국내 중증열성혈소판감소증후군의 시공간적 집적성 분석 및 기후·환경요인과의 관련성 (2013년~2023년)
Spatiotemporal Clustering Analysis of Severe Fever with Thrombocytopenia Syndrome in Korea and Association with Climatic and Environmental Factors (2013-2023)
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Objectives: This study aimed to identify areas and periods of high incidence of severe fever with thrombocytopenia syndrome (SFTS) in Korea through spatiotemporal analysis and association with climatic and environmental factors. Methods: Clusters of cities, counties, and districts were identified using spatiotemporal clustering analysis for each year from 2013 to 2023. Risk factors affecting the clusters, including monthly climatic and environmental factors for each area, were analyzed through generalized estimating equations. Results: From 2013 to 2023, a total of 68 spatiotemporal clusters were identified, with an average diameter of 34.16 kilometers and an average duration of 4.1 months. Most clusters were identified between July and October, with an average of 6.8 cities, counties, or districts per cluster. The analysis of risk factors affecting clusters showed that mean temperature, forest coverage rate, and average forest growing stock were associated with increased risk, while mean wind speed was associated with decreased risk. Conclusions: As there are currently no vaccines or specific therapeutics available for SFTS, the best prevention strategy is to avoid tick bites and ensure early diagnosis. Therefore, it is essential to continue promoting prevention policies and initiatives targeting people in high-risk areas during high-risk periods.
Abstract
Objectives: This study aimed to identify areas and periods of high incidence of severe fever with thrombocytopenia syndrome (SFTS) in Korea through spatiotemporal analysis and association with climatic and environmental factors. Methods: Clusters of cities, counties, and districts were identified using spatiotemporal clustering analysis for each year from 2013 to 2023. Risk factors affecting the clusters, including monthly climatic and environmental factors for each area, were analyzed through generalized estimating equations. Results: From 2013 to 2023, a total of 68 spatiotemporal clusters were identified, with an average diameter of 34.16 kilometers and an average duration of 4.1 months. Most clusters were identified between July and October, with an average of 6.8 cities, counties, or districts per cluster. The analysis of risk factors affecting clusters showed that mean temperature, forest coverage rate, and average forest growing stock were associated with increased risk, while mean wind speed was associated with decreased risk. Conclusions: As there are currently no vaccines or specific therapeutics available for SFTS, the best prevention strategy is to avoid tick bites and ensure early diagnosis. Therefore, it is essential to continue promoting prevention policies and initiatives targeting people in high-risk areas during high-risk periods.
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