Raw data.

Objective
This study aimed to enhance the prevention and control of pulmonary tuberculosis (PTB) and provide more effective and accurate methods in Changshu City.
Methods
The PTB patients’ information came from the China Information System for Disease Control and Prevention (CISDCP). The demographic data for Changshu city and towns came from the Suzhou Statistical Yearbook and the LandScan platform. ArcGIS was used for global spatial autocorrelation analysis and local spatial autocorrelation analysis. Univariate logistic regression and multivariate logistic regression were used to analyze the influencing factors of cured PTB patients. The receiver operating characteristic (ROC) curve and decision curve analysis (DCA) were used to analyze the predictive efficacy and clinical benefit of the indicators. XGBoost analysis was performed to explore the feature importance of key metrics for PTB outcome.
Results
A total of 3943 PTB cases were included. The annual incidence rate of new PTB in Changshu city was 27.081 per 100,000. Changshu High-tech Industrial Development Zone in Jiangsu Province and Shajiabang town were the high-high aggregation areas and hot spot areas. Diagnosis delay, TB strain types, and drug sensitivity were independent predictors of the cure of new PTB patients.
Conclusion
The central and southern areas of Changshu were the high-high cluster areas and hot spots for PTB. Shorter diagnosis delay days and mycobacterium tuberculosis (MTB) promote the cure of tuberculosis, while drug sensitivity was a risk factor for its cure.