Financial Projection for National Health Insurance using NHIS Sample Cohort Data Base
- Authors
- Park, Yousung; Park, Haemin; Kwon, Tae Yeon
- Issue Date
- 8월-2015
- Publisher
- KOREAN STATISTICAL SOC
- Keywords
- national health insurance; projection of finance; NHIS sample cohort DB; prevalence rate; per capita co-payment for healthcare expenses; VECM-LC
- Citation
- KOREAN JOURNAL OF APPLIED STATISTICS, v.28, no.4, pp.663 - 683
- Indexed
- KCI
- Journal Title
- KOREAN JOURNAL OF APPLIED STATISTICS
- Volume
- 28
- Number
- 4
- Start Page
- 663
- End Page
- 683
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/92796
- DOI
- 10.5351/KJAS.2015.28.4.663
- ISSN
- 1225-066X
- Abstract
- The change of the population pyramid due to low fertility and rapid aging threatens the financial sustainability of National Health Insurance. We construct statistical models for prevalence rates and medical expenses using National Health Insurance Service (NHIS) sample cohort data from 2002-2013. We then project yearly expenditures and income of national health insurance until 2060 that considers various assumptions in regards to future population structure and economic conditions. We adopt a VECM-LC model for prevalence rates and the double exponentially smoothing method for the per capita co-payment of healthcare expense (in which the two models are institution-disease-sex-age specific) to project of national health insurance expenditures. We accommodate various assumptions of economic situations provided by the national assembly and government to produce a financial projection for national health insurance. Two assumptions of dependents ratios are used for the projection of national health insurance income to conduct two future population structures by the two assumptions of aging progresses and various assumptions on economic circumstances as in the expenditure projection. The health care deficit is projected to be 20-30 trillion won by 2030 and 40-70 trillion won by 2060 in 2015 constant price.
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Collections - College of Political Science & Economics > Department of Statistics > 1. Journal Articles
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