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. 2013 Jun;16(2):87-100.
doi: 10.1007/s10729-012-9215-x. Epub 2012 Oct 23.

Tradeoffs in cardiovascular disease prevention, treatment, and research

George Miller  1 , Matthew Daly, Charles Roehrig
Affiliations

Tradeoffs in cardiovascular disease prevention, treatment, and research

George Miller et al. Health Care Manag Sci. 2013 Jun.

Abstract

It is widely believed that the US health care system needs to transition from a culture of reactive treatment of disease to one of proactive prevention. As a tool for understanding the appropriate allocation of spending to prevention versus treatment (including research into improved prevention and treatment), a simple Markov model is used to represent the flow of individuals among states of health, where the transition rates are governed by the magnitude of appropriately-lagged expenditures in each of these categories. The model estimates the discounted cost and discounted effectiveness (measured in quality adjusted life years or QALYs) associated with a given spending mix, and it allows computing the marginal cost-effectiveness associated with additional spending in a category. We apply the model to explore interactions of alternative investments in cardiovascular disease (CVD) and to identify an optimal spending mix. Under the assumptions of our model structure, we find that the marginal cost-effectiveness of prevention of CVD varies with changes in spending on treatment (and vice versa), and that the optimal mix of CVD spending (i.e., the spending mix that maximizes the overall QALYs achieved) would, indeed, shift spending from treatment to prevention.

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Conflict of interest statement

Conflict of interest The authors declare that they have no conflict of interest.

Figures

Fig. 1
Fig. 1
Model structure
Fig. 2
Fig. 2
Illustrative impact of equations – death rate as a function of treatment spending
Fig. 3
Fig. 3
Population growth in base case
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Fig. 4
Marginal cost-effectiveness of additional treatment (prevention) spending as a function of prevention (treatment) spending level
Fig. 5
Fig. 5
Impact of discount rate on marginal cost-effectiveness
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Fig. 6
Total QALYs and marginal cost-effectiveness as a function of spending mix
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Fig. 7
Impact of prevention lag on optimal sending mix
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Fig. 8
Impact of discount rate on optimal spending mix for alternative prevention lags
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Fig. 9
Impact of time horizon on optimal spending mix
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Fig. 10
Impact of research spending on population (not optimized)
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References

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