Policy options for endgame planning in tobacco control: a simulation modelling study.

OBJECTIVE: To investigate the potential impacts of several tobacco control interventions on adult daily smoking prevalence in the Australian state of Queensland, using a system dynamics model codeveloped with local and national stakeholders. METHODS: Eight intervention scenarios were simulated and compared with a reference scenario (business as usual), in which all tobacco control measures currently in place are maintained unchanged until the end of the simulation period (31 December 2037). FINDINGS: Under the business as usual scenario, adult daily smoking prevalence is projected to decline from 11.8% in 2017 to 5.58% in 2037. A sustained 50% increase in antismoking advertising exposure from 2018 reduces projected prevalence in 2037 by 0.80 percentage points. Similar reductions are projected with the introduction of tobacco wholesaler and retailer licensing schemes that either permit or prohibit tobacco sales by alcohol-licensed venues (0.65 and 1.73 percentage points, respectively). Increasing the minimum age of legal supply of tobacco products substantially reduces adolescent initiation, but has minimal impact on smoking prevalence in the adult population over the simulation period. Sustained reductions in antismoking advertising exposure of 50% and 100% from 2018 increase projected adult daily smoking prevalence in 2037 by 0.88 and 1.98 percentage points, respectively. CONCLUSIONS: These results suggest that any prudent approach to endgame planning should seek to build on rather than replace existing tobacco control measures that have proved effective to date. Additional interventions that can promote cessation are expected to be more successful in reducing smoking prevalence than interventions focussing exclusively on preventing initiation.

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.

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.

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.

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.

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.

Who pays and who benefits? How different models of shared responsibilities between formal and informal carers influence projections of costs of dementia management.

BACKGROUND: The few studies that have attempted to estimate the future cost of caring for people with dementia in Australia are typically based on total prevalence and the cost per patient over the average duration of illness. However, costs associated with dementia care also vary according to the length of the disease, severity of symptoms and type of care provided. This study aimed to determine more accurately the future costs of dementia management by taking these factors into consideration. METHODS: The current study estimated the prevalence of dementia in Australia (2010-2040). Data from a variety of sources was recalculated to distribute this prevalence according to the location (home/institution), care requirements (informal/formal), and dementia severity. The cost of care was attributed to redistributed prevalences and used in prediction of future costs of dementia. RESULTS: Our computer modeling indicates that the ratio between the prevalence of people with mild/moderate/severe dementia will change over the three decades from 2010 to 2040 from 50/30/20 to 44/32/24.Taking into account the severity of symptoms, location of care and cost of care per hour, the current study estimates that the informal cost of care in 2010 is AU$3.2 billion and formal care at AU$5.0 billion per annum. By 2040 informal care is estimated to cost AU$11.6 billion and formal care $AU16.7 billion per annum. Interventions to slow disease progression will result in relative savings of 5% (AU$1.5 billion) per annum and interventions to delay disease onset will result in relative savings of 14% (AU$4 billion) of the cost per annum.With no intervention, the projected combined annual cost of formal and informal care for a person with dementia in 2040 will be around AU$38,000 (in 2010 dollars). An intervention to delay progression by 2 years will see this reduced to AU$35,000. CONCLUSIONS: These findings highlight the need to account for more than total prevalence when estimating the costs of dementia care. While the absolute values of cost of care estimates are subject to the validity and reliability of currently available data, dynamic systems modeling allows for future trends to be estimated.

In silico modeling systems: learning about the prevalence and dynamics of dementia through virtual experimentation.

BACKGROUND: Virtual experimentation using computer modeling creates opportunities for researchers who want to better understand disease processes, foresee effects of future demographics, and evaluate combinations of interventions when applied to larger target groups. METHODS: We created a computer model of dementia prevalence consisting of six population groups representing diagnosed and undiagnosed dementia at mild, moderate, and severe levels. Dynamic transitions between these groups corresponded to the gradual progression of disease. The seventh group represented the general population without dementia aged >60 years from which new dementia cases emerged. Through a series of virtual experiments we estimated future changes in the severity-specific prevalence of dementia in Australia. RESULTS: The projected total prevalence of dementia in Australia for year 2040 changed from 742,000 to 986,000 (+33%) and to 433,000 (-42%) when the incidence rate was altered by ±50%. Increasing the transition time between mild and moderate dementia from 5 to 7 years and between moderate to severe from 7 to 9 years increased the prevalence of mild dementia by 23% and decreased the prevalence of severe dementia by 24%. CONCLUSIONS: As computer modeling becomes more accepted, in silico experiments are being routinely performed to update demographic projections. Despite its simplicity, the framework of this model integrates a large pool of knowledge and consists of components which are dynamically interconnected. The computational logic underpins series of assumptions and binds them together with demographic data.

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.

A computer model of dementia prevalence in Australia: foreseeing outcomes of delaying dementia onset, slowing disease progression, and eradicating dementia types.

BACKGROUND: A computer model was designed to test hypothetical scenarios regarding dementia prevalence in Australia (2001-2040). METHODS: The study implemented 3 scenarios: delaying dementia onset, slowing disease progression and, in a previously unpublished experiment, eradicating dementia types. Sensitivity analysis and parameter variation were the main methods of experimentation. RESULTS: The model predicts that delaying dementia onset by 5 years will reduce the 2040 prevalence by 37%. An onset delay of 2 years, introduced in 2010, will reduce the 2040 prevalence by 16%. Slowing disease progression increases the 2040 prevalence by 4-7%. Total eradication of Alzheimer's disease (currently approximately 50% of all dementia cases) in 2020 will decrease the 2040 prevalence by 42%. CONCLUSION: Computer modeling of future scenarios and interventions helps health and aged care planners understand the likely challenges society will face with the ageing of the world's population.

A model linking clinical workforce skill mix planning to health and health care dynamics.

BACKGROUND: In an attempt to devise a simpler computable tool to assist workforce planners in determining what might be an appropriate mix of health service skills, our discussion led us to consider the implications of skill mixing and workforce composition beyond the 'stock and flow' approach of much workforce planning activity. METHODS: Taking a dynamic systems approach, we were able to address the interactions, delays and feedbacks that influence the balance between the major components of health and health care. RESULTS: We linked clinical workforce requirements to clinical workforce workload, taking into account the requisite facilities, technologies, other material resources and their funding to support clinical care microsystems; gave recognition to productivity and quality issues; took cognisance of policies, governance and power concerns in the establishment and operation of the health care system; and, going back to the individual, gave due attention to personal behaviour and biology within the socio-political family environment. CONCLUSION: We have produced the broad endogenous systems model of health and health care which will enable human resource planners to operate within real world variables. We are now considering the development of simple, computable national versions of this model.

'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.

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.

General practice work and workforce: Interdependencies between demand, supply and quality.

BACKGROUND: General practice is regarded as central to the Australian health system. However, issues affecting the general practitioner (GP) workforce have been focused mainly on remuneration, numbers and distribution. The focus is shifting to how best to enable GPs to deliver effective, efficient and equitable care. OBJECTIVE: The aim of this paper is to identify important elements, dynamics and interdependencies that influence GPs' work and their ability to continually improve outcomes for individuals and communities. DISCUSSION: Most important problems are multifaceted and cannot be reduced to a simple, single solution. Influence diagrams capture the interdependent domains that affect general practice, such as the variations in patients' needs in the community and the impact of disadvantage and care expectations on outcomes. Identifying interrelationships between key domains should capture the dynamics that 'feed the problem'. Finding 'best possible solutions' to improve interdependent system problems and avoid the inherent risk of unintended failures requires an ongoing mix of qualitative and quantitative modelling.

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.

Harnessing advances in computer simulation to inform policy and planning to reduce alcohol-related harms.

OBJECTIVES: Alcohol misuse is a complex systemic problem. The aim of this study was to explore the feasibility of using a transparent and participatory agent-based modelling approach to develop a robust decision support tool to test alcohol policy scenarios before they are implemented in the real world. METHODS: A consortium of Australia's leading alcohol experts was engaged to collaboratively develop an agent-based model of alcohol consumption behaviour and related harms. As a case study, four policy scenarios were examined. RESULTS: A 19.5 ± 2.5% reduction in acute alcohol-related harms was estimated with the implementation of a 3 a.m. licensed venue closing time plus 1 a.m. lockout; and a 9 ± 2.6% reduction in incidence was estimated with expansion of treatment services to reach 20% of heavy drinkers. Combining the two scenarios produced a 33.3 ± 2.7% reduction in the incidence of acute alcohol-related harms, suggesting a synergistic effect. CONCLUSIONS: This study demonstrates the feasibility of participatory development of a contextually relevant computer simulation model of alcohol-related harms and highlights the value of the approach in identifying potential policy responses that best leverage limited resources.

Using an accident model to design safe electronic medication management systems.

Large-scale implementation of electronic prescribing systems (e-PS) is likely to introduce at least some machinerelated errors that will harm patients. We present a dynamic systems modeling approach to developing a comprehensive multilevel accident model of the process, context and task interaction variables which give rise to human error and system failure when e-PS are used in routine care. System dynamics methods are used to represent interactions between medication management processes and the context that is relevant to error generation, interception and transmission, agent-based methods represent task interactions. Capturing the patterns of failure within an accident model will facilitate an evidence-based approach to hazard analysis and design of e-PS features to improve patient safety. The model will have broad potential to guide the design, implementation and regulation of e-PS.

A decision-support tool to inform Australian strategies for preventing suicide and suicidal behaviour.

Dynamic simulation modelling is increasingly being recognised as a valuable decision-support tool to help guide investments and actions to address complex public health issues such as suicide. In particular, participatory system dynamics (SD) modelling provides a useful tool for asking high-level 'what if' questions, and testing the likely impacts of different combinations of policies and interventions at an aggregate level before they are implemented in the real world. We developed an SD model for suicide prevention in Australia, and investigated the hypothesised impacts over the next 10 years (2015-2025) of a combination of current intervention strategies proposed for population interventions in Australia: 1) general practitioner (GP) training, 2) coordinated aftercare in those who have attempted suicide, 3) school-based mental health literacy programs, 4) brief-contact interventions in hospital settings, and 5) psychosocial treatment approaches. Findings suggest that the largest reductions in suicide were associated with GP training (6%) and coordinated aftercare approaches (4%), with total reductions of 12% for all interventions combined. This paper highlights the value of dynamic modelling methods for managing complexity and uncertainty, and demonstrates their potential use as a decision-support tool for policy makers and program planners for community suicide prevention actions.

Dynamic simulation modelling of policy responses to reduce alcohol-related harms: rationale and procedure for a participatory approach.

Development of effective policy responses to address complex public health problems can be challenged by a lack of clarity about the interaction of risk factors driving the problem, differing views of stakeholders on the most appropriate and effective intervention approaches, a lack of evidence to support commonly implemented and acceptable intervention approaches, and a lack of acceptance of effective interventions. Consequently, political considerations, community advocacy and industry lobbying can contribute to a hotly contested debate about the most appropriate course of action; this can hinder consensus and give rise to policy resistance. The problem of alcohol misuse and its associated harms in New South Wales (NSW), Australia, provides a relevant example of such challenges. Dynamic simulation modelling is increasingly being valued by the health sector as a robust tool to support decision making to address complex problems. It allows policy makers to ask 'what-if' questions and test the potential impacts of different policy scenarios over time, before solutions are implemented in the real world. Participatory approaches to modelling enable researchers, policy makers, program planners, practitioners and consumer representatives to collaborate with expert modellers to ensure that models are transparent, incorporate diverse evidence and perspectives, are better aligned to the decision-support needs of policy makers, and can facilitate consensus building for action. This paper outlines a procedure for embedding stakeholder engagement and consensus building in the development of dynamic simulation models that can guide the development of effective, coordinated and acceptable policy responses to complex public health problems, such as alcohol-related harms in NSW.