DefinePK

Abstract

This population-based molecular epidemiological study addresses the formidable challenge of drug-resistant tuberculosis (DR-TB) in northeastern India, where complex epidemiological patterns are inadequately characterized. Amidst critically limited regional molecular data that hinders evidence-based intervention planning, we studied 108 tuberculosis patients (median age 40 years, 69.4% male) across 11 primary healthcare facilities between 2022 and 2025. Molecular characterization was performed using WHO-recommended GenoType MTBDRplus and MTBDRsl line probe assays with resistance gene sequencing. Modified compartmental dynamics with Monte Carlo uncertainty analysis were used in mathematical models to predict five-year epidemic trajectories under various intervention scenarios. Our results revealed an unprecedented regional burden, with rifampicin resistance reaching 96.3% (95% CI: 91.8-99.1%). Multidrug-resistant tuberculosis (MDR-TB) affected 25.0% (95% CI: 17.1-34.2%) of cases, and pre-extensively drug-resistant tuberculosis (pre-XDR-TB) was found in 2.8% of cases. Geospatial analysis showed significant clustering (p=0.002), indicating epidemic transmission. Predictive modeling demonstrated a potential catastrophic expansion to 6,440 cases by 2030 under current conditions, versus a 90% reduction achievable with comprehensive interventions. Furthermore, economic analysis revealed a favorable 5.3:1 return on investment for developing tertiary care infrastructure. In conclusion, this study documents an extraordinary burden of DR-TB characterized by near-universal rifampicin resistance, necessitating an immediate emergency response. The mathematical modeling provides an evidence-based justification for substantial investment in regional tuberculosis control infrastructure, supported by contemporary treatment and diagnostic advances