|Friday, February 19|
|PS2 Poster Session 2 & Refreshments||
Fri, Feb 19, 5:15 PM - 6:30 PM
Predictive Models of Health Expenditure Using Regularization: Do Low-Income and Lower Middle-Income Economies Share Common Predictors? (303232)*Emmanuel Thompson, Southeast Missouri State University
Faustine Williams, Washington University in St. Louis School of Medicine
Keywords: Regularization, Lasso, Elastic net, Health expenditure per capita
Countries around the world are presently confronted with gargantuan health care challenges and huge variability in health spending. In the literature, income has been recognized as a crucial predictor of health expenditure. However, there is no agreement on which other variables may be connected to the remaining largely unexplained variation in health expenditure. Therefore, the aim of the present study was to use regularization method to investigate the link between health expenditure and some important predictors among low-income and lower middle-income economies using 2013 data from the World Bank. Initial results showed that the elastic net algorithm produced a model with a better predictive power than the Lasso algorithm in the case of low-income economies. However, the Lasso resulted in a model with a better predictive power than the elastic net in the case of lower middle-income economies. Findings of the study would be valuable to governments seeking to lessen the impact of vast variability in health care spending on their economies by focusing on other key predictors of health expenditure per capita, such as life expectancy and population density.