OBJECTIVE: To quantify uncertainty in forecasts of health expenditures. STUDY DESIGN: Stochastic time series models are estimated for historical variations in fertility, mortality, and health spending per capita in the United States, and used to generate stochastic simulations of the growth of Medicare expenditures. Individual health spending is modeled to depend on the number of years until death. DATA SOURCES/STUDY SETTING: A simple accounting model is developed for forecasting health expenditures, using the U.S. Medicare system as an example. PRINCIPAL FINDINGS: Medicare expenditures are projected to rise from 2.2 percent of GDP (gross domestic product) to about 8 percent of GDP by 2075. This increase is due in equal measure to increasing health spending per beneficiary and to population aging. The traditional projection method constructs high, medium, and low scenarios to assess uncertainty, an approach that has many problems. Using stochastic forecasting, we find a 95 percent probability that Medicare spending in 2075 will fall between 4 percent and 18 percent of GDP, indicating a wide band of uncertainty. Although there is substantial uncertainty about future mortality decline, it contributed little to uncertainty about future Medicare spending, since lower mortality both raises the number of elderly, tending to raise spending, and is associated with improved health of the elderly, tending to reduce spending. Uncertainty about fertility, by contrast, leads to great uncertainty about the future size of the labor force, and therefore adds importantly to uncertainty about the health-share of GDP. In the shorter term, the major source of uncertainty is health spending per capita. CONCLUSIONS: History is a valuable guide for quantifying our uncertainty about future health expenditures. The probabilistic model we present has several advantages over the high-low scenario approach to forecasting. It indicates great uncertainty about future Medicare expenditures relative to GDP.
OBJECTIVE: To quantify uncertainty in forecasts of health expenditures. STUDY DESIGN: Stochastic time series models are estimated for historical variations in fertility, mortality, and health spending per capita in the United States, and used to generate stochastic simulations of the growth of Medicare expenditures. Individual health spending is modeled to depend on the number of years until death. DATA SOURCES/STUDY SETTING: A simple accounting model is developed for forecasting health expenditures, using the U.S. Medicare system as an example. PRINCIPAL FINDINGS: Medicare expenditures are projected to rise from 2.2 percent of GDP (gross domestic product) to about 8 percent of GDP by 2075. This increase is due in equal measure to increasing health spending per beneficiary and to population aging. The traditional projection method constructs high, medium, and low scenarios to assess uncertainty, an approach that has many problems. Using stochastic forecasting, we find a 95 percent probability that Medicare spending in 2075 will fall between 4 percent and 18 percent of GDP, indicating a wide band of uncertainty. Although there is substantial uncertainty about future mortality decline, it contributed little to uncertainty about future Medicare spending, since lower mortality both raises the number of elderly, tending to raise spending, and is associated with improved health of the elderly, tending to reduce spending. Uncertainty about fertility, by contrast, leads to great uncertainty about the future size of the labor force, and therefore adds importantly to uncertainty about the health-share of GDP. In the shorter term, the major source of uncertainty is health spending per capita. CONCLUSIONS: History is a valuable guide for quantifying our uncertainty about future health expenditures. The probabilistic model we present has several advantages over the high-low scenario approach to forecasting. It indicates great uncertainty about future Medicare expenditures relative to GDP.
Authors: Stephen B Thacker; Donna F Stroup; Vilma Carande-Kulis; James S Marks; Kakoli Roy; Julie L Gerberding Journal: Public Health Rep Date: 2006 Jan-Feb Impact factor: 2.792
Authors: Fredric D Wolinsky; Henry W Mahncke; Mark Kosinski; Frederick W Unverzagt; David M Smith; Richard N Jones; Anne Stoddard; Sharon L Tennstedt Journal: BMC Health Serv Res Date: 2009-06-29 Impact factor: 2.655