만성질환 의료비 예측과 건강행동시나리오에 따른 절감 효과 분석
Prediction of Medical Expenses for Chronic Diseases and Analysis of Savings According to Health Behavior Scenarios
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The rapid increase of chronic diseases caused by population aging and changes in lifestyle is proceeding all over the world. Chronic diseases are ‘lifestyle-related diseases' that require constant management and tracking. As chronic diseases increase, social and personal burdens also increase. It is essential to identify and predict the cost of chronic diseases. And It is necessary to propose a direction to move forward through the review of health promotion policies. The purpose of the study is to predict the medical expenses as well as the future prevalent population of chronic diseases. Not only this, but also the study simulates health promotion scenarios to propose the milestone to health promotion policies. Future Elderly Model(FEM) is a very useful dynamic microsimulation analysis method to predict future medical conditions(population, expenses etc.) First developed in 2004 by RAND Corp. and CMS(The Centers for Medicare & Medicaid Services), FEM was designed to investigate the medical expenses of Medicare beneficiaries. As FEM is a dynamic microsimulation model, the data should be personal level date with longitudinal design. National Health Insurance Service sample cohort DB(NHID-cohort) is a well-managed cohort database which represents total population. Based on FEM using NHID-cohort(2009~2013), we predicted the prevalent population and medical expenses for 11 chronic diseases by 2015 to 2041 in every two year. The most prevalent population increases disease is hypertension(6,932,380 increment). Chronic renal failure is the disease with the greatest rate of increase(3.68 times) in the prevalent population. The prevalence of hypertension increase(10.62) is the highest, and in 2041 it is estimated that 42.00% of the population over 40 has hypertension. The diseases with high prevalence are diabetes(18.89%), mental and behavioral disorders(18.96%), and neurological diseases(19.05%). The cost of chronic diseases in 2041 was estimated to increase by 61.68 trillion won(3.96 times) compared to 2015, to 82.52 trillion won. Disease with the largest increase in medical expense is mental and behavioral disorders with an increase of 14.76 trillion won, 4.81 times the cost of medical expenses in 2015. Cancer expense increases to 16.33 trillion won. Lastly, the effect of reducing medical expenditure reflecting changes in health status with decreasing smoking and obesity rates is simulated. If smoking rate reduction scenario is successfully implemented, the cost of 11 chronic diseases is predicted to save 15.47 billion won by 2021 and 50.71 billion won by 2041. If obesity rate reduction scenario successfully implemented, the cost of 11 chronic diseases is predicted to save 72 billion won in 2021 and 201.6 billion won in 2041. This study has significant implications for predicting the prevalent population of chronic diseases and increasing medical expenditure as social problems in Korea. It also has the potential to be used as a basic data for policy through improvement of health status through simulation.
Abstract
The rapid increase of chronic diseases caused by population aging and changes in lifestyle is proceeding all over the world. Chronic diseases are ‘lifestyle-related diseases' that require constant management and tracking. As chronic diseases increase, social and personal burdens also increase. It is essential to identify and predict the cost of chronic diseases. And It is necessary to propose a direction to move forward through the review of health promotion policies. The purpose of the study is to predict the medical expenses as well as the future prevalent population of chronic diseases. Not only this, but also the study simulates health promotion scenarios to propose the milestone to health promotion policies. Future Elderly Model(FEM) is a very useful dynamic microsimulation analysis method to predict future medical conditions(population, expenses etc.) First developed in 2004 by RAND Corp. and CMS(The Centers for Medicare & Medicaid Services), FEM was designed to investigate the medical expenses of Medicare beneficiaries. As FEM is a dynamic microsimulation model, the data should be personal level date with longitudinal design. National Health Insurance Service sample cohort DB(NHID-cohort) is a well-managed cohort database which represents total population. Based on FEM using NHID-cohort(2009~2013), we predicted the prevalent population and medical expenses for 11 chronic diseases by 2015 to 2041 in every two year. The most prevalent population increases disease is hypertension(6,932,380 increment). Chronic renal failure is the disease with the greatest rate of increase(3.68 times) in the prevalent population. The prevalence of hypertension increase(10.62) is the highest, and in 2041 it is estimated that 42.00% of the population over 40 has hypertension. The diseases with high prevalence are diabetes(18.89%), mental and behavioral disorders(18.96%), and neurological diseases(19.05%). The cost of chronic diseases in 2041 was estimated to increase by 61.68 trillion won(3.96 times) compared to 2015, to 82.52 trillion won. Disease with the largest increase in medical expense is mental and behavioral disorders with an increase of 14.76 trillion won, 4.81 times the cost of medical expenses in 2015. Cancer expense increases to 16.33 trillion won. Lastly, the effect of reducing medical expenditure reflecting changes in health status with decreasing smoking and obesity rates is simulated. If smoking rate reduction scenario is successfully implemented, the cost of 11 chronic diseases is predicted to save 15.47 billion won by 2021 and 50.71 billion won by 2041. If obesity rate reduction scenario successfully implemented, the cost of 11 chronic diseases is predicted to save 72 billion won in 2021 and 201.6 billion won in 2041. This study has significant implications for predicting the prevalent population of chronic diseases and increasing medical expenditure as social problems in Korea. It also has the potential to be used as a basic data for policy through improvement of health status through simulation.
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