Factors influencing health insurance premium rates: an explorative and predictive analysis

Abstract

The objective of this study was to assess the factors influencing the premium prices under health insurance. Therefore, it aims to find out whether gender, age, BMI, number of children, smoking status and region have a significant relationship the premium price paid under health insurance. The data used in this research was secondary data and it was got from an online source (https://www.kaggle.com/code/mariapushkareva/medical-insurance-cost-with-linear-regression). The analysis was done using stata. This study applies a linear regression model to secondary data to investigate the factor influencing health insurance premium price. A univariate analysis was done on each of the variables then the bivariate analysis was done for each of the variables alongside the charges to check how much they are correlated. A multiple linear regression model was then carried out and this was used to predict/ fit premium prices and a comparison was made between the actual charges and the fitted/ predicted values. Findings from the study revealed that age, BMI, smoking status, and number of children significantly influence health insurance premium price paid by an individual. However, sex of the individual and region of residence are insignificant. The study therefore recommended That Life insurance companies consider more factors in the computation of premiums especially pre-existing medical conditions and medical history. They should also come up with innovative tailor-made products for specific groups for example age groups, groups categorized by certain diseases. Insurance companies should also consider individual’s region of residence for example if one resides in the urban areas, the cost of living will be higher than that of one residing in rural areas hence recommending insurance companies to charge urban residents higher premiums than rural resident.