BMC public health · 2025
Spatiotemporal distribution and detection of spatial clusters of tuberculosis in Hubei Province, China using FleXScan (2017-2023).
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Wang Lina, Li Yaru, Chen Ji, Zhang Danfei, Zhang Zhengbin, Li Xiang
Аннотация
BACKGROUND: This study investigates the spatiotemporal distribution and spatial clustering of tuberculosis(TB) in 103 counties of Hubei Province, China, using spatial scan statistics. By identifying high-risk areas and temporal trends, the findings will provide a scientific foundation for targeted TB prevention and control strategies.
METHODS: This study employed the FleXScan method to detect spatial clusters of pulmonary tuberculosis cases in Hubei Province and identify statistically significant high-risk areas. Combined with Geographic Information System (GIS) spatial analysis techniques, we visualized the spatiotemporal distribution patterns and dynamic changes of these high-risk tuberculosis clusters.
RESULTS: Between 2017 and 2023, the incidence rate of Hubei Province decreased from 68.28 to 54.54 per 100,000 population. Using the FleXScan method, significant spatial clustering of TB cases was identified. The most likely clusters (MLCs) were primarily located in the western and southwestern regions, including Enshi Prefecture, the Shennongjia Forestry District, and parts of Yichang City. Notably, Enshi Prefecture maintained a persistently high average annual incidence of 110.78 per 100,000 with no significant temporal decline, highlighting the urgent need for targeted prevention and control measures.
CONCLUSION: TB in Hubei Province exhibits significant spatiotemporal heterogeneity. Its epidemiology is influenced by multiple factors, including economic conditions, geographical environment, healthcare access, and social determinants. Control strategies should take into account differences both between regions and within individual regions to accurately identify high-risk areas.
METHODS: This study employed the FleXScan method to detect spatial clusters of pulmonary tuberculosis cases in Hubei Province and identify statistically significant high-risk areas. Combined with Geographic Information System (GIS) spatial analysis techniques, we visualized the spatiotemporal distribution patterns and dynamic changes of these high-risk tuberculosis clusters.
RESULTS: Between 2017 and 2023, the incidence rate of Hubei Province decreased from 68.28 to 54.54 per 100,000 population. Using the FleXScan method, significant spatial clustering of TB cases was identified. The most likely clusters (MLCs) were primarily located in the western and southwestern regions, including Enshi Prefecture, the Shennongjia Forestry District, and parts of Yichang City. Notably, Enshi Prefecture maintained a persistently high average annual incidence of 110.78 per 100,000 with no significant temporal decline, highlighting the urgent need for targeted prevention and control measures.
CONCLUSION: TB in Hubei Province exhibits significant spatiotemporal heterogeneity. Its epidemiology is influenced by multiple factors, including economic conditions, geographical environment, healthcare access, and social determinants. Control strategies should take into account differences both between regions and within individual regions to accurately identify high-risk areas.