of-system model to inform strategies that reduce the burden of cardiovascular disease (CVD) in Australia.

METHODS

A system dynamics model was developed with a multidisciplinary modelling consortium. The model population comprised Australians aged 40 years and over, and the scope encompassed acute and chronic CVD as well as primary and secondary prevention. Health outcomes were CVD-related deaths and hospitalisations, and economic outcomes were the net benefit from both the healthcare system and societal perspectives. The eight strategies broadly included creating social and physical environments supportive of a healthy lifestyle, increasing the use of preventive treatments, and improving systems response to acute CVD events. The effects of strategies were estimated as relative differences to the business-as-usual between 2019-2039. Probabilistic sensitivity analysis produced uncertainty intervals of interquartile ranges (IQR).

FINDINGS

The greatest reduction in CVD-related deaths was seen in strategies that improve systems response to acute CVD events (8.9%, IQR: 7.7-10.2%), yet they resulted in an increase in CVD-related hospitalisations due to future recurrent admissions (1.6%, IQR: 0.1-2.3%). This flow-on effect highlighted the importance of addressing underlying CVD risks. On the other hand, strategies targeting the broad environment that supports a healthy lifestyle were effective in reducing both hospitalisations (7.1%; IQR: 5.0-9.5%) and deaths (8.1% reduction; IQR: 7.1-8.9%). They also produced an economic net benefit of AU$43.3 billion (IQR: 37.7-48.7) using a societal perspective, largely driven by productivity gains. Overall, strategic planning to reduce the burden of CVD should consider the varying effects of strategies over time and beyond the health sector.

Reducing youth suicide: systems modelling and simulation to guide targeted investments across the determinants

BACKGROUND

Reducing suicidal behaviour (SB) is a critical public health issue globally. The complex interplay of social determinants, service system factors, population demographics, and behavioural dynamics makes it extraordinarily difficult for decision makers to determine the nature and balance of investments required to have the greatest impacts on SB. Real-world experimentation to establish the optimal targeting, timing, scale, frequency, and intensity of investments required across the determinants is unfeasible. Therefore, this study harnesses systems modelling and simulation to guide population-level decision making that represent best strategic allocation of limited resources.

METHODS

Using a participatory approach, and informed by a range of national, state, and local datasets, a system dynamics model was developed, tested, and validated for a regional population catchment. The model incorporated defined pathways from social determinants of mental health to psychological distress, mental health care, and SB. Intervention scenarios were investigated to forecast their impact on SB over a 20-year period.

RESULTS

A combination of social connectedness programs, technology-enabled coordinated care, post-attempt assertive aftercare, reductions in childhood adversity, and increasing youth employment projected the greatest impacts on SB, particularly in a youth population, reducing self-harm hospitalisations (suicide attempts) by 28.5% (95% interval 26.3-30.8%) and suicide deaths by 29.3% (95% interval 27.1-31.5%). Introducing additional interventions beyond the best performing suite of interventions produced only marginal improvement in population level impacts, highlighting that 'more is not necessarily better.'

CONCLUSION

Results indicate that targeted investments in addressing the social determinants and in mental health services provides the best opportunity to reduce SB and suicide. Systems modelling and simulation offers a robust approach to leveraging best available research, data, and expert knowledge in a way that helps decision makers respond to the unique characteristics and drivers of SB in their catchments and more effectively focus limited health resources.

Defining and Intervening on Cumulative Environmental Neurodevelopmental Risks: Introducing a Complex Systems Approach

BACKGROUND

The combined effects of multiple environmental toxicants and social stressor exposures are widely recognized as important public health problems contributing to health inequities. However cumulative environmental health risks and impacts have received little attention from U.S. policy makers at state and federal levels to develop comprehensive strategies to reduce these exposures, mitigate cumulative risks, and prevent harm. An area for which the inherent limitations of current approaches to cumulative environmental health risks are well illustrated is children's neurodevelopment, which exhibits dynamic complexity of multiple interdependent and causally linked factors and intergenerational effects.

OBJECTIVES

We delineate how a complex systems approach, specifically system dynamics, can address shortcomings in environmental health risk assessment regarding exposures to multiple chemical and nonchemical stressors and reshape associated public policies.

DISCUSSION

Systems modeling assists in the goal of solving problems by improving the "mental models" we use to make decisions, including regulatory and policy decisions. In the context of disparities in children's cumulative exposure to neurodevelopmental stressors, we describe potential policy insights about the structure and behavior of the system and the types of system dynamics modeling that would be appropriate, from visual depiction (i.e., informal maps) to formal quantitative simulation models. A systems dynamics framework provides not only a language but also a set of methodological tools that can more easily operationalize existing multidisciplinary scientific evidence and conceptual frameworks on cumulative risks. Thus, we can arrive at more accurate diagnostic tools for children's' environmental health inequities that take into consideration the broader social and economic environment in which children live, grow, play, and learn. https://doi.org/10.1289/EHP7333.

A systems map of the determinants of child health inequalities in England at the local level

Children and young people in the UK have worse health outcomes than in many similar western countries and child health inequalities are persistent and increasing. Systems thinking has emerged as a promising approach to addressing complex public health issues. We report on a systems approach to mapping the determinants of child health inequalities at the local level in England for young people aged 0-25, and describe the resulting map. Qualitative group concept mapping workshops were held in two contrasting English local authorities with a range of stakeholders: professionals (N = 35); children and young people (N = 33) and carers (N = 5). Initial area maps were developed, and augmented using data from qualitative interviews with professionals (N = 16). The resulting local maps were reviewed and validated by expert stakeholders in each area (N = 9; N = 35). Commonalities between two area-specific system maps (and removal of locality-specific factors) were used to develop a map that could be applied in any English local area. Two rounds of online survey (N = 21; N = 8) experts in public health, local governance and systems science refined the final system map displaying the determinants of child health inequalities. The process created a map of over 150 factors influencing inequalities in health outcomes for children aged 0-25 years at the local area level. The system map has six domains; physical environment, governance, economic, social, service, and personal. To our knowledge this is the first study taking a systems approach to addressing inequalities across all aspects of child health. The study shows how group concept mapping can support systems thinking at the local level. The resulting system map illustrates the complexity of factors influencing child health inequalities, and it may be a useful tool in demonstrating to stakeholders the importance of policies that tackle the systemic drivers of child health inequalities beyond those traditionally associated with public health.

Engaging Healthcare Staff and Stakeholders in Healthcare Simulation Modeling to Better Translate Research Into Health Impact: A Systematic Review

Objective: To identify processes to engage stakeholders in healthcare Simulation Modeling (SM), and the impacts of this engagement on model design, model implementation, and stakeholder participants. To investigate how engagement process may lead to specific impacts. Data Sources: English-language articles on health SM engaging stakeholders in the MEDLINE, EMBASE, Scopus, Web of Science and Business Source Complete databases published from inception to February 2020. Study Design: A systematic review of the literature based on a priori protocol and reported according to PRISMA guidelines. Extraction Methods: Eligible articles were SM studies with a health outcome which engaged stakeholders in model design. Data were extracted using a data extraction form adapted to be specific for stakeholder engagement in SM studies. Data were analyzed using summary statistics, deductive and inductive content analysis, and narrative synthesis. Principal Findings: Thirty-two articles met inclusion criteria. Processes used to engage stakeholders in healthcare SM are heterogenous and often based on intuition rather than clear methodological frameworks. These processes most commonly involve stakeholders across multiple SM stages via discussion/dialogue, interviews, workshops and meetings. Key reported impacts of stakeholder engagement included improved model quality/accuracy, implementation, and stakeholder decision-making. However, for all but four studies, these reports represented author perceptions rather than formal evaluations incorporating stakeholder perspectives. Possible process enablers of impact included the use of models as "boundary objects" and structured facilitation via storytelling to promote effective communication and mutual understanding between stakeholders and modelers. Conclusions: There is a large gap in the current literature of formal evaluation of SM stakeholder engagement, and a lack of consensus about the processes required for effective SM stakeholder engagement. The adoption and clear reporting of structured engagement and process evaluation methodologies/frameworks are required to advance the field and produce evidence of impact.

Applying systems thinking to knowledge mobilisation in public health

CONTEXT

Knowledge mobilisation (KM) is a vital strategy in efforts to improve public health policy and practice. Linear models describing knowledge transfer and translation have moved towards multi-directional and complexity-attuned approaches where knowledge is produced and becomes meaningful through social processes. There are calls for systems approaches to KM but little guidance on how this can be operationalised. This paper describes the contribution that systems thinking can make to KM and provides guidance about how to put it into action.

METHODS

We apply a model of systems thinking (which focuses on leveraging change in complex systems) to eight KM practices empirically identified by others. We describe how these models interact and draw out some key learnings for applying systems thinking practically to KM in public health policy and practice. Examples of empirical studies, tools and targeted strategies are provided.

FINDINGS

Systems thinking can enhance and fundamentally transform KM. It upholds a pluralistic view of knowledge as informed by multiple parts of the system and reconstituted through use. Mobilisation is conceived as a situated, non-prescriptive and potentially destabilising practice, no longer conceptualised as a discrete piece of work within wider efforts to strengthen public health but as integral to and in continual dialogue with those efforts. A systems approach to KM relies on contextual understanding, collaborative practices, addressing power imbalances and adaptive learning that responds to changing interactions between mobilisation activities and context.

CONCLUSION

Systems thinking offers valuable perspectives, tools and strategies to better understand complex problems in their settings and for strengthening KM practice. We make four suggestions for further developing empirical evidence and debate about how systems thinking can enhance our capacity to mobilise knowledge for solving complex problems - (1) be specific about what is meant by 'systems thinking', (2) describe counterfactual KM scenarios so the added value of systems thinking is clearer, (3) widen conceptualisations of impact when evaluating KM, and (4) use methods that can track how and where knowledge is mobilised in complex systems.

Novel participatory methods for co-building an agent-based model of physical activity with youth

Public health scholarship has increasingly called for the use of system science approaches to understand complex problems, including the use of participatory engagement to inform the modeling process. Some system science traditions, specifically system dynamics modeling, have an established participatory practice tradition. Yet, there remains limited guidance on engagement strategies using other modeling approaches like agent-based models. Our objective is to describe how we engaged adolescent youth in co-building an agent-based model about physical activity. Specifically, we aim to describe how we communicated technical aspects of agent-based models, the participatory activities we developed, and the resulting visual diagrams that were produced. We implemented six sessions with nine adolescent participants. To make technical aspects more accessible, we used an analogy that linked core components of agent-based models to elements of storytelling. We also implemented novel, facilitated activities that engaged youth in the development, annotation, and review of graphs over time, geographical maps, and state charts. The process was well-received by the participants and helped inform the basic structure of an agent-based model. The resulting visual diagrams created space for deeper discussion among participants about patterns of daily activity, important places for physical activity, and interactions between social and built environments. This work lays a foundation to develop and refine engagement strategies, especially for translating qualitative insights into quantitative model specifications such as ‘decision rules’.

'Turning the tide' on hyperglycemia in pregnancy: insights from multiscale dynamic simulation modeling

INTRODUCTION

Hyperglycemia in pregnancy (HIP, including gestational diabetes and pre-existing type 1 and type 2 diabetes) is increasing, with associated risks to the health of women and their babies. Strategies to manage and prevent this condition are contested. Dynamic simulation models (DSM) can test policy and program scenarios before implementation in the real world. This paper reports the development and use of an advanced DSM exploring the impact of maternal weight status interventions on incidence of HIP.

METHODS

A consortium of experts collaboratively developed a hybrid DSM of HIP, comprising system dynamics, agent-based and discrete event model components. The structure and parameterization drew on a range of evidence and data sources. Scenarios comparing population-level and targeted prevention interventions were simulated from 2018 to identify the intervention combination that would deliver the greatest impact.

RESULTS

Population interventions promoting weight loss in early adulthood were found to be effective, reducing the population incidence of HIP by 17.3% by 2030 (baseline ('business as usual' scenario)=16.1%, 95% CI 15.8 to 16.4; population intervention=13.3%, 95% CI 13.0 to 13.6), more than targeted prepregnancy (5.2% reduction; incidence=15.3%, 95% CI 15.0 to 15.6) and interpregnancy (4.2% reduction; incidence=15.5%, 95% CI 15.2 to 15.8) interventions. Combining targeted interventions for high-risk groups with population interventions promoting healthy weight was most effective in reducing HIP incidence (28.8% reduction by 2030; incidence=11.5, 95% CI 11.2 to 11.8). Scenarios exploring the effect of childhood weight status on entry to adulthood demonstrated significant impact in the selected outcome measure for glycemic regulation, insulin sensitivity in the short term and HIP in the long term.

DISCUSSION

Population-level weight reduction interventions will be necessary to 'turn the tide' on HIP. Weight reduction interventions targeting high-risk individuals, while beneficial for those individuals, did not significantly impact forecasted HIP incidence rates. The importance of maintaining interventions promoting healthy weight in childhood was demonstrated.

Teaching systems science to public health professionals

OBJECTIVES

Systems thinking aims to understand the overall behavior of a system by examining the interdependencies of parts of the system. The objective of this study is to increase awareness of systems thinking and systems modeling in public health research and practice.

STUDY DESIGN

A short course was offered to public health professionals using a combination of teaching modalities: didactic lectures, group discussions, hands-on programming, and experiential learning.

METHODS

Course participants completed surveys and provided feedback on the effectiveness of the course. A description of participant backgrounds, survey responses, and feedback were summarized.

RESULTS

Overall, participants offered quantitative and qualitative feedback suggesting that course content was useful and effective for incorporating systems thinking/modeling in their public health practice.

CONCLUSIONS

Systems thinking can be taught through formal modes of instruction to public health workers, but more research and case studies are needed to identify who should be taught and when and how such instruction should take place given competing priorities of public health workers.