An Empirical Analysis of The Influencing Factors of America’s Medical Insurance Cost
DOI:
https://doi.org/10.54097/m45jqk85Keywords:
Medical insurance cost, BMI, Smoking status, Multiple linear regression, log-linear modelAbstract
This paper empirically analyzes the factors influencing the cost of medical insurance in the United States. Based on data from 12,761 American individuals sourced from Kaggle, the study examines the relationships between age, gender, body mass index (BMI), diabetes, smoking status, and medical insurance costs. Multiple linear regression models were employed, and model optimization techniques, such as log-linear transformation, were applied to enhance the explanatory power of the analysis. The results indicate that age, BMI, diabetes, and smoking status have significant positive correlations with medical insurance costs, while gender (female) shows a negative correlation. The final log-linear model achieved a high goodness-of-fit (R²=0.694), demonstrating that these factors effectively explain variations in medical insurance costs. The study provides valuable insights for insurance companies in premium pricing and for individuals in health management.
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[1] American Diabetes Association (2018) ‘Economic costs of diabetes in the U.S. in 2017’, Diabetes Care, 41(5), pp. 917-928. Available at: https://doi.org/10.2337/dci18-0007 (Accessed: 12 November 2024).
[2] Anderson, M., Dobkin, C. and Gross, T. (2012) ‘The Effect of Health Insurance Coverage on the Use of Medical Services’, American Economic Journal: Economic Policy, 4(1), pp. 1–27. doi: 10.1257/pol.4.1.1
[3] Benoit, K. (2011) Linear Regression Models with Logarithmic Transformations London: London School of Economics, pp. 1-8.(Accessed: 12 November 2024).
[4] Duncan, I., Loginov, M., and Ludkovski, M. (2016) ‘Testing Alternative Regression Frameworks for Predictive Modeling of Health Care Costs’, North American Actuarial Journal, 20(1), pp. 65-87.(Accessed: 12 November 2024).
[5] Gupta, S. (2022) Health insurance data set. Available at: https://www.kaggle.com/datasets/sureshgupta/health-insurance-data-set/data (Accessed: 10 November 2024).
[6] He, Q.J. (2024) ‘Research on Factors Influencing Medical Insurance Cost in the US’, Transactions on Economics Business and Management Research, 10, pp. 31-36. doi:10.62051/yj92kn72.
[7] Kwapisz, A. (2022) ‘Health insurance coverage and sources of advice in entrepreneurship: Gender differences’, Journal of Business Venturing Insights, 14, pp. 1-12. Available at: https://doi.org/10.1016/j.jbvi.2020.e00177 (Accessed: 13 November 2024).
[8] Li, K.T. (2021) ‘Regional Disparity of Medical Insurance Fund Expenditure——Based on Dagum Gini Method’, Public Finance Research Journal, 2, pp. 62-71. Available at: https://cstj.cqvip.com/Qikan/Article/Detail?id=7105015056 (Accessed: 10 November 2024).
[9] Norris, K.C. (2016) ‘Health Insurance and Blood Pressure Control’, Journal of the American Heart Association, 5(12), pp. 1-5. doi:10.1161/jaha.116.005130.
[10] Orji, U., Ukwandu, E. (2023) Machine learning for an explainable cost prediction of medical insurance, pp. 1-42. Available at: https://search.ebscohost.com/login.aspx?direct=true&db=edsarx&AN=edsarx.2311.14139&site=eds-live&scope=site (Accessed: 12 November 2024).
[11] Plemons, C. (2024) Individual and Family——What is a Health Insurance Claim? Available at: https://www.ehealthinsurance.com/resources/individual-and-family/what-is-a-health-insurance-claim (Accessed: 20 November 2024).
[12] Ramachandran, V., Kavitha, A.R. and Pandimeena, R. (2023) ‘An Accurate Prediction of Medical Insurance Cost Using Forest Regression Algorithms’, 2023 International Conference on Data Science, Agents & Artificial Intelligence (ICDSAAI), Chennai, India, 21-23 December, pp. 1-4. Available at: https://ieeexplore.ieee.org/abstract/document/10452541/citations#citations (Accessed: 20 November 2024)
[13] Singhal, N., Goyal, S. and Singhal, T. (2022) ‘The relationship between insurance and economic growth in Asian countries: a regional perspective’, Macroeconomics and Finance in Emerging Market Economies, 15(3), pp. 301-322. Available at: https://doi.org/10.1080/17520843.2021.1957599 (Accessed: 15 November 2024).
[14] World Health Organization (2021) Global expenditure on health: public spending on the rise? Geneva: World Health Organization. Available at: https://www.who.int/publications/i/item/9789240041219 (Accessed: 10 November 2024).
[15] Xia, X. et al. (2024) ‘Association of body mass index with risk of cardiometabolic disease, multimorbidity and mortality: a multi-state analysis based on the Kailuan cohort’, Endocrine: International Journal of Basic and Clinical Endocrinology, 84, pp. 355–364. Available at: https://doi.org/10.1007/s12020-023-03570-w (Accessed: 15 November 2024).
[16] Yun, Y.H. et al. (2005) ‘Cigarette smoking and cancer incidence risk in adult men: National Health Insurance Corporation Study’, Cancer Detection and Prevention, 29(1), pp.15–24. Available at: https://doi.org/10.1016/j.cdp.2004.08.006 (Accessed: 13 November 2024).
[17] Zhang, P.F. (2019) ‘Study on the Impact of Environmental Pollution on Medical Insurance Expenditure and Its Mechanism’, Modern Economic Research, 10, pp. 28-37. doi: 10.13891/j.cnki.mer.2019.10.005.
[18] Zhou, L.Y. and Zhao, L.L. (2022) ‘The Study on the Influence of WeChat-sports Health Factors on Health Insurance Premium Ratio’, Science Technology and Industry, 22(7), pp. 253-258. Available at: https://cstj.cqvip.com/Qikan/Article/Detail?id=7107609776&from=Qikan_Search_Index (Accessed: 10 November 2024).
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