군집분석을 활용한 지역별 건강격차 연구: 주관적 건강수준을 중심으로

• Published in: Health policy and management Vol. 33; no. 2; pp. 118 - 128
• Main Authors: 허민희, Min-hee Heo, 백세종, Sei-jong Baek, 김영진, Young-jin Kim, 노진원, Jin-won Noh
• Format: Journal Article
• Language: Korean
• Published: 한국보건행정학회 30.06.2023
• Subjects: Cluster analysis; Health inequalities; Individual factor; Regional factor; Self-rated health; Cluster analysis; Self-rated health; Individual factor; Regional factor; Health inequalities
• ISSN: 1225-4266

Background: Personal socio-economic abilities are crucial as it affects health inequalities. These multidimensional inequalities across the regions have been structured and fixed. This study aimed to analyze health vulnerabilities by regional cluster and identify regional health disparities of self-rated health, using nationally representative cross-sectional data. Methods: This study used personal and regional data. Data from the Community Health Survey 2021 were analyzed. K-means cluster analysis was applied to 250 si-gun-gu using administrative regional data. The clusters were based on three areas: physical environment, health-related behaviors and biological factors, and the psychosocial environment through the conceptual framework for action on the social determinants of health. And binary logistic regression analyses were conducted to examine the differences in self-rated health status by the regional clusters, controlling human biology, environment, lifestyle, and healthcare organization factors. Results: The most vulnerable group was group 3, the moderate vulnerable group was group 1, and the least vulnerable group was group 2. The group 2 was more likely to have high self-rated health status than the moderate vulnerable group (odds ratio [OR], 1.023; p<0.001). And the group 3 showed low self-rated health status than the moderate vulnerable group (OR, 0.775; p<0.001). However, the moderate vulnerable group had significantly higher self-rated health status than the most vulnerable group (group 2: OR, 1.023; p<0.001; group 3: OR, 0.775; p<0.001). Conclusion: These results demonstrate that community members’ health status is influenced by regional determinants of health and individual levels. And these contribute to understanding the importance of specific and differentiated interventions like locally tailored support programs considering both individual and regional health determinants.