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

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.