THE ECONOMICS OF HEALTH INSURANCE COVERAGE LEVELS IN THE U.S.: A PREDICTIVE MODELLING OF POLICYHOLDER PREFERENCES

This study uses a synthetic dataset modelled on the insured U.S. population to examine the determinants influencing the selection of coverage levels of Basic, Standard, and Premium health insurance plans in private markets. Multinomial logistic regression and random forest models were employed to evaluate the impact of demographic, socioeconomic, lifestyle, and clinical variables. The findings reveal that insurance cost is the most decisive factor, with higher premiums steering consumers away from basic plans toward more comprehensive options. Older individuals, those with higher BMI, and those with more children were more likely to choose lower-tier coverage, likely due to financial constraints, while younger individuals preferred premium plans. Surprisingly, smokers and those with a history of heart disease often selected Basic coverage, suggesting cost-related underinsurance among high-risk groups. Other influencing factors included gender, exercise habits, region, and occupation. The random forest model validated these results with an accuracy of 80%. Overall, the study highlights that insurance choices are shaped by a complex interplay of affordability, perceived risk, and socioeconomic context, underscoring the need for personalised pricing, streamlined plan design, and targeted support tools to promote equitable and efficient plan selection.