A modelling tool for policy analysis to support the design of efficient and effective policy responses for complex public health problems

Temporal meta-optimiser based sensitivity analysis (TMSA) for agent-based models and applications in children's services

With current and predicted economic pressures within English Children's Services in the UK, there is a growing discourse around the development of methods of analysis using existing data to make more effective interventions and policy decisions. Agent-Based modelling shows promise in aiding in this, with limitations that require novel methods to overcome. This can include challenges in managing model complexity, transparency, and validation; which may deter analysts from implementing such Agent-Based simulations. Children's Services specifically can gain from the expansion of modelling techniques available to them. Sensitivity analysis is a common step when analysing models that currently has methods with limitations regarding Agent-Based Models. This paper outlines an improved method of conducting Sensitivity Analysis to enable better utilisation of Agent-Based models (ABMs) within Children's Services. By using machine learning based regression in conjunction with the Nomadic Peoples Optimiser (NPO) a method of conducting sensitivity analysis tailored for ABMs is achieved. This paper demonstrates the effectiveness of the approach by drawing comparisons with common existing methods of sensitivity analysis, followed by a demonstration of an improved ABM design in the target use case.

Which programmes and policies across health and community settings will generate the most significant impacts for youth suicide prevention in Australia and the UK? Protocol for a systems modelling and simulation study

INTRODUCTION

Suicide is a leading cause of mortality among young people aged 15-24 globally. Despite the deployment of comprehensive suicide prevention strategies, we still do not know which interventions, for which groups of young people, for how long and with what intensity could generate the most significant reductions in suicide rates. System dynamics modelling has the potential to address these gaps. SEYMOUR (System Dynamics Modelling for Suicide Prevention) will develop and evaluate a system dynamics model that will indicate which suicide prevention interventions could generate the most significant reductions in rates of suicide and attempted suicide among young people aged 12-25 in Australia and the UK.

METHODS AND ANALYSIS

A comparative case study design, applying participatory system dynamics modelling in North-West Melbourne (Australia) and Birmingham (UK). A computer simulation model of mental health service pathways and suicidal behaviour among young people in North-West Melbourne will be developed through three workshops with expert stakeholder groups (young people with lived experience, carers, clinicians, policy makers, commissioners). The model will be calibrated and validated using national, state and local datasets (inputs). The simulation model will test a series of interventions identified in the workshops for inclusion. Primary model outputs include suicide deaths, self-harm hospitalisations and self-harm presentations to emergency departments. An implementation strategy for the sustainable embedding of promising suicide prevention interventions will be developed. This will be followed by model customisation, re-parameterisation, and validation in Birmingham and adaptation of the implementation strategy.

ETHICS AND DISSEMINATION

The project has received approval from the University of Melbourne Human Research Ethics Committee (2022-22885-25971-4), the University of Birmingham Science, Technology, Engineering and Mathematics Ethics Review Committee (ERN_21-02385) and the UK HRA (22/HRA/3826). SEYMOUR's dissemination strategy includes open-access academic publications, conference presentations, accessible findings coproduced with young people, e-briefs to policy makers, webinars for service providers and commissioners.

Making the invisible visible: Using a qualitative system dynamics model to map disparities in cumulative environmental stressors and children's neurodevelopment

BACKGROUND

The combined effects of multiple environmental toxicants and social stressor exposures are widely recognized as important public health problems, likely contributing to health inequities. However, US policy makers at state and federal levels typically focus on one stressor exposure at a time and have failed to develop comprehensive strategies to reduce multiple co-occurring exposures, mitigate cumulative risks and prevent harm. This research aimed to move from considering disparate environmental stressors in isolation to mapping the links between environmental, economic, social and health outcomes as a dynamic complex system using children's exposure to neurodevelopmental toxicants as an illustrative example. Such a model can be used to support a broad range of child developmental and environmental health policy stakeholders in improving their understanding of cumulative effects of multiple chemical, physical, biological and social environmental stressors as a complex system through a collaborative learning process.

METHODS

We used system dynamics (SD) group model building to develop a qualitative causal theory linking multiple interacting streams of social stressors and environmental neurotoxicants impacting children's neurodevelopment. A 2 1/2-day interactive system dynamics workshop involving experts across multiple disciplines was convened to develop the model followed by qualitative survey on system insights.

RESULTS

The SD causal map covered seven interconnected themes: environmental exposures, social environment, health status, education, employment, housing and advocacy. Potential high leverage intervention points for reducing disparities in children's cumulative neurotoxicant exposures and effects were identified. Workshop participants developed deeper level of understanding about the complexity of cumulative environmental health risks, increased their agreement about underlying causes, and enhanced their capabilities for integrating diverse forms of knowledge about the complex multi-level problem of cumulative chemical and non-chemical exposures.

CONCLUSION

Group model building using SD can lead to important insights to into the sociological, policy, and institutional mechanisms through which disparities in cumulative impacts are transmitted, resisted, and understood.

Constituent-driven health policy informed by policy advocacy literature

In this position paper, a theoretical framework is proposed to formulate engaged, evidence-based health policy based on the priorities of constituents. An initial literature review was conducted to gain insight on the gaps in knowledge. Three emergent domains were identified: advocacy, research, and policymaking. The inputs and intermediates to the final output (equitable, evidence-based health policy outcomes) were identified and further elaborated upon in each corresponding section of the paper. Additionally, the main objective of each domain based on the literature review and the implications of each step were noted. Researchers have been identified as crucial to the education of policymakers to ultimately produce informed, evidence-based policy. Community advocates and researchers must attempt to advocate for policy issues as the ultimate role of policymakers in this process necessitates effective engagement to promote political will in the policymaking process. To do so, community advocates must scale-up from the individual to coalitions with strong leadership. In conjunction with a policy champion, these efforts by constituents (community advocates and researchers) would result in the most effective modes of policy development and implementation. The Constituent-driven Policy Advocacy Model (CPAM) introduced in this paper creates the potential for a new precedent in policymaking, in which advocacy, community engagement, evidence synthesis and evaluation, as well as science communication are common practices, leading to more sensitive, targeted, and equitable policy outcomes.

What are the core concerns of policy analysis? A multidisciplinary investigation based on in-depth bibliometric analysis

Policy analysis provides multiple methods and tools for generating and transforming policy-relevant information and supporting policy evolution to address emerging social problems. In this study, a bibliometric analysis of a large number of studies on historical policy analysis was performed to provide a comprehensive understanding of the distribution and evolution of policy problems in different fields among countries. The analysis indicates that policy analysis has been a great concern for scholars in recent two decades, and is involved in multiple disciplines, among which the dominant ones are medicine, environment, energy and economy. The major concerns of policy analysts and scholars are human health needs, environmental pressures, energy consumption caused by economic growth and urbanization, and the resulting demand for sustainable development. The multidisciplinary dialog implies the complicated real-world social problems that calls for more endeavors to develop a harmonious society. A global profiling for policy analysis demonstrates that the central policy problems and the corresponding options align with national development, for example, developing countries represented by China are faced with greater environmental pressures after experiencing extensive economic growth, while developed countries such as the USA and the UK pay more attention to the social issues of health and economic transformation. Exploring the differences in policy priorities among countries can provide a new inspiration for further dialog and cooperation on the development of the international community in the future.

Assessing Risks in Dairy Supply Chain Systems: A System Dynamics Approach

Due to the dynamic nature of the food supply chain system, food supply management could suffer because of, and be interrupted by, unforeseen events. Considering the perishable nature of fresh food products and their short life cycle, fresh food companies feel immense pressure to adopt an efficient and proactive risk management system. The risk management aspects within the food supply chains have been addressed in several studies. However, only a few studies focus on the complex interactions between the various types of risks impacting food supply chain functionality and dynamic feedback effects, which can generate a reliable risk management system. This paper strives to contribute to this evident research gap by adopting a system dynamics modelling approach to generate a systemic risk management model. The system dynamics model serves as the basis for the simulation of risk index values and can be explored in future work to further analyse the dynamic risk’s effect on the food supply chain system’s behaviour. According to a literature review of published research from 2017 to 2021, nine different risks across the food supply chain were identified as a subsection of the major risk categories: macro-level and operational risks. Following this stage, two of the risk groups identified first were integrated with a developed system dynamics model to conduct this research and to evaluate the interaction between the risks and the functionality of the three main dairy supply chain processes: production, logistics, and retailing. The key findings drawn from this paper can be beneficial for enhancing managerial discernment regarding the critical role of system dynamics models for analysing various types of risks across the food supply chain process and improving its efficiency.

Using decision analysis to support implementation planning in research and practice

BACKGROUND

The process of implementing evidence-based interventions, programs, and policies is difficult and complex. Planning for implementation is critical and likely plays a key role in the long-term impact and sustainability of interventions in practice. However, implementation planning is also difficult. Implementors must choose what to implement and how best to implement it, and each choice has costs and consequences to consider. As a step towards supporting structured and organized implementation planning, we advocate for increased use of decision analysis.

MAIN TEXT

When applied to implementation planning, decision analysis guides users to explicitly define the problem of interest, outline different plans (e.g., interventions/actions, implementation strategies, timelines), and assess the potential outcomes under each alternative in their context. We ground our discussion of decision analysis in the PROACTIVE framework, which guides teams through key steps in decision analyses. This framework includes three phases: (1) definition of the decision problems and overall objectives with purposeful stakeholder engagement, (2) identification and comparison of different alternatives, and (3) synthesis of information on each alternative, incorporating uncertainty. We present three examples to illustrate the breadth of relevant decision analysis approaches to implementation planning.

CONCLUSION

To further the use of decision analysis for implementation planning, we suggest areas for future research and practice: embrace model thinking; build the business case for decision analysis; identify when, how, and for whom decision analysis is more or less useful; improve reporting and transparency of cost data; and increase collaborative opportunities and training.

Individual Workplace Well-Being Captured into a Literature- and Stakeholders-Based Causal Loop Diagram

This study demonstrates an innovative approach to capture the complexity of individual workplace well-being, improving our understanding of multicausal relationships and feedback loops involved. The literature shows that a high number of interacting factors are related to individual workplace well-being. However, many studies focus on subsets of factors, and causal loops are seldomly studied. The aim of the current study was, therefore, to capture individual workplace well-being in a comprehensive conceptual causal loop diagram (CLD). We followed an iterative, qualitative, and transdisciplinary systems-thinking approach including literature search, group model building sessions, retrospective in-depth interviews with employees, and group sessions with human resource professionals, managers, job coaches, and management consultants. The results were discussed with HR and well-being officers of twelve organizations for their critical reflection on the recognizability and potential of the developed CLD. The final result, a conceptual individual workplace well-being CLD, provides a comprehensive overview of multiple, measurable key factors relating to individual workplace well-being and of the way these factors may causally interact over time, either improving or deteriorating workplace well-being. In future studies, the CLD can be translated to a quantitative system dynamics model for simulating workplace well-being scenarios. Ultimately, these simulations could be used to design effective workplace well-being interventions.

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

Planning for suicide prevention in Thai refugee camps: Using community-based system dynamics modeling

Suicide and associated behaviors represent a significant health disparity among refugees and displaced persons. Despite this burden, evidence for prevention programing in these populations is limited. This study aimed to inform the selection and implementation of suicide prevention strategies in refugee camps in Northwestern, Thailand - camps that had experienced recent spikes in suicides and suicide attempts at the time of the study. We leveraged Community Based System Dynamics modeling through a series of four workshops with key local stakeholders and suicide prevention experts, to build a qualitative systems model that accounts for complexities and is aimed at assisting local partners with selecting the most promising strategies for implementation and evaluation. The process expanded local understanding of the causes and consequences of suicide and resulted in selection of priority interventions aimed at reducing suicide in this context. Our research illustrates the application of a novel methodology that aims to account for the complexities of suicide prevention in the context of displacement and helps to optimize local suicide prevention efforts.

Federal and state cooperation necessary but not sufficient for effective regional mental health systems: insights from systems modelling and simulation

For more than a decade, suicide rates in Australia have shown no improvement despite significant investment in reforms to support regionally driven initiatives. Further recommended reforms by the Productivity Commission call for Federal and State and Territory Government funding for mental health to be pooled and new Regional Commissioning Authorities established to take responsibility for efficient and effective allocation of ‘taxpayer money.’ This study explores the sufficiency of this recommendation in preventing ongoing policy resistance. A system dynamics model of pathways between psychological distress, the mental health care system, suicidal behaviour and their drivers was developed, tested, and validated for a large, geographically diverse region of New South Wales; the Hunter New England and Central Coast Primary Health Network (PHN). Multi-objective optimisation was used to explore potential discordance in the best-performing programs and initiatives (simulated from 2021 to 2031) across mental health outcomes between the two state-governed Local Health Districts (LHDs) and the federally governed PHN. Impacts on suicide deaths, mental health-related emergency department presentations, and service disengagement were explored. A combination of family psychoeducation, post-attempt aftercare, and safety planning, and social connectedness programs minimises the number of suicides across the PHN and in the Hunter New England LHD (13.5% reduction; 95% interval, 12.3–14.9%), and performs well in the Central Coast LHD (14.8% reduction, 13.5–16.3%), suggesting that aligned strategic decision making between the PHN and LHDs would deliver substantial impacts on suicide. Results also highlighted a marked trade-off between minimising suicide deaths versus minimising service disengagement. This is explained in part by the additional demand placed on services of intensive suicide prevention programs leading to increases in service disengagement as wait times for specialist community based mental health services and dissatisfaction with quality of care increases. Competing priorities between the PHN and LHDs (each seeking to optimise the different outcomes they are responsible for) can undermine the optimal impact of investments for suicide prevention. Systems modelling provides essential regional decision analysis infrastructure to facilitate coordinated federal and state investments for optimal impacts.

Agent-Based Modeling for Implementation Research: An Application to Tobacco Smoking Cessation for Persons with Serious Mental Illness

Background

Implementation researchers have sought ways to use simulations to support the core components of implementation, which typically include assessing the need for change, designing implementation strategies, executing the strategies, and evaluating outcomes. The goal of this paper is to explain how agent-based modeling could fulfill this role.

Methods

We describe agent-based modeling with respect to other simulation methods that have been used in implementation science, using non-technical language that is broadly accessible. We then provide a stepwise procedure for developing agent-based models of implementation processes. We use, as a case study to illustrate the procedure, the implementation of evidence-based smoking cessation practices for persons with serious mental illness (SMI) in community mental health clinics.

Results

For our case study, we present descriptions of the motivating research questions, specific models used to answer these questions, and a summary of the insights that can be obtained from the models. In the first example, we use a simple form of agent-based modeling to simulate the observed smoking behaviors of persons with SMI in a recently completed trial (IDEAL, Comprehensive Cardiovascular Risk Reduction Trial in Persons with SMI). In the second example, we illustrate how a more complex agent-based approach that includes interactions between patients, providers and site administrators can be used to provide guidance for an implementation intervention that includes training and organizational strategies. This example is based in part on an ongoing project focused on scaling up evidence-based tobacco smoking cessation practices in community mental health clinics in Maryland.

Conclusion

In this paper we explain how agent-based models can be used to address implementation science research questions and provide a procedure for setting up simulation models. Through our examples, we show how what-if scenarios can be examined in the implementation process, which are particularly useful in implementation frameworks with adaptive components.

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.

Systems modelling and simulation to inform strategic decision making for suicide prevention in rural New South Wales (Australia)

BACKGROUND

The need to understand and respond to the unique characteristics and drivers of suicidal behaviour in rural areas has been enabled through the Australian Government's 2015 mental health reforms facilitating a move to an evidence-based, regional approach to suicide prevention. However, a key challenge has been the complex decision-making environment and lack of appropriate tools to facilitate the use of evidence, data and expert knowledge in a way that can inform contextually appropriate strategies that will deliver the greatest impact. This paper reports the co-development of an advanced decision support tool that enables regional decision makers to explore the likely impacts of their decisions before implementing them in the real world.

METHODS

A system dynamics model for the rural and remote population catchment of Western New South Wales was developed. The model was based on defined pathways to mental health care and suicidal behaviour and reproduced historic trends in the incidence of attempted suicide (self-harm hospitalisations) and suicide deaths in the region. A series of intervention scenarios were investigated to forecast their impact on suicidal behaviour over a 10-year period.

RESULTS

Post-suicide attempt assertive aftercare was forecast to deliver the greatest impact, reducing the numbers of self-harm hospitalisations and suicide deaths by 5.65% (95% interval, 4.87-6.42%) and 5.45% (4.68-6.22%), respectively. Reductions were also projected for community support programs (self-harm hospitalisations: 2.83%, 95% interval 2.23-3.46%; suicide deaths: 4.38%, 95% interval 3.78-5.00%). Some scenarios produced unintuitive impacts or effect sizes that were significantly lower than what has been anticipated under the traditional evidence-based approach to suicide prevention and provide an opportunity for learning.

CONCLUSION

Systems modelling and simulation offers significant potential for regional decision makers to better understand and respond to the unique characteristics and drivers of suicidal behaviour in their catchments and more effectively allocate limited health resources.

Bringing new tools, a regional focus, resource-sensitivity, local engagement and necessary discipline to mental health policy and planning

Background

While reducing the burden of mental and substance use disorders is a global challenge, it is played out locally. Mental disorders have early ages of onset, syndromal complexity and high individual variability in course and response to treatment. As most locally-delivered health systems do not account for this complexity in their design, implementation, scale or evaluation they often result in disappointing impacts.

Discussion

In this viewpoint, we contend that the absence of an appropriate predictive planning framework is one critical reason that countries fail to make substantial progress in mental health outcomes. Addressing this missing infrastructure is vital to guide and coordinate national and regional (local) investments, to ensure limited mental health resources are put to best use, and to strengthen health systems to achieve the mental health targets of the 2015 Sustainable Development Goals.

Most broad national policies over-emphasize provision of single elements of care (e.g. medicines, individual psychological therapies) and assess their population-level impact through static, linear and program logic-based evaluation. More sophisticated decision analytic approaches that can account for complexity have long been successfully used in non-health sectors and are now emerging in mental health research and practice. We argue that utilization of advanced decision support tools such as systems modelling and simulation, is now required to bring a necessary discipline to new national and local investments in transforming mental health systems.

Conclusion

Systems modelling and simulation delivers an interactive decision analytic tool to test mental health reform and service planning scenarios in a safe environment before implementing them in the real world. The approach drives better decision-making and can inform the scale up of effective and contextually relevant strategies to reduce the burden of mental disorder and enhance the mental wealth of nations.

'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.

Integrating HIV, syphilis, malaria and anaemia point-of-care testing (POCT) for antenatal care at dispensaries in western Kenya: discrete-event simulation modelling of operational impact

BACKGROUND

Despite WHO advocating for an integrated approach to antenatal care (ANC), testing coverage for conditions other than HIV remains low and women are referred to distant laboratories for testing. Using point-of-care tests (POCTs) at peripheral dispensaries could improve access to testing and timely treatment. However, the effect of providing additional services on nurse workload and client wait times are unknown. We use discrete-event simulation (DES) modelling to understand the effect of providing four point-of-care tests for ANC on nurse utilization and wait times for women seeking maternal and child health (MCH) services.

METHODS

We collected detailed time-motion data over 20 days from one high volume dispensary in western Kenya during the 8-month implementation period (2014-2015) of the intervention. We constructed a simulation model using empirical arrival distributions, activity durations and client pathways of women seeking MCH services. We removed the intervention from the model to obtain wait times, length-of-stay and nurse utilization rates for the baseline scenario where only HIV testing was offered for ANC. Additionally, we modelled a scenario where nurse consultations were set to have minimum durations for sufficient delivery of all WHO-recommended services.

RESULTS

A total of 183 women visited the dispensary for MCH services and 14 of these women received point-of-care testing (POCT). The mean difference in total waiting time was 2 min (95%CI: < 1-4 min, p = 0.026) for MCH women when integrated POCT was given, and 9 min (95%CI: 4-14 min, p < 0.001) when integrated POCT with adequate ANC consult times was given compared to the baseline scenario. Mean length-of-stay increased by 2 min (95%CI: < 1-4 min, p = 0.015) with integrated POCT and by 16 min (95%CI: 10-21 min, p < 0.001) with integrated POCT and adequate consult times compared to the baseline scenario. The two nurses' overall daily utilization in the scenario with sufficient minimum consult durations were 72 and 75%.

CONCLUSION

The intervention had a modest overall impact on wait times and length-of-stay for women seeking MCH services while ensuring pregnant women received essential diagnostic testing. Nurse utilization rates fluctuated among days: nurses experienced spikes in workload on some days but were under-utilized on the majority of days. Overall, our model suggests there was sufficient time to deliver all WHO's required ANC activities and offer integrated testing for ANC first and re-visits with the current number of healthcare staff. Further investigations on improving healthcare worker, availability, performance and quality of care are needed. Delivering four point-of-care tests together for ANC at dispensary level would be a low burden strategy to improve ANC.

[The health sector and early childhood intervention: intersectoral collaboration in research]

BACKGROUND

Early childhood interventions are locally and regionally organized support services for families from pregnancy until the end of the third year of life. The interventions promote diverse measures to enhance parental skills in order to improve developmental and living circumstances. One crucial element of early childhood intervention in Germany are prevention networks at municipal level. The collaboration of healthcare professionals and child and youth welfare professionals in these networks aims to provide nonstigmatizing access to early childhood intervention for families with psychosocial burdens. From the point of view of the healthcare sector, the research program Together for Families (ZuFa Monitoring) of the National Centre on Early Prevention (NZFH) at the Federal Centre for Health Education (BZgA) has collected representative data at the interfaces of gynecology, obstetrics, pediatrics, and early childhood intervention since 2017.

GOAL

The background and goals, as well as design and methods of the ZuFa Monitoring studies are described. For obstetrics clinics and resident pediatricians, sample descriptions, including data on representativeness and early data regarding collaboration quality, are given.

EARLY RESULTS

The samples are representative for the population of obstetric clinics and resident pediatricians in Germany. At least two-thirds of the respondents indicate that the proportion of families with psychosocial burdens has increased. Care for psychosocially burdened families is regarded as challenging due to a lack of time, limited financial compensation, and aggravating conditions, such as language barriers. Respondents expect early childhood intervention to alleviate their daily work.

DISCUSSION

ZuFa Monitoring collects data regarding the care for families with psychosocial burdens at the interface of the health and the child and youth welfare sector. The research program generates information on inhibitory as well as promoting factors, thereby informing the further development and expansion of prevention networks at the municipal level, and heightening the quality of care for families in the health sector.

The impact of strengthening mental health services to prevent suicidal behaviour

OBJECTIVE

Successive suicide prevention frameworks and action plans in Australia and internationally have called for improvements to mental health services and enhancement of workforce capacity. However, there is debate regarding the priorities for resource allocation and the optimal combination of mental health services to best prevent suicidal behaviour. This study investigates the potential impacts of service capacity improvements on the incidence of suicidal behaviour in the Australian context.

METHODS

A system dynamics model was developed to investigate the optimal combination of (1) secondary (acute) mental health service capacity, (2) non-secondary (non-acute) mental health service capacity and (3) resources to re-engage those lost to services on the incidence of suicidal behaviour over the period 2018-2028 for the Greater Western Sydney (Australia) population catchment. The model captured population and behavioural dynamics and mental health service referral pathways and was validated using population survey and administrative data, evidence syntheses and an expert stakeholder group.

RESULTS

Findings suggest that 28% of attempted suicide and 29% of suicides could be averted over the forecast period based on a combination of increases in (1) hospital staffing (with training in trauma-informed care), (2) non-secondary health service capacity, (3) expansion of mental health assessment capacity and (4) re-engagement of at least 45% of individuals lost to services. Reduction in the number of available psychiatric beds by 15% had no substantial impact on the incidence of attempted suicide and suicide over the forecast period.

CONCLUSION

This study suggests that more than one-quarter of suicides and attempted suicides in the Greater Western Sydney population catchment could potentially be averted with a combination of increases to hospital staffing and non-secondary (non-acute) mental health care. Reductions in tertiary care services (e.g. psychiatric hospital beds) in combination with these increases would not adversely affect subsequent incidence of suicidal behaviour.

Analysis of the national hearing health care policy implementation in a Brazilian State

PURPOSE

The present study aimed to analyze the degree of implementation of the national health care policy at the state level.

METHODS

This qualitative evaluation study was carried out in two stages. Firstly, the policy was modelled by means of document analysis and the application of the Delphi technique for consensus among experts. In the second stage, a qualitative, exploratory evaluative research was conducted, designed as a single case study in a Brazilian state through semi-structured interviews with health managers.

RESULTS

The experts reached a consensus for a logical model and an evaluation matrix of the policy implementation. The results at the state level evinced an incipient degree of implementation, as the level of government characteristics achieved 45% of the maximum score; management, 41%; and system organization, 33%.

CONCLUSION

The degree of implementation in the state evaluated was classified as incipient. Barriers were identified in the management and organization levels of the system, as well as in the political context.

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

BACKGROUND

System science approaches are increasingly used to explore complex public health problems. Quantitative methods, such as participatory dynamic simulation modelling, can mobilise knowledge to inform health policy decisions. However, the analytic and practical steps required to turn collaboratively developed, qualitative system maps into rigorous and policy-relevant quantified dynamic simulation models are not well described. This paper reports on the processes, interactions and decisions that occurred at the interface between modellers and end-user participants in an applied health sector case study focusing on diabetes in pregnancy.

METHODS

An analysis was conducted using qualitative data from a participatory dynamic simulation modelling case study in an Australian health policy setting. Recordings of participatory model development workshops and subsequent meetings were analysed and triangulated with field notes and other written records of discussions and decisions. Case study vignettes were collated to illustrate the deliberations and decisions made throughout the model development process.

RESULTS

The key analytic objectives and decision-making processes included: defining the model scope; analysing and refining the model structure to maximise local relevance and utility; reviewing and incorporating evidence to inform model parameters and assumptions; focusing the model on priority policy questions; communicating results and applying the models to policy processes. These stages did not occur sequentially; the model development was cyclical and iterative with decisions being re-visited and refined throughout the process. Storytelling was an effective strategy to both communicate and resolve concerns about the model logic and structure, and to communicate the outputs of the model to a broader audience.

CONCLUSION

The in-depth analysis reported here examined the application of participatory modelling methods to move beyond qualitative conceptual mapping to the development of a rigorously quantified and policy relevant, complex dynamic simulation model. The analytic objectives and decision-making themes identified provide guidance for interpreting, understanding and reporting future participatory modelling projects and methods.

Spatial dynamics of access to primary care for the medicaid population

Primary care (PC) has always been underestimated and underinvested by the United States health system. Our goal was to investigate the effect of Medicaid expansion and the Affordable Care Act (ACA) provisions on PC access in Broome County, NY, a county that includes both rural and urban areas, and can serve as a benchmark for other regions. We developed a spatial system dynamics model to capture different stages of PC access for the Medicaid population by using the health belief model constructs and simulate the effect of several hypothetical interventions on PC utilisation. The government data portals used as data sources for calibrating our model include the New York State Department of Health, the Medicaid Delivery System Reform Incentive Payment (DSRIP) dashboards, and the US census. In our unique approach, we integrated the simulation results within Geographical Information System (GIS) maps, to assess the influence of geospatial factors on PC access. Our results identify hot spot demographic areas that have poor access to PC service facilities due to transportation constraints and a shortage in PC providers. Our decision support tool informs policymakers about programmes with the strongest impact on improving access to care, considering spatial and temporal characteristics of a region.

Decision makers' experience of participatory dynamic simulation modelling: methods for public health policy

BACKGROUND

Systems science methods such as dynamic simulation modelling are well suited to address questions about public health policy as they consider the complexity, context and dynamic nature of system-wide behaviours. Advances in technology have led to increased accessibility and interest in systems methods to address complex health policy issues. However, the involvement of policy decision makers in health-related simulation model development has been lacking. Where end-users have been included, there has been limited examination of their experience of the participatory modelling process and their views about the utility of the findings. This paper reports the experience of end-user decision makers, including senior public health policy makers and health service providers, who participated in three participatory simulation modelling for health policy case studies (alcohol related harm, childhood obesity prevention, diabetes in pregnancy), and their perceptions of the value and efficacy of this method in an applied health sector context.

METHODS

Semi-structured interviews were conducted with end-user participants from three participatory simulation modelling case studies in Australian real-world policy settings. Interviewees were employees of government agencies with jurisdiction over policy and program decisions and were purposively selected to include perspectives at different stages of model development.

RESULTS

The 'co-production' aspect of the participatory approach was highly valued. It was reported as an essential component of building understanding of the modelling process, and thus trust in the model and its outputs as a decision-support tool. The unique benefits of simulation modelling included its capacity to explore interactions of risk factors and combined interventions, and the impact of scaling up interventions. Participants also valued simulating new interventions prior to implementation in the real world, and the comprehensive mapping of evidence and its gaps to prioritise future research. The participatory aspect of simulation modelling was time and resource intensive and therefore most suited to high priority complex topics with contested options for intervening.

CONCLUSION

These findings highlight the value of a participatory approach to dynamic simulation modelling to support its utility in applied health policy settings.

A decision support tool to inform local suicide prevention activity in Greater Western Sydney (Australia)

OBJECTIVES

This study describes the development of a decision support tool to identify the combination of suicide prevention activities and service priorities likely to deliver the greatest reductions in suicidal behaviour in Western Sydney (Australia) over the period 2018-2028.

METHODS

A dynamic simulation model for the WentWest - Western Sydney Primary Health Network population-catchment was developed in partnership with primary health network stakeholders based on defined pathways to mental health care and suicidal behaviour, and which represented the current incidence of suicide and attempted suicide in Western Sydney. A series of scenarios relating to potential suicide prevention activities and service priorities identified by primary health network stakeholders were investigated to identify the combination of interventions associated with the largest reductions in the forecast number of attempted suicide and suicide cases for a 10-year follow-up period.

RESULTS

The largest number of cases averted for both suicide and attempted suicide was associated with (1) post-suicide attempt assertive aftercare (6.1% for both attempted suicide and suicide), (2) improved community support and reductions in psychological distress in the community (5.1% for attempted suicide and 14.8% for suicide), and (3) reductions in the proportion of those lost to services following a mental health service contact (10.5% for both attempted suicide and suicide). In combination, these interventions were forecast to avert approximately 29.7% of attempted suicides and 37.1% of suicides in the primary health network catchment over the 10-year period.

CONCLUSION

This study demonstrates the utility of dynamic simulation models, co-designed with multi-disciplinary stakeholder groups, to capture and analyse complex mental health and suicide prevention regional planning problems. The model can be used by WentWest - Western Sydney Primary Health Network as a decision support tool to guide the commissioning of future service activity, and more efficiently frame the monitoring and evaluation of interventions as they are implemented in Western Sydney.

Can big data solve a big problem? Reporting the obesity data landscape in line with the Foresight obesity system map

Background

Obesity research at a population level is multifaceted and complex. This has been characterised in the UK by the Foresight obesity systems map, identifying over 100 variables, across seven domain areas which are thought to influence energy balance, and subsequent obesity. Availability of data to consider the whole obesity system is traditionally lacking. However, in an era of big data, new possibilities are emerging. Understanding what data are available can be the first challenge, followed by an inconsistency in data reporting to enable adequate use in the obesity context. In this study we map data sources against the Foresight obesity system map domains and nodes and develop a framework to report big data for obesity research. Opportunities and challenges associated with this new data approach to whole systems obesity research are discussed.

Methods

Expert opinion from the ESRC Strategic Network for Obesity was harnessed in order to develop a data source reporting framework for obesity research. The framework was then tested on a range of data sources. In order to assess availability of data sources relevant to obesity research, a data mapping exercise against the Foresight obesity systems map domains and nodes was carried out.

Results

A reporting framework was developed to recommend the reporting of key information in line with these headings: Background; Elements; Exemplars; Content; Ownership; Aggregation; Sharing; Temporality (BEE-COAST). The new BEE-COAST framework was successfully applied to eight exemplar data sources from the UK. 80% coverage of the Foresight obesity systems map is possible using a wide range of big data sources. The remaining 20% were primarily biological measurements often captured by more traditional laboratory based research.

Conclusions

Big data offer great potential across many domains of obesity research and need to be leveraged in conjunction with traditional data for societal benefit and health promotion.

Static metrics of impact for a dynamic problem: The need for smarter tools to guide suicide prevention planning and investment

OBJECTIVES

This study investigates two approaches to estimate the potential impact of a population-level intervention on Australian suicide, to highlight the importance of selecting appropriate analytic approaches for informing evidence-based strategies for suicide prevention.

METHODS

The potential impact of a psychosocial therapy intervention on the incidence of suicide in Australia over the next 10 years was used as a case study to compare the potential impact on suicides averted using: (1) a traditional epidemiological measure of population attributable risk and (2) a dynamic measure of population impact based on a systems science model of suicide that incorporates changes over time.

RESULTS

Based on the population preventive fraction, findings suggest that the psychosocial therapy intervention if implemented among all eligible individuals in the Australian population would prevent 5.4% of suicides (or 1936 suicides) over the next 10 years. In comparison, estimates from the dynamic simulation model which accounts for changes in the effect size of the intervention over time, the time taken for the intervention to have an impact in the population, and likely barriers to the uptake and availability of services suggest that the intervention would avert a lower proportion of suicides (between 0.4% and 0.5%) over the same follow-up period.

CONCLUSION

Traditional epidemiological measures used to estimate population health burden have several limitations that are often understated and can lead to unrealistic expectations of the potential impact of evidence-based interventions in real-world settings. This study highlights these limitations and proposes an alternative analytic approach to guide policy and practice decisions to achieve reductions in Australian suicide.

Impacts of licensed premises trading hour policies on alcohol-related harms

BACKGROUND AND AIM

Evaluations of alcohol policy changes demonstrate that restriction of trading hours of both 'on'- and 'off'-licence venues can be an effective means of reducing rates of alcohol-related harm. Despite this, the effects of different trading hour policy options over time, accounting for different contexts and demographic characteristics, and the common co-occurrence of other harm reduction strategies in trading hour policy initiatives, are difficult to estimate. The aim of this study was to use dynamic simulation modelling to compare estimated impacts over time of a range of trading hour policy options on various indicators of acute alcohol-related harm.

METHODS

An agent-based model of alcohol consumption in New South Wales, Australia was developed using existing research evidence, analysis of available data and a structured approach to incorporating expert opinion. Five policy scenarios were simulated, including restrictions to trading hours of on-licence venues and extensions to trading hours of bottle shops. The impact of the scenarios on four measures of alcohol-related harm were considered: total acute harms, alcohol-related violence, emergency department (ED) presentations and hospitalizations.

RESULTS

Simulation of a 3 a.m. (rather than 5 a.m.) closing time resulted in an estimated 12.3 ± 2.4% reduction in total acute alcohol-related harms, a 7.9 ± 0.8% reduction in violence, an 11.9 ± 2.1% reduction in ED presentations and a 9.5 ± 1.8% reduction in hospitalizations. Further reductions were achieved simulating a 1 a.m. closing time, including a 17.5 ± 1.1% reduction in alcohol-related violence. Simulated extensions to bottle shop trading hours resulted in increases in rates of all four measures of harm, although most of the effects came from increasing operating hours from 10 p.m. to 11 p.m.

CONCLUSIONS

An agent-based simulation model suggests that restricting trading hours of licensed venues reduces rates of alcohol-related harm and extending trading hours of bottle shops increases rates of alcohol-related harm. The model can estimate the effects of a range of policy options.

The application of system dynamics modelling to environmental health decision-making and policy - a scoping review

BACKGROUND

Policy and decision-making processes are routinely challenged by the complex and dynamic nature of environmental health problems. System dynamics modelling has demonstrated considerable value across a number of different fields to help decision-makers understand and predict the dynamic behaviour of complex systems in support the development of effective policy actions. In this scoping review we investigate if, and in what contexts, system dynamics modelling is being used to inform policy or decision-making processes related to environmental health.

METHODS

Four electronic databases and the grey literature were systematically searched to identify studies that intersect the areas environmental health, system dynamics modelling, and decision-making. Studies identified in the initial screening were further screened for their contextual, methodological and application-related relevancy. Studies deemed 'relevant' or 'highly relevant' according to all three criteria were included in this review. Key themes related to the rationale, impact and limitation of using system dynamics in the context of environmental health decision-making and policy were analysed.

RESULTS

We identified a limited number of relevant studies (n = 15), two-thirds of which were conducted between 2011 and 2016. The majority of applications occurred in non-health related sectors (n = 9) including transportation, public utilities, water, housing, food, agriculture, and urban and regional planning. Applications were primarily targeted at micro-level (local, community or grassroots) decision-making processes (n = 9), with macro-level (national or international) decision-making to a lesser degree. There was significant heterogeneity in the stated rationales for using system dynamics and the intended impact of the system dynamics model on decision-making processes. A series of user-related, technical and application-related limitations and challenges were identified. None of the reported limitations or challenges appeared unique to the application of system dynamics within the context of environmental health problems, but rather to the use of system dynamics in general.

CONCLUSIONS

This review reveals that while system dynamics modelling is increasingly being used to inform decision-making related to environmental health, applications are currently limited. Greater application of system dynamics within this context is needed before its benefits and limitations can be fully understood.

A socio-historical approach to policy analysis: the case of the Brazilian Workers’ Food Policy

Abstract: Policy analyses based on traditional or structuralist definitions of the state are important, but they have some limitations for explaining processes related to policymaking, implementation, and results. Bourdieusian sociology links the analysis to objective and subjective dimensions of social practices and can help elucidate these phenomena. This article provides such empirical evidence by analyzing the social genesis of a Brazilian policy that currently serves 18 million workers and was established by the state in 1976 through the Fiscal Incentives Program for Workers’ Nutrition (PIFAT/PAT). The study linked the analysis of the trajectory of social agents involved in the policy’s formulation to the historical conditions that allowed the policy to exist in the first place. Although the literature treats the policy as a workers’ food program (PAT), the current study showed that it actually represented a new model for paying financial subsidies to companies that provided food to their employees, meanwhile upgrading the commercial market for collective meals. The study further showed that the program emerged as an administrative policy, but linked to economic agents. The program became a specific social space in which issues related to workers’ nutrition became secondary, but useful for disguising what had been an explicit side of its genesis, namely its essentially fiscal nature.

Knowledge mobilisation for policy development: implementing systems approaches through participatory dynamic simulation modelling

BACKGROUND

Evidence-based decision-making is an important foundation for health policy and service planning decisions, yet there remain challenges in ensuring that the many forms of available evidence are considered when decisions are being made. Mobilising knowledge for policy and practice is an emergent process, and one that is highly relational, often messy and profoundly context dependent. Systems approaches, such as dynamic simulation modelling can be used to examine both complex health issues and the context in which they are embedded, and to develop decision support tools.

OBJECTIVE

This paper reports on the novel use of participatory simulation modelling as a knowledge mobilisation tool in Australian real-world policy settings. We describe how this approach combined systems science methodology and some of the core elements of knowledge mobilisation best practice. We describe the strategies adopted in three case studies to address both technical and socio-political issues, and compile the experiential lessons derived. Finally, we consider the implications of these knowledge mobilisation case studies and provide evidence for the feasibility of this approach in policy development settings.

CONCLUSION

Participatory dynamic simulation modelling builds on contemporary knowledge mobilisation approaches for health stakeholders to collaborate and explore policy and health service scenarios for priority public health topics. The participatory methods place the decision-maker at the centre of the process and embed deliberative methods and co-production of knowledge. The simulation models function as health policy and programme dynamic decision support tools that integrate diverse forms of evidence, including research evidence, expert knowledge and localised contextual information. Further research is underway to determine the impact of these methods on health service decision-making.

A modelling tool for capacity planning in acute and community stroke services

Background

Mathematical capacity planning methods that can take account of variations in patient complexity, admission rates and delayed discharges have long been available, but their implementation in complex pathways such as stroke care remains limited. Instead simple average based estimates are commonplace. These methods often substantially underestimate capacity requirements.

We analyse the capacity requirements for acute and community stroke services in a pathway with over 630 admissions per year. We sought to identify current capacity bottlenecks affecting patient flow, future capacity requirements in the presence of increased admissions, the impact of co-location and pooling of the acute and rehabilitation units and the impact of patient subgroups on capacity requirements. We contrast these results to the often used method of planning by average occupancy, often with arbitrary uplifts to cater for variability.

Methods

We developed a discrete-event simulation model using aggregate parameter values derived from routine administrative data on over 2000 anonymised admission and discharge timestamps. The model mimicked the flow of stroke, high risk TIA and complex neurological patients from admission to an acute ward through to community rehab and early supported discharge, and predicted the probability of admission delays.

Results

An increase from 10 to 14 acute beds reduces the number of patients experiencing a delay to the acute stroke unit from 1 in every 7 to 1 in 50. Co-location of the acute and rehabilitation units and pooling eight beds out of a total bed stock of 26 reduce the number of delayed acute admissions to 1 in every 29 and the number of delayed rehabilitation admissions to 1 in every 20. Planning by average occupancy would resulted in delays for one in every five patients in the acute stroke unit.

Conclusions

Planning by average occupancy fails to provide appropriate reserve capacity to manage the variations seen in stroke pathways to desired service levels. An appropriate uplift from the average cannot be based simply on occupancy figures. Our method draws on long available, intuitive, but underused mathematical techniques for capacity planning. Implementation via simulation at our study hospital provided valuable decision support for planners to assess future bed numbers and organisation of the acute and rehabilitation services.

Electronic supplementary material

The online version of this article (doi:10.1186/s12913-016-1789-4) contains supplementary material, which is available to authorized users.

Operational research as implementation science: definitions, challenges and research priorities

BACKGROUND

Operational research (OR) is the discipline of using models, either quantitative or qualitative, to aid decision-making in complex implementation problems. The methods of OR have been used in healthcare since the 1950s in diverse areas such as emergency medicine and the interface between acute and community care; hospital performance; scheduling and management of patient home visits; scheduling of patient appointments; and many other complex implementation problems of an operational or logistical nature.

DISCUSSION

To date, there has been limited debate about the role that operational research should take within implementation science. I detail three such roles for OR all grounded in upfront system thinking: structuring implementation problems, prospective evaluation of improvement interventions, and strategic reconfiguration. Case studies from mental health, emergency medicine, and stroke care are used to illustrate each role. I then describe the challenges for applied OR within implementation science at the organisational, interventional, and disciplinary levels. Two key challenges include the difficulty faced in achieving a position of mutual understanding between implementation scientists and research users and a stark lack of evaluation of OR interventions. To address these challenges, I propose a research agenda to evaluate applied OR through the lens of implementation science, the liberation of OR from the specialist research and consultancy environment, and co-design of models with service users. Operational research is a mature discipline that has developed a significant volume of methodology to improve health services. OR offers implementation scientists the opportunity to do more upfront system thinking before committing resources or taking risks. OR has three roles within implementation science: structuring an implementation problem, prospective evaluation of implementation problems, and a tool for strategic reconfiguration of health services. Challenges facing OR as implementation science include limited evidence and evaluation of impact, limited service user involvement, a lack of managerial awareness, effective communication between research users and OR modellers, and availability of healthcare data. To progress the science, a focus is needed in three key areas: evaluation of OR interventions, embedding the knowledge of OR in health services, and educating OR modellers about the aims and benefits of service user involvement.