Making a compelling case for prevention

The Prevention Centre is a pioneer of dynamic simulation modelling to provide synthesised advice for policy on chronic disease prevention. We developed a national dynamic simulation model of chronic disease prevention, the GoHealth model, based on trends in the prevalence of nine modifiable risk factors. The model was used to simulate the future burden of chronic disease and related healthcare costs under various scenarios that reflected the dynamic nature of risk factor trajectories, stratified by age.

It was developed as part of our Compelling case project aimed at establishing a compelling argument for investment in prevention and to determine how best to target preventive strategies for maximum impact.

To the best of our knowledge, the GoHealth model is the first dynamic simulation model in the world that captures the dynamic relationships between risk factors for chronic disease in a comprehensive way and links these to burden of disease and associated economic costs.

Prevention Centre Findings Brief

Lack of consistent and reliable Australian data on risk factor prevalence meant we could not develop the model into a robust decision support tool for policy. However, the model demonstrates underlying behaviour in the system, enabling us to offer policy-relevant recommendations and make a compelling case for action on chronic disease prevention.

This project aimed to establish a compelling argument for investment in prevention and determine how best to target preventive strategies for maximum impact.

Dr Danielle Currie presented a summary of the GoHealth model at the PHAA Preventive Health Conference and recently joined other members of the team to present insights at a one-hour webinar. You can watch a recording of the webinar with Danielle joined by Compelling Case Project investigators , Associate Professor Jo-An Occhipinti and Associate Professor Stephen Begg alongside Prevention Centre Co-Directors Professor Andrew Wilson and Professor Lucie Rychetnik.

Despite the many challenges associated with building Australia’s first simulation model of chronic disease burden incorporating multiple risk factors, this model offers us a useful policy tool to explore and understand expected impacts of potential prevention investments before they are implemented. It offers a mechanism to potentially evaluate the impact of planned prevention strategies that encompasses multiple risk factors, differential age-group targeting and accounts for co-morbidity effects.