Evaluating policies that simultanously target several chronic diseases: using a new Australian economic model system

Presenter: Agnes Walker, Australian National University

Abstract

Background: Chronic diseases - eg heart disease, cancer, diabetes - affect around 80% of older Australians, are the main causes of disability and premature death, and account for 70% of Australia’s health expenditures. The major chronic diseases tend to have common risk factors and individuals often acquire several chronic diseases as they age. Despite this the available data often limit studies in the literature to a single chronic disease and to the use of group rather than person-level data.
Objectives: (a) Report on a new individual level model-system able to account for multiple chronic diseases (comorbidities) that can be acquired by Australians as they age.

(b) Use the model-system to simulate policy interventions that simultaneously target several chronic diseases.

(c) Compare model-system simulations which deal with these diseases one-by-one, with a simulation that accounts for all these diseases combined.

Methodology: The study investigated international and Australian data sources to see if they could be combined so as to allow consideration of multiple chronic diseases at the level of the individual-level. A model-system was developed which links disease-specific progression models to an ‘Umbrella’ microsimulation model representing the Australian population. The current version considers type 2 diabetes, cardiovascular disease (CVD) as a complication of diabetes and CVD in persons without diabetes. It projects 20 years ahead and accounts for individuals’ demographic, socioeconomic and health-risk-factor characteristics; progression of their health status over time; their number of chronic diseases; their quality of life; and health-related expenditures. It is also able to estimate the costs versus the benefits of policy interventions.

Results: (1) We found that self-rated quality of life declined dramatically as the number of chronic diseases a person had increased. We also found that the cost of treating individuals with multiple chronic illnesses was significantly greater than the sum of treatment costs for single-disease individuals across the same chronic diseases.

(2) In building the prototype model-system we found that disease patterns emerging from international data differed considerably from the patterns observed in recently released Australian data. Also, we had difficulty matching detailed data from smaller Australian disease-specific surveys with aggregate Australian benchmarks available in broader and nationally representative health statistics collections.

(3) Separate simulations of a life-style-change policy intervention for diabetes and for CVD resulted in lower summed health expenditures and lesser quality of life deteriorations than did a simulation of the same life-style intervention which accounted for these diseases simultaneously.

Conclusions: Accounting for multiple chronic diseases at the level of the individual led to more accurate estimates of the costs and benefits of policy interventions and allowed complex intervention combinations to be studied across several diseases. Also, development of our model-system highlighted gaps in Australia’s traditional databases and led to the broadening of data collections with a view to filling in such gaps.