Compelling case project – Phase 2

What is the issue?

Despite many major chronic diseases being largely preventable through changes to health behaviours (including poor diet, physical inactivity, tobacco use, harmful alcohol consumption and obesity), only 1.3% of all health spending is currently directed towards prevention programs.

In an effort to establish a compelling argument for investment in prevention and to determine how best to target strategies for maximum impact, the Prevention Centre previously supported the Compelling Case Project Phase 1.

How did the project address the issue?

The first phase of the Compelling Case project aimed to provide a proof-of-concept for a single systems model to link risk factors to disability-adjusted life years and health expenditure. It tested four agreed interventions.

The second phase of the project expanded the model to drive insights for policy, planning and advocacy. The aim was to inform policy makers how to invest in a targeted and coordinated way across a portfolio of risk factors to achieve the greatest impact. It also aimed to make explicit the links between population risk factors and their diseases or causes of ill-health. The questions explored included:

1. Can we capture the complexities of modifiable risks and related disease burden and costs using system dynamics?
2. If so, what could this simulation model tell us?

We gathered datasets and evidence on Australia’s health burden and economic costs of preventable disease. We collaborated with experts and policy partners to develop a conceptual map that was then converted to a computer model of Australia’s burden of disease – the GoHealth model.

We then ran and validated the model to produce insights that could help inform decision making and explore the underlying behaviour of these interacting risks and their linked diseases.

Relevance for policy and practice

The Compelling Case Project demonstrates that a systems approach is crucial to understanding the complex drivers of chronic disease and how best to address them. It illustrates how strategies to address one risk factor influence others, and how risk factors overlap in driving the incidence and prevalence of chronic disease.

We have built the architecture for a model that has great potential for providing a decision support tool to assist policy makers in more precisely targeting interventions against chronic disease. We have shown that systems modelling is an important tool for capturing the complexity of preventable chronic disease and quantifying the impact of interconnections between different risk factors.

The GoHealth model supports governments to think differently about prevention. It illustrates that policy makers should:

• Consider the interactions of strategies, rather than the volume, and the sum benefit of strategic investment in prevention
• Consider the interactions between risk factors and their joint contribution to certain diseases
• Identify opportunities for intervention that address multiple interacting risk factors at once
• Consider the economic impacts of chronic disease beyond the healthcare costs, such as absenteeism and lost productivity, when making decisions on resourcing the prevention of risk factors for chronic disease.

What are the next steps?

Australia now has an advanced chronic disease decision support infrastructure ready for deployment once it can be coupled with a more robust data ecosystem.

There is an urgent need for improved population-level time series data on the risk factors for chronic disease, with consistent definitions and differential demographic trends.

To achieve consistent data for all relevant exposures requires national collaboration and effort. If we are to model chronic disease and risk factors in the same way as we modelled COVID-19, we need to strengthen the modelling workforce, physical infrastructure, and legislative environment to support data collection and sharing.

We support several initiatives outlined in the National Preventive Health Strategy, such as the establishment of a national prevention monitoring and reporting framework, that will allow us to achieve better data collection and understanding of risk factors in Australia.

Mapping, quantifying, testing, and validating a system dynamics model of the complex causal interrelationships between the common risk factors for chronic disease was a hugely ambitious undertaking.

It pushed the boundaries of decision analysis using complex systems modelling and simulation. This project has thus contributed to building Australia’s world-leading capacity in dynamic simulation modelling research for population health and decision making.