Dynamic modelling captures complexity
By Associate Professor Jo-An Atkinson
Atkinson J, Page A, Wells R, Milat A, Wilson A. A modelling tool for policy analysis to support the design of efficient and effective policy responses for complex public health problems. Implement Sci 2015;10:26 doi: 10.1186/s13012-015-0221-5.
Why I wrote about this topic
Through my evidence synthesis work I began to recognise the challenges our policy partners face in using evidence for planning strategy or making policy decisions about preventing lifestyle-related chronic diseases. I was also aware of other complex public health problems such as suicide prevention where effective responses remain elusive, despite 20 years of research and ‘evidence-informed’ policy. I wanted to draw on, and benefit from those lessons for the prevention of chronic disease. I used this paper to highlight the challenges and the limitations of traditional analytic tools for supporting evidence-informed policy and proposed how quantitative systems science approach might help address those limitations.
What’s my key point?
Dynamic simulation modelling brings together a variety of evidence, such as research, expert knowledge, practice experience and data, to capture the complexity of a problem. That model is then used as a ‘what-if tool’ – to simulate various policy scenarios to see which is likely to have the most effect. It offers promise in being able to better use diverse evidence sources to support decision making for complex problems, and provides a platform for consensus building and strengthening relationships between policy makers, stakeholders, and researchers.
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.