Accelerating risk factor reductions in a systems model of chronic disease burden

Dr Danielle Currie, delivered this presentation at the Public Health Association of Australia’s (PHAA’s) annual prevention conference in Brisbane, 11-13 May 2022.

Chronic disease is associated with multiple modifiable risk factors with complex causal interactions and relationships between them. Although these relationships have been quantified in burden of disease studies, we currently lack tools to assess the impact of potential public health strategies in combination. A nuanced model that reconstructs, quantifies and simulates these relationships between risk factors and chronic disease burden will help decision-makers evaluate the benefits and cost impacts of changing trajectories of key risk factor prevalence.

Process

Building on existing Australian and global burden of disease studies, we developed a dynamic simulation model of the aggregate burden of chronic disease in Australia that is attributable to a suite of interrelated 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.

Analysis

Risk factor trajectories from 2011 -2015 were calculated and projected forward. To test scenarios of interest, accelerations and decelerations of these trajectories were analysed. Model outputs suggest that testing risk factor modifications can best illustrate the dynamic nature of intervention impact when:

1. Mediating risk fractions are represented, and
2. Historical and projected trends of other underlying risk dynamics are accounted for.

Outcomes

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.