서울특별시 사망률 변이 및 관련 특성 분석 (Analysis of Related Factors and Regional Variation of Mortality in Seoul)

Background: Health is affected by various local factors. This study aims to investigate the age-standardized mortality variation of Seoul as well as the characteristics of the factors related to the mortality variation.
Methods: The Korea Community Health Survey data, Seoul Survey data, Seoul statistics, and e-regional indicators of the National Statistical Office were used. To investigate the basic boroughs standardized mortality variation in Seoul, external quotient, coefficient of variation (CV), and systematic component of variation (SCV) values were suggested; correlation analysis and multiple regression analysis were conducted to investigate the characteristics related to standardized mortality rate.
Results: The highest and the lowest standardized mortality rate of Seoul by boroughs had as much as 1.4 times difference; a low level of variation was shown in CV by 8.2; and was shown in SCV by 79. As a result of the multiple regression analysis of the factors that affect standardized mortality variation, the higher the rate of householders with college or higher, the lower the standardized mortality rate, and the higher the high-risk drinking rate, the higher the standardized mortality rate. Of the two, the rate of householder with a degree equivalent or higher than college was shown to have the biggest impact, followed by high-risk drinking rate.
Conclusion: We found a variation in age-standardized mortality rate of boroughs in Seoul. The results suggest that policy makers should take into account socioeconomic environmental characteristics of community in developing community-based health promotion rather than focusing on lifestyle changes of residents.

Background: Health is affected by various local factors. This study aims to investigate the age-standardized mortality variation of Seoul as well as the characteristics of the factors related to the mortality variation.
Methods: The Korea Community Health Survey data, Seoul Survey data, Seoul statistics, and e-regional indicators of the National Statistical Office were used. To investigate the basic boroughs standardized mortality variation in Seoul, external quotient, coefficient of variation (CV), and systematic component of variation (SCV) values were suggested; correlation analysis and multiple regression analysis were conducted to investigate the characteristics related to standardized mortality rate.
Results: The highest and the lowest standardized mortality rate of Seoul by boroughs had as much as 1.4 times difference; a low level of variation was shown in CV by 8.2; and was shown in SCV by 79. As a result of the multiple regression analysis of the factors that affect standardized mortality variation, the higher the rate of householders with college or higher, the lower the standardized mortality rate, and the higher the high-risk drinking rate, the higher the standardized mortality rate. Of the two, the rate of householder with a degree equivalent or higher than college was shown to have the biggest impact, followed by high-risk drinking rate.
Conclusion: We found a variation in age-standardized mortality rate of boroughs in Seoul. The results suggest that policy makers should take into account socioeconomic environmental characteristics of community in developing community-based health promotion rather than focusing on lifestyle changes of residents.