The Influencing Factors of Healthcare Insurance Coverage Based on the Multiple Linear Regression Model

Abstract

The rapid growth of the healthcare insurance market in recent years has caused a number of concerns about the financial burden on insured individuals in purchasing health insurance coverage, as well as the sustainability of insurance funds in the future. To address these issues, multiple linear regression is defined as a method to interpret the change in healthcare coverage amount as a result of demographic factors, disease type, and healthcare expenses. Due to the time effects on the original healthcare coverage amount, the lagged healthcare coverage amount has been added. The model is based on a dataset containing 736 patients' records. This relationship is formulated as an equation. The assumptions and multilinear regression analysis - normality, linearity, and extreme values - are examined. The results suggest that gender, body mass index, race, healthcare expenses, and lagged healthcare coverage amount have a significant impact on the level of healthcare coverage amount.