In this article, we provide a novel criterion for decision making by addressing the statistical analysis and modeling of health protection expenditures relative to health system of gross domestic product in a comparative study of four different countries, namely the United States, Malaysia, Egypt, and kingdom of Saudi Arabia. Researchers examined the issue of spending on health protection expenditures in relation to gross domestic product from a variety of angles, including social and statistical. Previous statistical studies also addressed the study of statistical modeling through regression approach. Here we study this issue from a different perspective, namely modeling with statistical distributions. In the statistical modeling of the data, we use an extended heavy-tailed updated version of Weibull distribution named the generalized Weibull distribution Weibull (GWD-W) model, which has good statistical properties in terms of flexibility and goodness of fit. Some distributional properties and statistical functions, including the Renyi entropy, skewness, kurtosis, the heavy-tailed behavior, regular variation, and identifiable property are given. Two important actuarial risk measures are derived. A simulation study is conducted to prove the usefulness of the two actuarial measures in finance. The estimation of the model parameters via the maximum likelihood approach is discussed. Comparison study vs some competitive statistical models is performed using the Kolmogorov-Smirnov test for a sample and some detection criteria. The discussion shows that proposed statistical modeling of health care expenditure as a percentage of gross domestic product (GDP) for health care compares well with their peers.
Citation: Walid Emam. Benefiting from statistical modeling in the analysis of current health expenditure to gross domestic product[J]. AIMS Mathematics, 2023, 8(5): 12398-12421. doi: 10.3934/math.2023623
In this article, we provide a novel criterion for decision making by addressing the statistical analysis and modeling of health protection expenditures relative to health system of gross domestic product in a comparative study of four different countries, namely the United States, Malaysia, Egypt, and kingdom of Saudi Arabia. Researchers examined the issue of spending on health protection expenditures in relation to gross domestic product from a variety of angles, including social and statistical. Previous statistical studies also addressed the study of statistical modeling through regression approach. Here we study this issue from a different perspective, namely modeling with statistical distributions. In the statistical modeling of the data, we use an extended heavy-tailed updated version of Weibull distribution named the generalized Weibull distribution Weibull (GWD-W) model, which has good statistical properties in terms of flexibility and goodness of fit. Some distributional properties and statistical functions, including the Renyi entropy, skewness, kurtosis, the heavy-tailed behavior, regular variation, and identifiable property are given. Two important actuarial risk measures are derived. A simulation study is conducted to prove the usefulness of the two actuarial measures in finance. The estimation of the model parameters via the maximum likelihood approach is discussed. Comparison study vs some competitive statistical models is performed using the Kolmogorov-Smirnov test for a sample and some detection criteria. The discussion shows that proposed statistical modeling of health care expenditure as a percentage of gross domestic product (GDP) for health care compares well with their peers.
[1] | M. Sardar, The relationship between economic development and social welfare: A new adjusted GDP measure of welfare, Soc. Indic. Res., 57 (2002), 201–229. |
[2] | C. Matthew, M. Sardar, Health adjusted GDP measures of the relationship between economic growth, health outcomes and socialwelfare, In: Conference on Health and Economic Policy, Australia, 57 (2003), 201–228. |
[3] | B. Brent, Alternative welfare measures, Vrije Universiteit Brussel, Belgium, 2005, 1–15. |
[4] | B. Romina, J. Asa, M. Marco, Alternative measures of well-being, OECD Social, Employment and Migration Working Papers, 2006. https://doi.org/10.1787/1815199X |
[5] | H. Malin, Economic growth and happiness in the western world today, University of Lund, Department of Sociology, 2006, 1–28. |
[6] | S. Gabriel, An empirical investigation of social protection expenditures on economic growth in Greece, Paper for the 4th Hellenic Observatory, European lnstitute, LSE, Greece, 2009, 25–26. |
[7] | D. Hong, Economic growth and welfare state, a debate of econometrics, MPRA Paper, 2012. |
[8] | D. Partha, E. C. Norton, Modeling health care expenditures and use, Annu. Rev. Publ. Health, 39 (2018), 489–505. https://doi.org/10.1146/annurev-publhealth-040617-013517 doi: 10.1146/annurev-publhealth-040617-013517 |
[9] | A. S. Malehi, F. Pourmotahari, K. A. Angali, Statistical models for the analysis of skewed healthcare cost data: A simulation study, Health Econ. Rev., 5 (2015), 1–16. https://doi.org/10.1186/s13561-015-0045-7 doi: 10.1186/s13561-015-0045-7 |
[10] | G. Cuckler, A. Sisko, Modeling per capita state health expenditure variation: State-level characteristics matter, Medicare Medicaid Res. Rev., 26 (2013), 4. https://doi.org/10.5600/mmrr.003.04.a03 doi: 10.5600/mmrr.003.04.a03 |
[11] | J. T. Guemmegne, J. J. Kengwoung-Keumo, M. A. Tabatabai, K. P. Singh, Modeling the dynamics of the U.S. healthcare expenditure using a hyperbolastic function, Medicare Medicaid Res. Rev., 2 (2014), 95–117. |
[12] | W. Emam, Y. Tashkandy, A new generalized modified Weibull model: Simulating and modeling the dynamics of the COVID-19 pandemic in Saudi Arabia and Egypt, Math. Probl. Eng., 2022 (2022), 1–9. https://doi.org/10.1155/2022/1947098 doi: 10.1155/2022/1947098 |
[13] | W. Emam, Y. Tashkandy, Khalil new generalized weibull distribution based on ranked samples: Estimation, mathematical properties, and application to COVID-19 data, Symmetry, 44 (2022), 853. https://doi.org/10.3390/sym14050853 doi: 10.3390/sym14050853 |
[14] | W. Emam, Y. Tashkandy, The Arcsine Kumaraswamy-generalized family: MLE and classical estimates and application, Symmetry, 14 (2022), 2311. https://doi.org/10.3390/sym14112311 doi: 10.3390/sym14112311 |
[15] | W. Emam, Y. Tashkandy, Modeling the amount of carbon dioxide emissions application: New modified Alpha power Weibull-X family of distributions, Symmetry, 15 (2023), 366. https://doi.org/10.3390/sym15020366 doi: 10.3390/sym15020366 |
[16] | W. Emam, On statistical modeling using a new version of the flexible Weibull model: Bayesian, maximum likelihood estimates, and distributional properties with applications in the actuarial and engineering fields, Symmetry, 15 (2023), 560. https://doi.org/10.3390/sym15020560 doi: 10.3390/sym15020560 |
[17] | W. Emam, Y. Tashkandy, The Weibull claim model: Bivariate extension, MLE, and maximum likelihood estimations, Math. Probl. Eng., 2022 (2022), 1–10. https://doi.org/10.1155/2022/8729529 doi: 10.1155/2022/8729529 |
[18] | J. Cong, Z. Ahmad, B. S. Alsaedi, O. A. Alamri, I. Alkhairy, H. Alsuhabi, The role of Twitter medium in business with regression analysis and statistical modelling, Comput. Intell. Neurosci., 2021 (2021), 1–12. https://doi.org/10.1155/2021/1346994 doi: 10.1155/2021/1346994 |
[19] | Z. Ahmad, E. Mahmoudi, M. Alizadeh, R. Roozegar, A. Z. Afify, The exponential TX family of distributions: Properties and an application to insurance data, J. Math., 2021 (2021), 1–18. https://doi.org/10.1155/2021/3058170 doi: 10.1155/2021/3058170 |
[20] | A. L. Roos, T. V. Goetz, M. Oracek, M. Krannich, M. Bieg, A. Jarrell, et al., Test anxiety and physiological arousal: A systematic review and meta-analysis, Educ. Psychol. Rev., 33 (2021), 579–618. https://doi.org/10.1007/s10648-020-09543-z doi: 10.1007/s10648-020-09543-z |
[21] | R. M. Nejad, Z. Liu, W. Ma, F. Berto, Reliability analysis of fatigue crack growth for rail steel under variable amplitude service loading conditions and wear, Int. J. Fatigue, 152 (2021), 106450. https://doi.org/10.1016/j.ijfatigue.2021.106450 doi: 10.1016/j.ijfatigue.2021.106450 |
[22] | M. Du, X. Yuan, A survey of competitive sports data visualization and visual analysis, J. Visual., 24 (2021), 47–67. https://doi.org/10.1007/s12650-020-00687-2 doi: 10.1007/s12650-020-00687-2 |
[23] | M. Misuraca, G. Scepi, M. Spano, Using opinion mining as an educational analytic: An integrated strategy for the analysis of students' feedback, Stud. Educ. Eval., 68 (2021), 100979. https://doi.org/10.1016/j.stueduc.2021.100979 doi: 10.1016/j.stueduc.2021.100979 |
[24] | M. Specht, Consistency of the empirical distributions of navigation positioning system errors with theoretical distributions: Comparative analysis of the DGPS and EGNOS systems in the years 2006 and 2014, Sensors, 1 (2021), 1–23. https://doi.org/10.3390/s21010031 doi: 10.3390/s21010031 |
[25] | D. Liao, S. P. Zhu, B. Keshtegar, G. Qian, Q. Wang, Probabilistic framework for fatigue life assessment of notched components under size effects, Int. J. Mech. Sci., 181 (2020), 105685. https://doi.org/10.1016/j.ijmecsci.2020.105685 doi: 10.1016/j.ijmecsci.2020.105685 |
[26] | M. A. Mansournia, M. Nazemipour, A. I. Naimi, G. S. Collins, M. J. Campbell, Reflection on modern methods: Demystifying robust standard errors for epidemiologists, Int. J. Epidemiol., 50 (2021), 346–351. https://doi.org/10.1093/ije/dyaa260 doi: 10.1093/ije/dyaa260 |
[27] | S. A. Klugman, H. H. Panjer, G. E. Willmot, Loss models: From data to decisions, 4 Eds., In: Wiley Series in Probability and Statistics, John Wiley and Sons, Hoboken, 2012,528. |
[28] | M. N. Lane, Pricing risk transfer transactions 1, Astin. Bull., 30 (2000), 259–293. https://doi.org/10.2143/AST.30.2.504635 doi: 10.2143/AST.30.2.504635 |
[29] | K. Cooray, M. M. Ananda, Modeling actuarial data with a composite lognormal-Pareto model, Scand. Actuar. J., 5 (2005), 321–334. https://doi.org/10.1080/03461230510009763 doi: 10.1080/03461230510009763 |
[30] | R. Ibragimov, A. Prokhorov, Heavy tails and copulas: Topics in dependence modelling in economics and finance, World Scientific, Connecting Great Mind, 2017. |
[31] | M. Bernardi, A. Maruotti, L. Petrella, Skew mixture models for loss distributions: A MLE approach, Insur. Math. Econ., 51 (2012), 617–623. https://doi.org/10.1016/j.insmatheco.2012.08.002 doi: 10.1016/j.insmatheco.2012.08.002 |
[32] | C. Adcock, M. Eling, N. Loperfido, Skewed distributions in finance and actuarial science: A review, Eur. J. Financ., 21 (2015), 1253–1281. https://doi.org/10.1080/1351847X.2012.720269 doi: 10.1080/1351847X.2012.720269 |
[33] | Z. Landsman, U. Makov, T. Shushi, Tail conditional moments for elliptical and log-elliptical distributions, Insur. Math. Econ., 71 (2016), 179–188. https://doi.org/10.4171/EM/315 doi: 10.4171/EM/315 |
[34] | D. Bhati, S. Ravi, On generalized log-Moyal distribution: A new heavy tailed size distribution, Insur. Math. Econ., 79 (2018), 247–259. https://doi.org/10.1016/j.insmatheco.2018.02.002 doi: 10.1016/j.insmatheco.2018.02.002 |
[35] | G. M. Cordeiro, E. M. M. Ortega, T. G. Ramires, A new generalized Weibull family of distributions: Mathematical properties and applications, J. Stat. Distrib. Appl., 13 (2015), 2–13. https://doi.org/10.1186/s40488-015-0036-6 doi: 10.1186/s40488-015-0036-6 |
[36] | S. Golomb, The IGF of a probability distribution, IEEE T. Inform. Theory, 12 (1966), 75–77. https://doi.org/10.1109/TIT.1966.1053843 doi: 10.1109/TIT.1966.1053843 |
[37] | A. L. Bowley, Elements of statistics, 4 Eds., Charles Scribner's Sons, New York, 1920,220–224. |
[38] | J. J. A. Moors, The meaning of kurtosis: Darlington re-examined, Am. Stat., 40 (1986), 283–284. https://doi.org/10.1080/00031305.1986.10475415 doi: 10.1080/00031305.1986.10475415 |
[39] | E. Seneta, Karamata's characterization theorem, feller and regular variation in probability theory, Publ. I. Math., 71 (2002), 79–89. https://doi.org/10.2298/PIM0271079S doi: 10.2298/PIM0271079S |
[40] | P. Artzner, Application of coherent risk measures to capital requirements in insurance, N. Am. Actuar. J., 3 (1999), 11–25. https://doi.org/10.1080/10920277.1999.10595795 doi: 10.1080/10920277.1999.10595795 |