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Prediction of Changes in Health Expenditure of Chronic Diseases between Age group of Middle and Old Aged Population by using Future Elderly Model

Future Elderly Model을 활용한 중·고령자의 연령집단별 3대 만성질환 의료비 변화 예측

  • Baek, Mi Ra (Department of Health Services Management, Kyung Hee University Graduate School) ;
  • Jung, Kee Taig (Department of Health Services Management, Kyung Hee University Graduate School)
  • 백미라 (경희대학교 일반대학원 의료경영학과) ;
  • 정기택 (경희대학교 일반대학원 의료경영학과)
  • Received : 2016.08.01
  • Accepted : 2016.09.21
  • Published : 2016.09.30

Abstract

Background: The purpose of this study is to forecast changes in the prevalence of chronic diseases and health expenditure by age group. Methods: Based on the Future Elderly Model, this study projects the size of Korean population, the prevalence of chronic diseases, and health expenditure over the 2014-2040 period using two waves (2012, 2013) of the Korea Health Panel and National Health Insurance Service database. Results: First, the prevalence of chronic diseases increases by 2040. The population with hypertension increases 2.04 times; the diabetes increases 2.43 times; and the cancer increases 3.38 times. Second, health expenditure on chronic diseases increases as well. Health expenditure on hypertension increases 4.33 times (1,098,753 million won in 2014 to 4,760,811 million won in 2040); diabetes increases 5.34 times (792,444 million won in 2014 to 4,232,714 million won in 2040); and cancer increases 6.09 times (4,396,223 million won in 2014 to 26,776,724 million won in 2040). Third, men and women who belong to the early middle-aged group (44-55 years old) as of 2014, have the highest increase rate in health spending. Conclusion: Most Korean literature on health expenditure estimation employs a macro-simulation approach and does not fully take into account personal characteristics and behaviors. Thus, this study aims to benefit medical administrators and policy makers to frame effective and targeted health policies by analyzing personal-level data with a microsimulation model and providing health expenditure projections by age group.

Keywords

References

  1. Statistics Korea. 2015 Elderly statistics. Daejeon: Statistics Korea; 2015.
  2. Health Insurance Review and Assessment Service. The rapid increase of the olds over 75 years old led the entire health expenditure. Wonju: Health Insurance Review and Assessment Service; 2015.
  3. Goldman DP, Shekelle PG, Bhattacharya J, Hurd M, Joyce GF; Rand Corporation. Health status and medical treatment of the future elderly. Santa Monica (CA): RAND; 2014.
  4. Astolfi R, Lorenzoni L, Oderkirk J. A comparative analysis of health forecasting methods. Paris: Organization for Economic Cooperation and Development; 2012.
  5. Jung KT, Ha BC. Vision and strategy of the medical services industry by 2020. Sejong: Korea Institute for Industrial Economics and Trade; 2007.
  6. Jeong HS, Song YM. Contributing factors to the increases in health insurance expenditures for the aged and their forecasts. Korean J Health Econ Policy 2013;19(2):21-38.
  7. Kim JM. The long-term outlook: the case of health expenditure (health insurance expenditure). Sejong: Korea Institute of Public Finance; 2015.
  8. Lee SY, Kim YH, Kim HS. Estimate over the number of chronic disease patients and medical care expenditure at the time of transition of baby boomer into 65 years old aging population. Health Policy Manag 2013;23(4):377-386. DOI: http://dx.doi.org/10.4332/kjhpa.2013.23.4.376.
  9. Lee SY, Lee DH, Joe JW. A medium-and long-term estimated study on the health expenditure over 65 years old. Wonju: National Health Insurance Service; 2015.
  10. Jeong YH, KO SJ, Lee YK, Park SB, Lee JH. Lifetime cost of obesity and smoking and long-term effectiveness of health promotion. Sejong: Korea Institute for Health and Social Affairs; 2010.
  11. Lim DO. The estimated lifetime health expenditure and characterization. Cheongju: Korea Health Industry Development Institute; 2013.
  12. Alemayehu B, Warner KE. The lifetime distribution of health care costs. Health Serv Res 2004;39(3):627-642. DOI: http://dx.doi.org/10.1111/j.1475-6773.2004.00248.x.
  13. Chung WJ. Stochastic forecasting health expenditure with the application to the Korea's National Health Insurance System. Korean Soc Secur Assoc 2007;23(2):249-270.
  14. Lakdawalla DN, Goldman DP, Shang B. The health and cost consequences of obesity among the future elderly. Health Aff (Millwood) 2005;24 Suppl 2:W5R30-41. DOI: http://dx.doi.org/10.1377/hlthaff.w5.r30.
  15. Shekelle PG, Ortiz E, Newberry SJ, Rich MW, Rhodes SL, Brook RH, et al. Identifying potential health care innovations for the future elderly. Health Aff (Millwood) 2005;24 Suppl 2:W5R67-76. DOI: http://dx.doi.org/10.1377/hlthaff.w5.r67.
  16. Goldman DP, Shang B, Bhattacharya J, Garber AM, Hurd M, Joyce GF, et al. Consequences of health trends and medical innovation for the future elderly. Health Aff (Millwood) 2005;24 Suppl 2:W5R5-17. DOI: http://dx.doi.org/10.1377/hlthaff.w5.r5.
  17. Michaud PC, Goldman DP, Lakdawalla DN, Zheng Y, Gailey AH. The value of medical and pharmaceutical interventions for reducing obesity. J Health Econ 2012;31(4):630-643. DOI: http://dx.doi.org/10.1016/j.jhealeco.2012.04.006.
  18. Bhattacharya J, Cutler DM, Goldman DP, Hurd MD, Joyce GF, Lakdawalla DN, et al. Disability forecasts and future Medicare costs. Front Health Policy Res 2004;7:75-94. DOI: http://dx.doi.org/10.2202/1558-9544.1052.
  19. Manton KG, Corder L, Stallard E. Chronic disability trends in elderly United States populations: 1982-1994. Proc Natl Acad Sci U S A 1997;94(6):2593-2598. DOI: http://dx.doi.org/10.1073/pnas.94.6.2593.
  20. Bhattacharya J, Shang B, Su CK, Goldman DP. Technological advances in cancer and future spending by the elderly. Health Aff (Millwood) 2005;24 Suppl 2:W5R53-66. DOI: http://dx.doi.org/10.1377/hlthaff.w5.r53.
  21. Shang B, Goldman D. Does age or life expectancy better predict health care expenditures? Health Econ 2008;17(4):487-501. DOI: http://dx.doi.org/10.1002/hec.1295.
  22. Michaud PC, Goldman D, Lakdawalla D, Gailey A, Zheng Y. Differences in health between Americans and Western Europeans: effects on longevity and public finance. Soc Sci Med 2011;73(2):254-263. DOI: http://dx.doi.org/10.1016/j.socscimed.2011.05.027.
  23. Chen BK, Jalal H, Hashimoto H, Suen SC, Eggleston K, Hurley M, et al. Forecasting trends in disability in a super-aging society: adapting the future elderly model to Japan. Cambridge (MA): National Bureau of Economic Research; Baek MR, Min IS, Jung KT.
  24. Baek M, Min I, Jung K. Estimating the middle and old aged population with major chronic diseases: adapting the future elderly model. J Health Info Stat 2016;41(2):212-222. DOI: http://dx.doi.org/10.21032/jhis.2016.41.2.212.
  25. Kang SM, Jeong HS, Song YM, Lee KS. Forecasting future public health expenditures in consideration of population ageing. The Korean Journal of Health Economics and Policy 2009;15(2):1-20.