Scholarlink

Background <p>Pulmonary tuberculosis (PTB) remains a significant public health challenge in underdeveloped regions of western China. A critical, yet underutilized, approach to strengthening grassroots control lies in township-level Perspective. This study takes Zigong City, a prototypical prefectural-level city in Southwestern China, as a case study to elucidate the fine-scale spatiotemporal patterns, potential influencing factors, and future trends of PTB. The findings are intended to provide a transferable model for similar regions across Southwest China.</p> Methods <p>Utilizing township-level PTB case data from Zigong City (2005–2023), we performed an in-depth spatiotemporal investigation. Joinpoint regression was used to assess temporal trends. Spatial autocorrelation, spatiotemporal scan statistics, and emerging hot spot analysis were employed to identify high-risk clusters. The Spatial Durbin Model (SDM) was used to examine the associations and spatial spillover effects of environmental and socioeconomic variables. A Seasonal Autoregressive Integrated Moving Average (SARIMA) model was developed to forecast incidence.</p> Results <p>A total of 31,729 PTB cases were notified. The overall incidence showed a significant declining trend (Average Annual Percent Change: -2.54%). Spatiotemporal analysis not only identified one most likely and twelve secondary clusters but also revealed a consistent annual peak in incidence during March and April. Eight townships in eastern Zigong were consistently classified as persistent high-risk hot spots. According to the SDM, particulate matter with a diameter less than 2.5&#xa0;μm (PM₂.₅), ozone (O₃), precipitation, and relative humidity showed positive associations with local PTB risk. Conversely, negative associations were observed for higher gross domestic product (GDP) per capita, population density, night-time light (NTL), normalized difference vegetation index (NDVI), temperature, and sunshine duration. The SARIMA model projected a continued decline under the current trend, with incidence falling to 39.91 per 100,000 by 2030.</p> Conclusion <p>The analysis of Zigong City from 2005 to 2023 reveals that while PTB incidence is declining overall, it exhibits distinct spatiotemporal heterogeneity, characterized by persistent clustering in eastern townships and a consistent spring peak. Key environmental variables such as PM₂.₅, O₃, precipitation, and relative humidity were positively associated with PTB risk, whereas higher socioeconomic indicators and favorable natural conditions were associated with lower risk. The SARIMA model further forecasts a continued decline under current conditions, with incidence expected to fall to 39.91 per 100,000 by 2030. These findings underscore the importance of integrated, spatially-targeted interventions that address both environmental and socioeconomic determinants. By leveraging township-level spatiotemporal analysis and predictive modeling, this study provides an evidence-based and scalable framework to guide TB elimination efforts in southwestern China and other comparable settings.</p> Graphical Abstract <p></p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Spatiotemporal patterns, associated factors, and forecasting of pulmonary tuberculosis: a 19-year township-level case study in Zigong City, China

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月16日

查看全文
  • Qunzheng Mu,
  • Hong Cao,
  • Fengfeng Li,
  • Bin Wang,
  • Shirong Zhang,
  • Ruixin Luo,
  • Jingqi Liu,
  • Peiqu Liu,
  • Jianping Deng,
  • Chuan Wang

摘要

Background

Pulmonary tuberculosis (PTB) remains a significant public health challenge in underdeveloped regions of western China. A critical, yet underutilized, approach to strengthening grassroots control lies in township-level Perspective. This study takes Zigong City, a prototypical prefectural-level city in Southwestern China, as a case study to elucidate the fine-scale spatiotemporal patterns, potential influencing factors, and future trends of PTB. The findings are intended to provide a transferable model for similar regions across Southwest China.

Methods

Utilizing township-level PTB case data from Zigong City (2005–2023), we performed an in-depth spatiotemporal investigation. Joinpoint regression was used to assess temporal trends. Spatial autocorrelation, spatiotemporal scan statistics, and emerging hot spot analysis were employed to identify high-risk clusters. The Spatial Durbin Model (SDM) was used to examine the associations and spatial spillover effects of environmental and socioeconomic variables. A Seasonal Autoregressive Integrated Moving Average (SARIMA) model was developed to forecast incidence.

Results

A total of 31,729 PTB cases were notified. The overall incidence showed a significant declining trend (Average Annual Percent Change: -2.54%). Spatiotemporal analysis not only identified one most likely and twelve secondary clusters but also revealed a consistent annual peak in incidence during March and April. Eight townships in eastern Zigong were consistently classified as persistent high-risk hot spots. According to the SDM, particulate matter with a diameter less than 2.5 μm (PM₂.₅), ozone (O₃), precipitation, and relative humidity showed positive associations with local PTB risk. Conversely, negative associations were observed for higher gross domestic product (GDP) per capita, population density, night-time light (NTL), normalized difference vegetation index (NDVI), temperature, and sunshine duration. The SARIMA model projected a continued decline under the current trend, with incidence falling to 39.91 per 100,000 by 2030.

Conclusion

The analysis of Zigong City from 2005 to 2023 reveals that while PTB incidence is declining overall, it exhibits distinct spatiotemporal heterogeneity, characterized by persistent clustering in eastern townships and a consistent spring peak. Key environmental variables such as PM₂.₅, O₃, precipitation, and relative humidity were positively associated with PTB risk, whereas higher socioeconomic indicators and favorable natural conditions were associated with lower risk. The SARIMA model further forecasts a continued decline under current conditions, with incidence expected to fall to 39.91 per 100,000 by 2030. These findings underscore the importance of integrated, spatially-targeted interventions that address both environmental and socioeconomic determinants. By leveraging township-level spatiotemporal analysis and predictive modeling, this study provides an evidence-based and scalable framework to guide TB elimination efforts in southwestern China and other comparable settings.

Graphical Abstract