Facility-Level Mutation Fingerprints and Early-Warning Surveillance of Drug-Resistant Mycobacterium tuberculosis in Rural Eastern Cape

Background Routine molecular tuberculosis (TB) diagnostics generate high-dimensional "off-protocol" data, including mutant melt peak temperatures and cycle threshold (CT) values. These data are currently underutilized, typically discarded after individual resistance reporting. We aimed to evaluate whether aggregating these routine "mutation-proxy" signals could provide a scalable framework for facility-level surveillance and early warning of emerging drug resistance. Methods We conducted a retrospective longitudinal study of 4,300 TB diagnostic episodes across 139 health-care facilities over 16 quarters. Mutation-proxy signals for five key loci ( katG, inhA, gyrA, rrs, eis ) were extracted from raw diagnostic outputs. We constructed "facility-level mutation fingerprints" by aggregating prevalence data and employed hierarchical clustering to identify distinct resistance topographies. Associations between proxies and laboratory-confirmed resistance were modelled using L2-regularized (ridge) logistic regression with facility-level cluster bootstrap confidence intervals to account for near-separation and spatial autocorrelation. Results Isoniazid-associated proxies predominated ( katG : 46.3%; inhA : 25.1%), while gyrA (fluoroquinolone-associated) and rrs (injectable-associated) proxies were detected in 12.5% and 7.7% of episodes, respectively. Clustering revealed four distinct facility profiles: katG -dominant, inhA -dominant, mixed-isoniazid, and a high-risk "emerging gyrA " profile. Regression analysis confirmed high diagnostic accuracy for the proxies, notably for isoniazid (katG: OR = 1,146; inhA: OR = 603) and fluoroquinolones (gyrA: OR = 7,136). Longitudinal analysis successfully identified a subset of facilities that exhibited significant quarter-over-quarter increases in second-line resistance proxies prior to traditional surveillance detection. Conclusion Facility-level mutation fingerprinting leverages existing, "near-zero-cost" laboratory data to provide a granular, real-time map of the resistance landscape. This framework enables precision public health interventions, allowing TB programmes to transition from reactive to proactive, facility-targeted containment of emerging drug-resistant Mycobacterium tuberculosis .