China ranks third globally in tuberculosis burden and is classified as one of the highest tuberculosis (TB) burden countries. Xinjiang, a multi-ethnic region in northwestern China, has consistently reported one of the highest TB incidence rates nationwide, posing significant challenges to Chinese TB prevention and control efforts. Therefore, investigating region-specific epidemiological characteristics, quantifying the effects of covariates on pulmonary tuberculosis (PTB) risk, and deriving robust conclusions aligned with Xinjiang's sociodemographic and environmental contexts is critical to formulating targeted, evidence-based strategies for pulmonary tuberculosis control, thereby providing actionable scientific recommendations to mitigate the disease burden in this region.

A hierarchical Bayesian spatiotemporal model integrated with the Integrated Nested Laplace Approximations-Stochastic Partial Differential Equations (INLA-SPDE) acceleration algorithm was employed to analyze the spatiotemporal distribution patterns and associated influencing factors of PTB in Xinjiang.

Data on PTB reported incidence counts and population demographics across 14 prefectures in Xinjiang from 2010 to 2019 were retrieved from the China Disease Prevention and Control Information System (CDCIS). An exploratory statistical analysis was conducted to characterize the spatiotemporal distribution of PTB incidence. Subsequently, a hierarchical Bayesian spatiotemporal model integrated with the INLA-SPDE acceleration algorithm was developed to assess the epidemiological trends and quantify the impact of associated risk factors on PTB transmission dynamics during the study period.

The study area encompassed 14 prefectures in Xinjiang, categorized into 10 estimation sites (Urumqi City, Changji Hui Autonomous Prefecture, Karamay City, Hami City, Ili Kazakh Autonomous Prefecture, Bayingolin Mongol Autonomous Prefecture, Kashgar Prefecture, Tacheng Prefecture, Aksu Prefecture and Altay Prefecture) and 4 validation sites (Turpan City, Bortala Mongol Autonomous Prefecture, Kizilsu Kirghiz Autonomous Prefecture and Hotan Prefecture). From 2010 to 2019, the incidence rate and reported case count of PTB in Xinjiang peaked in 2018 (304.945 per 100 000 population, 76 846 cases), followed by a declining trend thereafter. The Bayesian posterior estimates indicated that population size, mean temperature, PM_2.5 concentration, and longitude were positively associated with tuberculosis incidence, whereas GDP per capita, hospital bed availability, and latitude demonstrated protective effects against disease incidence. Spatial autocorrelation analysis revealed significant geographic clustering of PTB cases (σ²_ω=1.806), with a spatial correlation range of 946.053 km, indicating that spatial dependence diminished with increasing distance between sites. Furthermore, short-term temporal persistence of PTB infection rates was strongly supported (α=17.926), suggesting sustained transmission dynamics within localized regions.

Significant spatial autocorrelation of PTB incidence was observed in Xinjiang from 2010 to 2019, with a spatial correlation range of 946.053 km, indicating that spatial dependence diminished with increasing distance between geographic locations. Furthermore, population density, mean annual temperature, PM_2.5 concentration, and longitude exhibited positive associations with PTB incidence rates. In contrast, GDP per capita, healthcare bed availability, and latitude were identified as protective factors, demonstrating inverse associations with PTB risk.