미시모의실험모형을 이용한 장래 건강보험 진료비 추정 = Projecting the National Health Insurance medical care cost using Micro-simulation model

다국어 초록 (Multilingual Abstract)

In Korea, the National Health Insurance (NHI) expenditure is expected to rapidly increase due to population ageing and the government’s policy of expanding the health insurance coverage, as estimated in previous studies using component-based or macro models. Compared to such models, the micro-simulation model has advantages in reflecting dynamic changes in individual-level determinants of medical care cost, population structure, and policy effect on individual behaviors. In this study, we designed a micro-simulation model for estimating the National Health Insurance medical care cost between 2019 and 2065 and produced detailed projection results using the Korean Health Panel Survey data. Our results suggested that total NHI medical care cost increased to 150 trillion Korean Won (KRW) in 2065, of which 2/3 was cost incurred by older adults aged 65 and over. Once we incorporated effect of the health insurance coverage expansion policy, the total NHI medical cost in 2065 increased to 206 trillion KRW. Additionally, projected medical care cost in 2060 approximately corresponded to 6% of projected GDP in Korea. We also showed that our results are subject to the key components of the model, including the period for parameter estimation, population projection scenarios, the presumed relationship between changes in health insurance coverage and changes in individual medical cost, by suggesting that the NHI medical care cost increased to 16% of the projected GDP if we change the parameter estimation period and the assumption on insurance coverage-medical care cost relationship.

참고문헌 (Reference)

• 1 최대환, "최근 20년간 건강보험 진료비 경향" 14 (14): 2020
• 2 통계청, "지출목적별 소비자물가지수(품목포함, 2020=10 0)"
• 3 행정안전부, "주민등록인구통계, 주민등록 인구현황"
• 4 통계청, "장래인구추계 : 2020~2070년" 통계청 2021
• 5 통계청, "완전생명표(1세별)"
• 6 고제이, "미시모의실험 기반 중장기 사회 재정 영향 평가 모형 개발-노후 소득 보장 정책을 중심으로" 2016
• 7 김우현, "미시 모의실험 모형을 이용한 의료 이용량 추정" 한국보건경제정책학회 27 (27): 89-117, 2021
• 8 박지혜, "늦어지는 은퇴, 생애주기수지 적자에 대비하라" 미래에셋투자 2022
• 9 정형선, "국민의료비 미래추계 구축방안" 보건복지부·연세대학교 원주산학협력단·한국보건사회연구원 2015
• 10 이태진, "건강보험 중장기 재정 추계 방법론 연구" 국회예산정책처 2020
• 11 이은경, "건강보험 재정의 현황과 정책과제" 51-64, 2018
• 12 김우현, "건강보험 보장성 강화 정책의 효과 분석 : 4대 중증질환을 중심으로" 한국조세재정연구원 1-274, 2018
• 13 Flood, L., "SESIM III–a Swedish dynamic microsimulation model" Ministry of Finance 2005
• 14 Flood, L, "SESIM III - A Swedish dynamic micro simulation model" 2012
• 15 Briggs, A., "Parametric modelling of cost data: some simulation evidence" 14 (14): 421-428, 2005
• 16 White, I. R., "Multiple imputation using chained equations: Issues and guidance for practice" 30 (30): 377-399, 2011
• 17 Royston P, "Multiple imputation of missing values" 4 (4): 227-241, 2004
• 18 Sterne, J. . A. C., "Multiple imputation for missing data in epidemiological and clinical research: potential and pitfalls" 338 : b2393-, 2009
• 19 Goldman, Dana P., "Modeling the Health and Medical Care Spending of the Future Elderly" RAND Corporation
• 20 OECD, "GDP long-term forecast(indicator)"
• 21 Manning, W. G., "Estimating log models: to transform or not to transform?" 20 (20): 461-494, 2001
• 22 Li, Jinjing., "A survey of dynamic microsimulation models : uses, model structure, and methodology" 6 (6): 3-55, 2013
• 23 Astolfi, R., "A comparative analysis of health forecasting methods" OECD 1-120, 2012
• 24 통계청, "2021년 출생통계(확정), 국가승인통계 제10103호 출생통계"
• 25 국민건강보험공단, "2021년 건강보험 보장률 64. 5%"
• 26 건강보험심사평가원, "2019건강보험통계연보"
• 27 건강보험심사평가원, "2012건강보험통계연보"