Health

Research articles · Dec 31, 2011

Cost-benefit model system of chronic diseases in Australia to assess and rank prevention and treatment options

Agnes E Walker, Stephen Colagiuri

Australian National University, Australia; University of Sydney, Australia

Volume 4(3) Winter 2011

Cite as: A. Walker, S. Colagiuri; 2011; Cost-benefit model system of chronic diseases in Australia to assess and rank prevention and treatment options; International Journal of Microsimulation; 4(3); 57–70. doi: 10.34196/ijm.00061

Abstract

Chronic diseases – eg heart disease, cancer, diabetes, mental disorders – affect around 80% of older Australians, are the main causes of disability and premature death, and account for 70% of total health expenditures. Because lifestyle patterns are major risk factors, chronic disease prevention and treatment are not only of medical concern, but also of considerable social, family-level and personal interest. While this makes microsimulation approaches particularly suitable for assessing intervention costs and benefits, such approaches will need to be combined with disease-progression models if health status and treatment choices are also to be simulated. Aims: Describe methodological and technical proposals for the development of a cost-benefit model-system. Methods: Several chronic disease progression models are to be linked to an ‘Umbrella’ microsimulation model representing the Australian population. To project 20 years ahead, use of reweighting techniques are proposed for population projections, disease-specific predictions and for health-related projections. The model-system is to account simultaneously for Australians’ demographic, socioeconomic and health-risk-factor characteristics; progression of their health status; the number of chronic diseases (comorbidities) they accumulate over time; health-related expenditures; and changes in quality of life. Standard methods are proposed to estimate costs versus benefits of simulated policy interventions and related quality of life improvements. Key outcome: Proposal of novel methods for modelling comorbidities – a task rarely attempted, although quality of life is known to decline and health expenditures to increase well above what a linear addition of the effects of individual chronic diseases would predict.

This article contains 2 figures and 1 table.

Figures

Figure 1. Chronic disease model-system.
Figure 2. Umbrella model’s projection module.

Tables

Table 1. Coefficients of the Anderson CHD and stroke equation.
Explanatory variables CHD CHD deaths Stroke CVD deaths
θ0 0.9145 2.9851 −0.4312 0.8207
θ1 −0.2784 −0.9142 − −0.4346
β0 15.5305 11.2889 26.5116 −5.0385
female 28.4441 0.2332 0.2019 0.2243
log(age) −1.4792 −0.9440 −2.3741 8.2370
(log(age))2 − − − −1.2109
log(age)* female −14.4588 − − −
(log(age))2 * female 1.8515 − − −
log (SBP) −0.9119 −0.5880 −2.4643 −0.8383
cigarettes (Y/N) −0.2767 −0.1367 −0.3914 −0.1618
log (total-C/HDL_C) −0.7181 −0.3448 −0.0229 −0.3493
diabetes −0.1759 −0.0474 −0.3087 −0.0833
diabetes * female −0.1999 −0.2233 −0.2627 −0.2067
ECG-LVH −0.5865 −0.1237 −0.2355 −0.2946

Source: Anderson et al. (1991).