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<p>Pulmonary tuberculosis (PTB) is a chronic infectious disease that remains a critical public health concern. This study aimed to investigate the spatial and temporal epidemiological characteristics of PTB in Fuzhou City, China. Joinpoint regression, spatial autocorrelation analysis, and a geographically and temporally weighted regression (GTWR) model were used to examine PTB incidence trends, spatial clustering, and associations with environmental variables. From 2014 to 2022, PTB incidence in Fuzhou City showed a significant decreasing trend, particularly during 2019–2022 (P &lt; 0.05). PTB incidence exhibited significant positive spatial autocorrelation. The number of high-high clusters decreased after 2019, while low-low clusters increased. The GTWR model revealed that PM2.5 was positively associated with PTB incidence across all counties of Fuzhou, whereas SO2 showed a negative association in the eastern part of the city. PTB incidence in Fuzhou showed an overall decreasing trend from 2014 to 2022, with significant spatial and temporal clustering. PM2.5 and SO2 were identified as associated environmental variables. Region-specific prevention and control strategies tailored to local clustering patterns and environmental factors should be formulate to achieve precise prevention and control of PTB in Fuzhou City. </p>

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Spatial-temporal analysis and influencing factors of pulmonary tuberculosis in Fuzhou, China from 2014 to 2022

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年8月3日

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  • Hewei Peng,
  • Shuo Yin,
  • Xiaoyan Zheng,
  • Hanwei Wang,
  • Shenggen Wu,
  • Danjing Chen,
  • Xiane Peng

摘要

Pulmonary tuberculosis (PTB) is a chronic infectious disease that remains a critical public health concern. This study aimed to investigate the spatial and temporal epidemiological characteristics of PTB in Fuzhou City, China. Joinpoint regression, spatial autocorrelation analysis, and a geographically and temporally weighted regression (GTWR) model were used to examine PTB incidence trends, spatial clustering, and associations with environmental variables. From 2014 to 2022, PTB incidence in Fuzhou City showed a significant decreasing trend, particularly during 2019–2022 (P < 0.05). PTB incidence exhibited significant positive spatial autocorrelation. The number of high-high clusters decreased after 2019, while low-low clusters increased. The GTWR model revealed that PM2.5 was positively associated with PTB incidence across all counties of Fuzhou, whereas SO2 showed a negative association in the eastern part of the city. PTB incidence in Fuzhou showed an overall decreasing trend from 2014 to 2022, with significant spatial and temporal clustering. PM2.5 and SO2 were identified as associated environmental variables. Region-specific prevention and control strategies tailored to local clustering patterns and environmental factors should be formulate to achieve precise prevention and control of PTB in Fuzhou City.