Social and Behavioral Sciences Commons^™

Estimation Of Uninsurance Rate: Comparison Of Four Estimation Methods, Jackie Zhang, Renee Hotchkiss, Thomas Wan

Thomas T.H. Wan

Objective: Although high percentage of the uninsured is an important public policy issue, the discrepancies in both state and national estimates of the numbers of uninsured are reported. This study compares four advanced estimation methods for uninsurance, by using Florida Health Insurance Survey data as an example. Design: The four predictive models include decision tree, neural network, general logistic regression, and two-stage logistic regression. Risk factors to uninsurance are identified. Population: The study sample comes from the Florida Health Insurance Study data collected for the Florida Agency of Health Care Administration in 1999, representing the first large-scale study designed exclusively …