Beyond boundaries: an operational intelligence framework using geospatial mapping for precision tuberculosis programmatic action in Tamil Nadu, India
Abstract
Tuberculosis (TB) programmatic outcomes are heavily influenced by heterogeneous disease distribution and localized variations in loss to follow-up (LFU). While routine electronic surveillance provides robust epidemiological totals, it may mask localized patterns of treatment attrition. Aim. To develop an operational geospatial intelligence framework using secondary surveillance data to identify and map LFU hotspots and establish a risk-stratified model for targeted programmatic prioritization. Materials and Methods. This retrospective operational research study analyzed anonymized treatment outcome records from TB patients ( n = 92,824) in Tamil Nadu, 2022. Treatment outcomes were aggregated across 234 tuberculosis units (TU) in 35 districts and integrated into a Geographic Information System framework. Spatial autocorrelation (Global Moran’s I), Getis-Ord Gi* hotspot analysis, and Kernel Density Estimation were applied to identify LFU clusters. Spatial outputs were integrated with core epidemiological indicators within a risk-stratification framework. Results. Spatial analysis demonstrated significant positive spatial autocorrelation (Global Moran’s I, p < 0.001) and marked heterogeneity in LFU across Tamil Nadu. Getis-Ord Gi* analysis identified statistically significant hotspots (95–99% confidence). District Tuberculosis Centre Vellore showed the highest LFU proportion (12.0%), with additional priority TU identified across Villupuram, Krishnagiri, Coimbatore, Perambalur, and Trichy. Villupuram contributed three hotspot TU. Overall, 8 TU (3.4%) as high-risk programme areas, providing a basis for more focused programme review and prioritisation. Conclusion. Geospatial analytics directly compliments routine TB surveillance by replacing uniform programmatic strategies with data-driven, geographically targeted actions. This framework offers a scalable, replicable model to improve treatment retention and accelerate TB elimination targets across high-burden settings.