호남권 쯔쯔가무시증 발생률과 지역 특성과의 관련성: 공간 상관성 분석*
Association Between Scrub Typhus Incidence and Regional Characteristics in the Honam Region; Focusing on Spatial Correlation
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= Abstract = Objectives: This study analyzed the spatial autocorrelation of scrub typhus incidence rates in 43 cities and counties in the Honam region. Spatial regression models were applied to identify regional characteristics associated with disease incidence. Methods: An ecological study design, collecting scrub typhus incidence rates and region-specific characteristics based on a multifactor convergence model from 2020 to 2023. To assess the spatial autocorrelation of the regional scrub typhus incidence rates, Moran’s I and LISA cluster maps were generated. Spatial regression models-including the ordinary least squares (OLS), spatial lag model (SLM) and spatial error model (SEM)-were applied to identify related factors. Results: Between 2020 to 2023, the average scrub typhus incidence rate in the Honam region was 64.9 cases per 100,000 population. Spatial autocorrelation analysis revealed a statistically significant Moran's I of 0.625. LISA cluster maps identified five high-high (HH), five low-low (LL), and one high-low (HL) spatial clusters. Among the spatial regression models, the OLS model provided the best fit, explaining 87.4% of variance with significant predictors including obesity rate, prevalence of physician-diagnosed hypertension, population density, proportion of elderly population, green area, forest area, fiscal autonomy ratio, and medical aid population ratio. After controlling for these variables, the residual spatial autocorrelation of scrub typhus incidence rates (Moran's I=0.021, p>0.05) was not statistically significant. Conclusions: The findings suggest that regions with high scrub typhus incidence are associated with host immune status, demographic profiles, socioeconomic factors related to poverty, and extensive unmanaged habitats for vector. Effective scrub typhus control policies should prioritize targeted intervention in high-risk areas, enhance disease surveillance and vector management, and ensure equitable access to timely diagnosis and treatment.
Abstract
= Abstract = Objectives: This study analyzed the spatial autocorrelation of scrub typhus incidence rates in 43 cities and counties in the Honam region. Spatial regression models were applied to identify regional characteristics associated with disease incidence. Methods: An ecological study design, collecting scrub typhus incidence rates and region-specific characteristics based on a multifactor convergence model from 2020 to 2023. To assess the spatial autocorrelation of the regional scrub typhus incidence rates, Moran’s I and LISA cluster maps were generated. Spatial regression models-including the ordinary least squares (OLS), spatial lag model (SLM) and spatial error model (SEM)-were applied to identify related factors. Results: Between 2020 to 2023, the average scrub typhus incidence rate in the Honam region was 64.9 cases per 100,000 population. Spatial autocorrelation analysis revealed a statistically significant Moran's I of 0.625. LISA cluster maps identified five high-high (HH), five low-low (LL), and one high-low (HL) spatial clusters. Among the spatial regression models, the OLS model provided the best fit, explaining 87.4% of variance with significant predictors including obesity rate, prevalence of physician-diagnosed hypertension, population density, proportion of elderly population, green area, forest area, fiscal autonomy ratio, and medical aid population ratio. After controlling for these variables, the residual spatial autocorrelation of scrub typhus incidence rates (Moran's I=0.021, p>0.05) was not statistically significant. Conclusions: The findings suggest that regions with high scrub typhus incidence are associated with host immune status, demographic profiles, socioeconomic factors related to poverty, and extensive unmanaged habitats for vector. Effective scrub typhus control policies should prioritize targeted intervention in high-risk areas, enhance disease surveillance and vector management, and ensure equitable access to timely diagnosis and treatment.