Spatially Filtered Multilevel Analysis on Spatial Inequality of Tuberculosis in Gyeongsangbuk-do, Korea

Abstract

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.

Evaluation of the accessibility and its equity of the national public-private mix program for tuberculosis in Korea: a multilevel analysis

“…In addition, when the PPM program engaged HCPs or had programs to provide services in poor areas, the equity of access increased [ 26 ]. These are crucial evidence for “engaging all HCPs.” In addition to these policies, Korean NTP should consider introducing an approach for utilizing local data [ 27 ] or control efforts tailored to local conditions [ 10 - 13 ], such as cohort review that allows for systematic monitoring of courses and outcome indicators within the area. Without NTP’s additional control efforts for the social determinant of TB, the PPM program could lead to the unintended widening of health inequalities [ 28 , 29 ].…”

“…다수준 분석은 개인과 지역 수준 요인의 영향 여부와 교호작용을 파악할 수 있어서, 감염병 연구에서도 중요성이 크다 [9]. 결핵 발생과 신고 [8,10], 전파 [11], 진단과 치료에 대한 지침준수 [12]와 공간적 분포 특성 [13]과 다제내성 결핵의 분포 [14] And optionally, comparisons or contrasts among them. In order to express the interaction between a continuous variable and two categorical variables, Table 3 expresses the interaction of categorical variables(left), and presents the slope of deprivation at the determined categorical variables and deprivation value (0) (right).…”

“…Multilevel analysis is of great importance in infectious disease research because it can determine whether individuals and area-level characteristics have an effect and interactions [ 9 ]. The researchers conducted multilevel studies on TB incidence and reporting [ 8 , 10 ], transmission [ 11 ], adherence to guidelines for diagnosis and treatment [ 12 ], spatial distribution patterns [ 13 ], and distribution of multiple drug resistance (MDR) TB [ 14 ]. The material and collective social function of the area determines the distribution of HCPs and the accessibility to TB HCPs, and public health functions such as the TB control program of the public health center (PHC) influence treatment support as a collective social function of the area [ 15 ].…”

Local Characteristics Related to SARS-CoV-2 Transmissions in the Seoul Metropolitan Area, South Korea

“…Low-income households are populations that are exposed to high SARS-CoV-2 infection risks [ 51 , 52 ]. Low economic status is a risk factor for infectious disease transmission [ 27 , 28 , 53 , 54 , 66 ]. Naturally, in Korea, low economic status is related to an increased risk of SARS-CoV-2 infection [ 50 ].…”

“…Local spatial regression models provide spatially varying coefficients by applying region-specific information [ 23 , 24 ]. Previous research, which adopted global spatial models, only provided generalized conclusions that did not take into account region-specific information [ 25 , 26 , 27 , 28 , 29 ]. Such a global model, in turn, would mislead public health policy makers [ 30 ].…”