DC Field | Value |
---|---|
dc.contributor.author | 고든솔 |
dc.contributor.author | 옹열여 |
dc.contributor.author | 배재용 |
dc.contributor.author | 이루겸 |
dc.contributor.author | 정윤선 |
dc.contributor.author | 천오벳 |
dc.contributor.author | 김영은 |
dc.coverage.temporal | 2022-01-01 - 2022-12-31 |
dc.date.accessioned | 2023-03-30T04:30:10Z |
dc.date.available | 2023-03-30T04:30:10Z |
dc.date.issued | 2022 |
dc.identifier.isbn | 9788968278792 |
dc.identifier.uri | https://repository.kihasa.re.kr/handle/201002/42337 |
dc.description.abstract | 지역 간 건강 격차 및 보건의료자원 분포의 불균형 해소의 중요성이 강조되고 있다. 이 연구는 지역별 질병부담을 사회경제적 비용으로 산출하고, 지역 간 건강 격차 현황과 결정요인을 분석하여 건강 격차를 해소하기 위한 정책과제와 활용 방안을 제언하였다. 연구결과를 바탕으로 지역 간 건강 격차 및 보건의료자원 분포의 불균형을 완화하고 지역의 건강수준을 개선하기 위한 정책 목표와 방향을 설정하는 데 기초자료로 활용되기를 기대한다. |
dc.description.abstract | This study aims to calculate the burden of diseases by region as socioeconomic costs, and to analyze the current status and determinants of regional gaps in order to suggest policy tasks and utilization measures to bridge the gap. The socioeconomic burden of disease was estimated from a social perspective to cover not only medical and non-medical costs, but also productivity loss due to disease transmission of patients and future income loss due to early death. In 2020, the overall socioeconomic disease burden in Korea was KRW169.493 trillion, accounting for 8.7% of GDP. Compared to 2011, the total cost up to 2020 increased by 4.8% annually (direct cost 5.9% and indirect cost 2.6%). In order to examine the regional gap in socioeconomic disease burden, calculation was made per capita by region, which was then standardized based on the population by gender and age nationwide in 2015. From 2011 to 2020, the variation between regions was identified by using the external quantitative (EQ) and co-efficient of variation (CV) of socioeconomic disease burden per person by year. The socioeconomic disease burden per capita by city, county, and district was KRW3,188,212 on average as of 2020, an annual average increase of 3.22% from KRW2,321,573 in 2011. The annual gap ratio (EQ) was distributed from a minimum of 1.74 to a maximum of 2.08, and the CV was 10.67 to 13.19. As a result of regression analysis on the influencing factors, it was found that the lower the proportion of households with income of 3 million won or more, the higher the per capita disease cost in the region, and the higher the number of nursing institutions per 100,000 people, the higher the per capita disease cost. Policy tasks for each major target can be derived based on the results of regional socioeconomic disease burden analysis. Regional socioeconomic disease burden data are expected to highlight the importance of population group strategies, to establish the regional health statistics, and to lead the policy efforts to improve regional health levels. |
dc.description.tableOfContents | Abstract 1 요 약 3 제1장 서론 7 제1절 연구의 배경 및 목적 9 제2절 연구 내용 및 방법 13 제2장 사회경제적 질병부담 산출 방법론 고찰 15 제1절 산출 방법론 고찰 및 논의 17 제2절 국내 선행연구 고찰 42 제3장 지역 단위 질병부담 산출의 의의와 정책적 활용 49 제1절 사회경제적 질병부담 산출의 의의 51 제2절 주요국의 정책적 활용사례 54 제4장 지역별 사회경제적 질병부담 추이 77 제1절 분석방법 79 제2절 사회경제적 질병부담 추이 99 제5장 지역 변이와 영향요인 119 제1절 지역 변이 121 제2절 지역 변이 영향요인 136 제6장 결론 및 제언 159 제1절 주요 연구 결과 161 제2절 정책제언 166 참고문헌 177 |
dc.format | text/plain |
dc.format | text/html; charset=utf-8 |
dc.format | application/rdf+xml; charset=utf-8 |
dc.format.extent | 200 |
dc.language | kor |
dc.publisher | 한국보건사회연구원 |
dc.publisher | Korea Institute for Health and Social Affairs |
dc.rights | KOGL BY-NC-ND |
dc.rights.uri | https://creativecommons.org/licenses/by-nc-nd/2.0/kr/ |
dc.rights.uri | https://www.kogl.or.kr/info/licenseType4.do |
dc.title | 지역별 사회경제적 질병부담 추이와 영향요인 |
dc.title.alternative | Trends in the Regional Socio-economic Burden of Diseases and the Influencing Factors |
dc.type | Book |
dc.type.local | Report |
dc.subject.keyword | 질병부담 |
dc.subject.keyword | 질병비용 |
dc.subject.keyword | 건강결정요인 |
dc.subject.keyword | burden of disease |
dc.subject.keyword | burden |
dc.subject.keyword | cost-of-illness |
dc.subject.keyword | determinants of health |
dc.contributor.alternativeName | Go, Dun-Sol |
dc.contributor.alternativeName | Weng, Yueru |
dc.contributor.alternativeName | Bae, Jaeyong |
dc.contributor.alternativeName | Lee, Rugyeom |
dc.contributor.alternativeName | Jung, Yoon-Sun |
dc.contributor.alternativeName | Cheon, Ohbet |
dc.contributor.alternativeName | Kim, Young-Eun |
dc.type.other | 연구보고서 |
dc.identifier.doi | 10.23060/kihasa.a.2022.15 |
dc.identifier.localId | 연구보고서 2022-15 |
dc.identifier.localId | Research Monographs 2022-15 |
dc.citation.date | 2022 |
dc.citation.date | 2022 |
dc.citation.date | 2022 |
dc.citation.date | 2022 |
dc.date.dateaccepted | 2023-03-30T04:30:10Z |
dc.date.datesubmitted | 2023-03-30T04:30:10Z |
dc.type.research | 기초연구 |
dc.type.nkis | 기본연구보고서 |
dc.subject.nkisMain | J |
dc.subject.nkisMiddle | J3 |
dc.description.sponsorshipAwardNumber | P202200003_1_1 |
dc.type.project | 기본연구과제 |
dc.description.sponsorshipAwardTitle | [기본22-001-00]지역별 사회경제적 질병부담 추이와 영향요인 |
dc.subject.kihasa | 보건의료 서비스 |
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