Trends in the Regional Socio-economic Burden of Diseases and the Influencing Factors

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.