Can We Determine the Optimal Size of Government (Development Policy Analysis no. 7)

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A large range of prevention programs and activities are being implemented at the national, state and local levels.

These initiatives comprehensively address identified risk factors for suicide and include direct prevention initiatives e. However, it is argued that these falls can be explained by a reduction in the availability of lethal methods of suicide, namely, measures to control the availability of firearms following the Port Arthur massacre, the requirement for new cars to be fitted with catalytic converters, and the decline in the prescription of tricyclic antidepressants due to availability of a new class of antidepressant compounds with fewer side effects and lower toxicity in overdose [ 41 , 42 ].

Therefore, the impact on male suicide was most likely a consequence of independent policy actions, unrelated to the national strategy for suicide prevention. For more information on data visit ABS website at www. Over the same year period, there was no decline in suicidal ideation or rates of attempted suicide [ 41 ].

This suggests that the root causes of suicide were not adequately addressed by the National Strategy, and while regulation of access to lethal means of suicide can reduce the death rate to a point, as an ongoing strategy, it may have limited impact due to the difficulty in regulating to prevent access to other suicide methods hanging, sharp objects, jumping from a height. This raises some important questions. Why is evidence of impact of the National Strategy for Suicide Prevention on population-level suicide rates limited?

Do we really understand the complex and dynamic interrelation of causal factors of suicide over the life course? Unfortunately, current tools for synthesising and operationalising research evidence are not able to answer vital question of what the ideal targeting, intensity, consistency, and coordination of programs to prevent suicide is. For complex public health problems such as suicide, there are two important limitations of traditional analytic tools to support the design of effective evidence-informed policy responses:. I Analytic limitations for exploring the impact of policy options.

Evidence of measurable impacts of suicide prevention policy responses on population-level suicide rates is limited [ 28 , 29 ]. Uncertainties remain around the type, scope, and intensity of interventions to implement, and the right place and right period to implement them. Designing an effective and efficient policy response for suicide prevention requires a comprehensive perspective on causation, consideration of the influence of factors such as access to healthcare and preventive services, and analytic methods for testing the range of policy options and their consequences to better target actions for the Australian context.

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Numerous conceptual models of suicide have been developed for specific populations and stages of the life course [ 17 , 43 - 52 ], with varying emphasis on proximal causal factors, ecological influences, and multilevel determinants. II Constraints of traditional approaches to data analysis. These characteristics violate the conditions for use of traditional analytic methods.

While traditional methods provide valuable data-driven explanations of simple causal relationships between a finite range of variables for well-defined problems, and rigorously take account of variables that can confound these relationships [ 53 ], public health problems that arise from complex human behaviours makes reliance on traditional methods problematic and undermines confidence in the ability of research evidence to inform effective policy. Systems science is an interdisciplinary field that investigates the nature of complex systems and is underpinned by well-established mathematical theory of nonlinear dynamics [ 54 - 56 ].

It is not a new science, and its methods have been successfully applied to sectors such as engineering, defence, economics, ecology, and business since the mids.


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Systems science is increasingly being recognised in the health sector for its utility in mapping and understanding complex health problems, operationalising research evidence, and systematically analysing a range of intervention and policy solutions [ 57 ]. System dynamics and agent-based modelling simulation modelling are systems science methods that can be used to develop a tool for policy analysis. Such a tool would allow virtual desktop experimentation of policy scenarios to test their comparative impact and cost over the short, medium, and longer term. The policy analysis tool could test the efficiency, effectiveness, and equity of policy responses, exposing unintended consequences and perverse incentives in the system through computer simulation, averting the need for costly trial and error approaches.

Systems science adopts a perspective that encompasses the inherent complexity of a public health problem and avoids inferences being drawn from narrowly focussed investigations. System dynamics and agent-based modelling take into account the interrelations, reciprocity, discontinuity, and dynamic nature of influences on health and health behaviours within a broader context [ 53 ]. The structure of relationships between numerous interacting factors is mapped and modelled encompassing feedback and delays [ 58 ]. This enables analysis and identification of causal loops that are most influential in determining the evolutionary behaviour of the system that produces the public health problem in question [ 58 ].

These systems science methods are therefore better able to embrace and make sense of the complexity that characterises public health problems such as suicide. In particular, multiscale modelling has been successfully applied in biology, environmental sciences, and physical sciences, but only recently adapted to address public health problems [ 59 ]. It provides a method for mapping and understanding how proximal causal factors interact with each other and with ecological factors to determine health behaviour [ 53 ]. It combines agent-based modelling capable of capturing heterogeneous attributes, behaviours, and interactions of individuals and system dynamics modelling which captures population-level, ecological influences, and whole system dynamics.

Multiscale modelling provides policy makers with a powerful analysis tool that is also capable of exploring equity effects of policy scenarios. That is, simulation modelling can be used as a policy analysis tool to inform more efficient targeting of resources at specific risk factors, using particular interventions and approaches projected to have greatest impact, while exploring where disinvestment can occur without adversely affecting population health or equity.

The potential utility of these systems science methods lie in their ability to systematically and quantitatively analyse a range of intervention and policy options and identify leverage points in the system places to intervene where small inputs might result in large impacts [ 60 ]. Simulation modelling commences with the collation of existing conceptual models of suicide, reviews of research evidence, and expert knowledge. Dialogues informed by the collated information proceed to conceptual mapping, quantification, and simulation.

Models are able to incorporate the impact of contextual influences on policy making e. If desired, the process can permit the broader involvement of key stakeholders in model development which may act to foster trust and transparency in the policy-making process and accelerate policy adoption, implementation, and health sector change. This important validation step helps build confidence in the structure and predictions of a model. The final product is a policy analysis tool that can systematically explore the impact on suicide of.

Changes to arrangements of the system relating to workforce, infrastructure, governance and financing;. Systems science methods are not without criticism. In reality, empirical analyses of causation of system components, and simulation modelling that brings these components together positing their interaction, are complementary.

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Integrating the use of sound epidemiological approaches to specify the most likely causal pathways between putative exposures and outcomes, with simulation modelling to explore whole system dynamics, may contribute to the credibility of systems science methods. Of particular relevance in this regard is the use of directed acyclic graphs [ 61 ], which explicitly define the hypothesised causal relationships between exposures, confounders, intermediaries, and effect measure modifiers. These then guide the analytic strategy to obtain less biased estimates of an association between an exposure and outcome.

These approaches have most commonly been used in the context of defining aetiological assumptions and analytic adjustments [ 61 ]. However, directed acyclic graphs and causal relationships which incorporate levels of scale inherent in eco-social approaches to understanding health could be adapted to inform the content of multiscale models and lead to more empirically defensible simulations of hypothetical impacts of interventions or public health policies.

The extent to which evidence is incorporated into public health policy is challenged by factors including the political environment [ 62 , 63 ]; vast, inconsistent, or inaccessible scientific information [ 62 , 64 , 65 ]; deficits in relevant and timely research [ 63 , 65 ]; a tradition of relying on intuition or advice from opinion leaders [ 62 , 66 , 67 ]; and inadequate information systems, resources, leadership, and required competencies to capture and synthesise disparate evidence sources [ 1 , 62 , 68 - 70 ]. The use of simulation modelling offers potential in addressing many of these challenges, particularly when policy makers and other key stakeholders are engaged in the model development process.

For example, applications of this method have led to increased awareness by policy makers of the dynamics of the health problem to be addressed and demonstrated to them how research findings and data can be practically used to generate projections that can guide policy decisions Atkinson J, Wells R, Page A, Dominello A, Haines M, Wilson A: A review of applications of system dynamics modelling to support health policy making, submitted.

In addition, it provided a framework to facilitate more rapid integration and use of new evidence that arises for policy analysis, and it led to insights on the value of collaboration with non-health sectors. Simulation modelling provides a platform for this process and for strengthening relationships among policy makers, stakeholders, and researchers.

In addition, a systematic narrative review of the influences on evidence uptake by policy makers found a recurring theme that the decision process is influenced by opinion leaders, who either make judgements based on expert opinion, or act as a filter through which research evidence is transferred, undermining its neutrality [ 66 ].

Simulation modelling can diffuse this process by providing a platform for systematic integration of diverse evidence sources and encourages participation of stakeholders in development of the model that is then used by policy makers as an impartial tool for analysis of policy options. This contributes to transparency in the decision-making process, fosters trust and greater buy-in by stakeholders, and can accelerate policy adoption, implementation, and change.


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  • However, further work is needed to adapt, apply, and evaluate the method as a knowledge translation tool to support evidence-informed public health policy and practice. The recent systematic review of the application of system dynamics modelling for health policy making yielded only seven case studies, two of which described their use to address public health problems Atkinson J, Wells R, Page A, Dominello A, Haines M, Wilson A: A review of applications of system dynamics modelling to support health policy making, submitted.

    The lack of published literature detailing practical applications of this methodology to support health policy making precludes a detailed understanding of the issues and limitations of the approach both as a knowledge translation tool and in the design of effective health policy. However, a systematic review and synthesis of applications of system dynamics modelling in non-health sectors reported that among its many benefits, several potential pitfalls exist [ 73 ].

    Such pitfalls can prevent the process of gaining insights into a problem, understanding the mental models of others, and ultimately achieving consensus on the structure and behaviour of the model. Without this consensus, the confidence of some key stakeholders in model projections and subsequent policy decisions will be lacking.

    Simulation modelling used as a policy analysis tool and based on a detailed understanding of aetiology and evidence-based estimates of the impact of preventive interventions on absolute and relative risk provides a rigorous mechanism for virtual testing of policy alternatives in order to determine which policy responses would achieve the greatest impact. It is an approach that offers promise in being able to better operationalize research evidence to support decision making for complex problems, improves targeting of public health action, and provides a platform for strengthening relationships between policy makers, stakeholders, and researchers.

    Next steps for realising the benefits of this policy analysis tool to support evidence-informed policy responses in public health include leveraging existing structures and expertise; developing the necessary capacity and processes to support implementation of this approach in Australia; and evaluating their use. The recently established Australian Prevention Partnership Centre [ 74 ] is aiming to achieve this in the prevention of lifestyle-related chronic conditions through the use of systems tools and approaches.

    Among other outcomes, the Partnership Centre is working towards developing stronger national networks of researchers, policy, and program practitioners. This provides a valuable opportunity for co-production of simulation models to better inform policy responses for complex public health problems. Evidence-based public health: a fundamental concept for public health practice. Annu Rev Public Health. Hunter D. Public health policy. In: Cambridge: Polity Press; What works to increase the use of research in population health policy and programmes: a review.

    Evid Policy. McMichael AJ. Prisoners of the proximate: loosening the constraints on epidemiology in an age of change. Am J Epidemiol. Guidance for evidence-informed policies about health systems: rationale for and challenges of guidance development. PLoS Med. Australian Bureau of Statistics: Suicide in Australia.

    The decline in Australian young male suicide. Soc Sci Med. Mendoza J, Rosenberg S. Suicide and suicide prevention in Australia: breaking the silence. Australian Bureau of Statistics. National survey of mental health and wellbeing: Summary of Results The burden attributable to mental and substance use disorders as risk factors for suicide: findings from the Global Burden of Disease Study PLoS One.

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    Attributable risk of psychiatric and socio-economic factors for suicide from individual-level, population-based studies: a systematic review. Australian Government Department of Health. Chronic disease.

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    Mental disorders and socioeconomic status: impact on population risk of attempted suicide in australia. Suicide Life Threat Behav.