Over the past 50 years, longevity has risen at a steady pace. Functional status of the elderly has also witnessed important improvements, although gains have leveled off recently. These improvements do not come without costs. The United States has devoted an increasing share of its income to health care over that same period. In part because of the projected increase of health costs, the long-term fiscal outlook of the U.S. presents important challenges. Fiscal forecasts from the Congressional Budget Office and the Centers for Medicare &Medicaid Services (CMS) are dire, with Medicare spending alone projected to more than double as a share of national income, from 3.7% today to 7.3% in 2050. However, these forecasts take the driver of health care costs-technology-as exogenous, or fixed. This is unrealistic. We know innovators maximize the expected value of a future profit stream, and they will take into account the demand for their innovations in the future. The proposed research will marry two distinct but complementary approaches from the health policy and macroeconomic fields. First, we will incorporate technological change, and its determinants, into existing models projecting the long- term fiscal outlook of the U.S. Our engine will be an economic-demographic microsimulation that covers the population aged 25 and older, the Future Americans Model (FAM). This approach will examine granular technological change to understand consequences for spending and population health. Second, we will build a macroeconomic growth model to understand endogenous innovation and health care spending in overlapping generations (OLG) that incorporates the projections of our economic-demographic simulation. Such OLG models have proven very useful for understanding long-run economic outcomes and their effects on government debt. Results from the OLG model will complement the results of the FAM by revealing the fiscal paths consistent with a balanced government budget when taking behavioral and macroeconomic changes into account. There are four principal outcomes of this project. First, we will quantify the fiscal consequences of various innovation scenarios. Second, the research will identify scenarios under which new innovations will impact health by effectively treating one disease while simultaneously impacting innovation for other diseases. Third, the project will identify scenarios under which health spending would rise or fall in future years as a result of technological change and how health spending can be financed with minimum welfare consequences using the FAM-calibrated OLG model. Fourth, by combining behavioral models with simulation models such as the Future Americans Model, this project will advance the state of knowledge on the possibilities of combining different approaches to make long-term fiscal forecasts.

Public Health Relevance

A primary driver of health care costs - medical technology - is taken as fixed in fiscal forecasts but technology is dynamic and responsive to future demand. These dynamics are the core of this project that marries approaches from health policy and macroeconomic fields. Using micro simulation and overlapping generations models we quantify fiscal and welfare consequences of myriad innovation scenarios and advance scientific knowledge on the possibilities of combining these two approaches.

National Institute of Health (NIH)
National Institute on Aging (NIA)
High Priority, Short Term Project Award (R56)
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Special Emphasis Panel (ZRG1)
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Phillips, John
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University of Southern California
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Los Angeles
United States
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Gaudette, √Čtienne; Goldman, Dana P; Messali, Andrew et al. (2015) Do Statins Reduce the Health and Health Care Costs of Obesity? Pharmacoeconomics 33:723-34
Gaudette, √Čtienne; Tysinger, Bryan; Cassil, Alwyn et al. (2015) Health and Health Care of Medicare Beneficiaries in 2030. Forum Health Econ Policy 18:75-96
Zissimopoulos, Julie; Crimmins, Eileen; St Clair, Patricia (2014) The Value of Delaying Alzheimer's Disease Onset. Forum Health Econ Policy 18:25-39