A Systematic Review of Spatial Analysis Techniques Used in Tuberculosis Prevalence

Background Tuberculosis (TB) continues to be a significant global health challenge. This systematic review examined the application of spatial analysis techniques in understanding TB prevalence. Methods We searched five databases (PubMed, Embase, Web of Science, Scopus, and CABI) from January 2010–31 August 2024. Full reproducible search strings are provided in Appendix S1. Thirty‐two studies met the inclusion criteria. Geographic Information Systems, spatial autocorrelation, and Geographically Weighted Regression were the main methods identified. Due to high heterogeneity in spatial scales and outcome definitions, we performed a narrative synthesis without meta‐analysis. Results Spatial techniques identified TB hotspots linked to high population density, poor socioeconomic conditions, and specific climatic factors. In South Asia (India, Nepal, Bangladesh, and Pakistan), reported prevalence rates ranged from 180 to 260 per 100,000; these values are the minimum and maximum across individual studies, not pooled estimates. Fourteen studies reported significant associations with poverty, nine with population density. The COVID‐19 pandemic disrupted TB services in 10 high‐burden countries. Limitations include substantial heterogeneity in spatial scales and risk of bias in 19% of studies (rated high risk using the Newcastle–Ottawa Scale). Conclusions Targeted, location‐specific interventions are needed. Future research should standardize spatial methods and integrate genomic and environmental data.