Beyond mapping: how these models are bringing systems to life



TYPE Prevention Centre News

Lead author Dr Louise Freebairn, a policy maker with ACT Health who completed her PhD with the Prevention Centre, said the process helped experts from different disciplines come out of their respective silos to develop a shared understanding of the problem and potential solutions, which were then tested by the model.

Through the exchange of information, knowledge was shared and new knowledge created, leading to changes in understanding. 

She said that, despite hundreds of millions of dollars and decades of research, ‘wicked’ public health problems like obesity and diabetes in pregnancy were still proving difficult to fix.

“That’s partly because there are so many interacting risk factors and influences on people’s behaviour as well as multiple ways to tackle these problems. It’s hard for policy makers to know where to intervene amid competing cost demands, implications for the health system and community and industry views,” Ms Freebairn said.

By combining expertise across disciplines, we have shown we can represent the complex systems behind chronic disease in a way that goes beyond qualitative system mapping to the development of rigorously quantified and policy relevant, multi-scale computer models that can be used to guide decision making.

Dr Louise Freebairn

‘Dynamic simulation models’ are sophisticated ‘what if tools’ that allow users to pull different policy levers and see the effects of different policy combinations into the future. Based on real data and context-specific characteristics of the system, the models can be used to explore different solutions to see which will be most effective and cost effective.

The article describes the process of collaboratively developing a model with recognised experts in providing care, planning services, undertaking research and developing policy for the diagnosis and management of diabetes. Their insights were combined with research and data to develop a dynamic simulation model of diabetes in pregnancy in the Australian Capital Territory.

The team used an iterative process of model development where they presented the model back to participants at meetings and workshops to continually incorporate their feedback and refine the structure.

The participants have since shared the model with other decision makers and have identified opportunities for it to be used as a decision support tool. The model has been used to prioritise future research and data collection efforts (not all evidence gaps are equal) and to facilitate new relationships and opportunities for engagement.

These findings are relevant to anyone working on participatory modelling projects and methods, and provide a way forward for all those struggling with complexity.

Read more about dynamic simulation modelling