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Synthesis Capacity

The Prevention Centre’s Synthesis Capacity aims to synthesise and make readily available what is known about the prevention of lifestyle-related conditions to support evidence-informed policy and practice.

The Capacity is developing and testing tools and methods that will help decision makers systematically combine local experience and contextual information with expert knowledge and published evidence when addressing complex problems.

The main focus is on dynamic simulation modelling, which brings together diverse evidence sources and uses a participatory approach to develop advanced computer models that can forecast the impact of various policy interventions.

The Synthesis Capacity’s work aims to support the Prevention Centre to build consensus on strategies for preventing lifestyle-related chronic disease in Australia.

Capacity Head

Capacity Lead, Evidence Synthesis and Simulation for Policy

Capacity team

  • Jacqueline Davison, Prevention Centre
  • Louise Freebairn, Prevention Centre, ACT Health
  • Dr Geoff McDonnell, Prevention Centre, Adaptive Care Systems
  • Dylan Knowles, Prevention Centre, Minus Fifty Software
  • Dr Ante Prodan, Prevention Centre, Western Sydney University
  • Allen McLean, Prevention Centre, University of Saskatchewan
  • Mark Heffernan, Prevention Centre, Dynamic Operations
  • Professor Nate Osgood, Prevention Centre, University of Saskatchewan

Dynamic simulation modelling

The Prevention Centre is pioneering the use of dynamic simulation modelling to provide policy makers with a unique ‘what if’ tool to test the likely impact of a range of possible solutions before implementing them in the real world.

Read a factsheet about dynamic simulation modelling.


The Synthesis Capacity of the Prevention Centre, in collaboration with our policy partners, is undertaking a program of work to trial a range of applications of dynamic simulation modelling methods and approaches, and evaluating their feasibility and utility in supporting policy and practice for prevention of chronic disease.

Read a Findings Brief on the NSW Premier’s Priority of reducing childhood overweight and obesity project by clicking on the image.




The solutions to complex public health problems such as harmful alcohol consumption, overweight and obesity and suicide prevention remain elusive, despite hundreds of millions of dollars and decades of research that have led to attempts to combat them through ‘evidence-informed’ policy.

This is partly because there are many ways to tackle public health problems, multiple risk factors, and different intervention points during the lifespan. Complex chronic health problems have many inter-related causes and it can be unclear how these factors interact. In addition, there is often a broad range of possible interventions to prevent chronic disease, applied individually or in combination, which must be considered in light of their comparative costs, benefits and health system implications over the short and longer term. Health decision makers often face the challenges of differing stakeholder and community views, limitations in research evidence, political considerations and industry lobbying.

Traditional analytic tools for supporting evidence-informed policy are limited in their ability to help policy makers to plan and make decisions. Dynamic simulation modelling can answer important questions, such as which risk factors are the most important, when in people’s lives we should target interventions, and which combinations of interventions work best, are most equitable and most cost effective.


What is dynamic simulation modelling?

Dynamic simulation models are virtual worlds in which individuals and communities act and react in the same way as people do in real life.

Based on a variety of evidence sources such as research, expert knowledge, practice experience and data, the models represent human behaviour in all its complexity. They can follow individuals throughout life, identifying how their influences and behaviours change, and how their health is impacted over time.

They can also map the aggregate effects of thousands of people operating in a complex system, following their interactions and responses to different policies and interventions.

A dynamic simulation model is a ‘what if’ tool through which we can test various policy scenarios over time to see which are likely to have the most effect, both on individuals as well as on the system as a whole.


What questions can it answer?

Our dynamic simulation models can test interventions before they are implemented in the real world. They provide accurate, reliable insights into:

  • Which risk factors are the most important in the prevention of chronic disease
  • How different risk factors interact in complex ways to cause a number of outcomes
  • What different outcomes can be achieved by intervening on (or targeting) one risk factor, or a combination of risk factors
  • Which health interventions are most likely to work
  • Which interventions are the most equitable
  • Which interventions are the most cost effective
  • How different interventions will impact the health system as a whole
  • How and when in people’s lives different interventions should be targeted.


Who is it for?

  • Policy makers
  • Treasury officials
  • Program planners
  • Public health practitioners
  • Health economists
  • Academics and researchers


How does it work?

  1. We work collaboratively with academics, policy makers and practitioners to map the key risk factors and likely causal pathways leading to the health outcomes we are interested in. This information is combined with empirical evidence and administrative, survey and burden of disease data to create a computer model that represents the real world.
  2. We test the model to ensure it accurately represents the target community we are aiming to study. We can model anything from small sections of the community in different geographic locations to the population of the entire country.
  3. We produce a baseline to see what would happen with no new interventions (business as usual).
  4. Using a simple interface, we key in different interventions, individually or in combination, and target them in different ways to see how this impacts the outcomes.


Why use dynamic simulation modelling?

  • It offers a way of helping to better focus resource allocation for optimal impact.
  • It helps policy makers to test big decisions in the virtual world before implementing them in real life.
  • It enables us to follow the dynamics of populations, their behaviours and outcomes over time (rather than providing a static snapshot as with other models).
  • It is low risk and cheap: We can test what works without the expense and ramifications of trialling different interventions in the real world.


What makes our model different?

Dynamic simulation modelling has been used successfully in engineering, ecology, defence and business for many years. Its benefits for health policy are only now starting to emerge.

The Prevention Centre has developed a form of dynamic simulation modelling that is unique because:

  • Our product is more technically advanced: We integrate multiple modelling methods in many of our projects
  • Our product is more transparent: We work collaboratively to obtain the inputs that make the model
  • Our project is more accessible: We have a user-friendly interface that anyone can use
  • Our product is different in terms of stakeholder engagement: We embed stakeholder engagement, consultation and consensus from the very beginning of the project
  • Our product allows us to synthesise more diverse evidence sources: Compared to systematic reviews or meta-analyses
  • Our processes are robust and reliable: We test our models retrospectively against reported outcome indicators


Synthesis Capacity team members are engaged on a large number of Prevention Centre projects and related activities.

Developing and testing new methods of consensus development

The Capacity is embedding stakeholder engagement and consensus building methods in the development and use of dynamic simulation models to support policy decisions and align stakeholder actions to address complex problems. For example, it will adapt existing deliberative methods by using dynamic simulation models and their outputs to facilitate policy dialogues through support of informed debate around the quantitative trade-offs between alternative solutions, and help build consensus on a course of action.

Developing concise policy briefs

The Capacity is collaborating with the Sax Institute Knowledge Exchange Division and CIPHER (the Centre for Informing Policy in Health with Evidence from Research) to develop policy brief options and recommendations for prevention policy settings. The project will compare and evaluate policy brief formats, and develop and implement professional development and training on knowledge exchange for early career researchers.

A rapid scan of projects and programs related to chronic disease prevention

The scan aimed to identify current or recently completed activities that may be relevant and/or complementary to the Prevention Centre’s work. The scan focused on the following categories of information in the areas of diet, physical activity, alcohol and tobacco:

  • Current research or evaluation projects
  • Evidence reviews/syntheses
  • Policies and program initiatives
  • Examples of the application of systems-based approaches.

This information was compiled through interviews with the Centre’s lead investigators and funding partners.

The information gathered through the Activity Scan is not intended to be exhaustive and the Prevention Centre has not appraised or endorsed the initiatives. However, the scan offers a valuable overview of some relevant and interesting projects in chronic disease prevention, and identifies opportunities for the Prevention Centre to build on existing work and explore new collaborations.

The summary of the results of the activity scan is here.

Evidence reviews

The Capacity coordinates evidence reviews, which to date have included a review of the benefits of healthy eating and active living programs, rapid reviews of evidence for the prevention of type 2 diabetes and Alzheimer’s disease, and a systematic review of applications of systems mapping and dynamic modelling to inform health policy. These reviews will be posted here as they are finalised.


Published research