Tuberculosis (TB) incidence and corresponding mortality rates in S. Korea are unusual and unique compared to other economically developed countries. Korea has the highest TB incidence rate in Organization for Economic Co-operation and Development (OECD) countries. TB is known as a disease reflecting socio-economic and environmental conditions of a society. Besides, TB is an infectious disease spread through the air, naturally forming spatial dependence of its incidence. This study investigates TB incidences in Korea in socio-economic and environmental perspectives. Eigenvector spatial filtering applied accounts for spatial autocorrelation in the TB incidence, and Getis-Ord Gi* statistic tracks the changes of TB clusters at given time. The results show that population composition ratio, population growth rate, health insurance payment, and public health variables are significant throughout the study period. Environmental variables make minor effects on TB incidence. This study argues that unique demographic features of Korea are a potential threat to TB control in the future.

Objectives: This study aims to identify the spatial inequality of tuberculosis (TB) and related socio-environmental factors in Gyeongsangbuk-do, Korea. Methods: This study explains TB incidences in a spatial perspective. A spatial multilevel regression model was applied to account for the spatial inequality of TB and related factors. Applied socio-environmental factors were average age, population density, Normalized Difference Built-up Index (NDBI), particulate matter (PM10), medical insurance, financial independence rate, and medical doctors per 1,000 people. Results: This study finds evident spatial inequality of TB in Gyeongsangbuk-do. Most TB hotspots are identified around Yeongyang-gun, the area with the lowest socio-economic status in Korea. The regression result shows that level 1 factors (Average age, NDBI, PM10) and level 2 factors (medical insurance, financial independence rate) were statistically significant related to TB incidences. Furthermore, spatial filtering applied to multilevel regression accounts for spatial autocorrelation of TB incidence. Conclusions: Regional socio-economic and demographic conditions in Gyeongsangbuk-do influence TB incidences. Given low expectations on Gyeongsangbuk-do's socio-economic and demographic growth, reducing the spatial inequality of TB would be challenging.