Unprecedented Molecular Epidemiology of Extreme Drug-Resistant Tuberculosis with Predictive Modeling Analysis: A Comprehensive Investigation in Northeastern India

Authors

  • Harikumar Pallathadka
  • Parag Deb Roy
  • Bipul Chandra Deka
  • Deba Kumar Mishra
  • Jayshree Saha
  • Rita Sarkar
  • Minkon Roy
  • Mahitosh Banerjee

Keywords:

Drug-resistant tuberculosis, molecular epidemiology, mathematical modeling, line probe assay, northeastern India

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

Downloads

Download data is not yet available.

References

World Health Organization. Tuberculosis resurges as top infectious disease killer. Geneva: WHO; 2024. Available from: https://www.who.int/news/item/29-10-2024-tuberculosis-resurges-as-top-infectious-disease-killer

Dheda K, Gumbo T, Maartens G, et al. The epidemiology, pathogenesis, transmission, diagnosis, and management of multidrug-resistant, extensively drug-resistant, and incurable tuberculosis. Lancet Respir Med. 2017;5(4):291-360.

Zignol M, Dean AS, Falzon D, et al. Twenty years of global surveillance of antituberculosis-drug resistance. N Engl J Med. 2016;375(11):1081-9.

World Health Organization. The End TB Strategy. Geneva: WHO; 2024. Available from: https://www.who.int/teams/global-programme-on-tuberculosis-and-lung-health/the-end-tb-strategy

Floyd K, Glaziou P, Zumla A, Raviglione M. The global tuberculosis epidemic and progress in care, prevention, and research: an overview in year 3 of the End TB era. Lancet Respir Med. 2018;6(4):299-314.

Central TB Division, Ministry of Health and Family Welfare. India TB Report 2023. New Delhi: Government of India; 2023.

Pai M, Behr MA, Dowdy D, et al. Tuberculosis. Nat Rev Dis Primers. 2016;2:16076.

Sharma SK, Kumar S, Saha PK, et al. Prevalence of multidrug-resistant tuberculosis among category II pulmonary tuberculosis patients. Indian J Med Res. 2011;133(3):312-15.

Muniyandi M, Ramachandran R, Gopi PG, et al. The prevalence of tuberculosis in different economic strata: a community survey from South India. Int J Tuberc Lung Dis. 2007;11(9):1042-5.

Baruah BK, Borah P, Saikia L. Drug resistance pattern of Mycobacterium tuberculosis in retreatment cases in Assam. J Assoc Physicians India. 2019;67(8):45-48.

Central TB Division. National Drug Resistance Survey Report 2019-20. New Delhi: Ministry of Health and Family Welfare; 2020.

Singh AK, Maurya AK, Kant S, et al. Molecular epidemiology of tuberculosis: Opportunities & challenges in disease control. Indian J Med Res. 2017;146(2):181-8.

World Health Organization. WHO launches new guidance on the use of targeted next-generation sequencing tests for the diagnosis of drug-resistant TB and a new sequencing portal. Geneva: WHO; 2024. Available from: https://www.who.int/news/item/20-03-2024-who-launches-new-guidance-on-the-use-of-targeted-next-generation-sequencing-tests-for-the-diagnosis-of-drug-resistant-tb-and-a-new-sequencing-portal

Miotto P, Tessema B, Tagliani E, et al. Targeted next-generation sequencing to diagnose drug-resistant tuberculosis: a systematic review and meta-analysis. Lancet Infect Dis. 2024;24(7):e449-e460.

Centers for Disease Control and Prevention. Tuberculosis Whole-Genome Sequencing. Atlanta: CDC; 2024. Available from: https://www.cdc.gov/tb/php/genotyping/whole-genome-sequencing.html

Ochieng D, Wanjuki I, Nyabadza F. Mathematical Modeling of Tuberculosis Transmission Dynamics With Reinfection and Optimal Control. Eng Rep. 2025;7(1):e13068.

Agusto FB, Marcus N, Okosun KO. Application of optimal control to the epidemiology of malaria. Electron J Differ Equ. 2012;2012(81):1-22.

Conradie F, Diacon AH, Ngubane N, et al. Treatment of highly drug-resistant pulmonary tuberculosis. N Engl J Med. 2020;382(10):893-902.

Ndjeka N, Conradie F, Schnippel K, et al. Treatment of drug-resistant tuberculosis with bedaquiline in a high HIV prevalence setting: an interim cohort analysis. Int J Tuberc Lung Dis. 2015;19(8):979-85.

Hatami H, Sotgiu G, Brust JC, et al. Oral Regimens for Rifampin-Resistant, Fluoroquinolone-Susceptible Tuberculosis. N Engl J Med. 2024;391(14):1285-97.

Andries K, Verhasselt P, Guillemont J, et al. A diarylquinoline drug active on the ATP synthase of Mycobacterium tuberculosis. Science. 2005;307(5707):223-7.

Villellas C, Coeck N, Meehan CJ, et al. Unexpected high prevalence of resistance-associated Rv0678 variants in MDR-TB patients without documented prior bedaquiline exposure. J Antimicrob Chemother. 2017;72(3):684-90.

Yang Y, Zhang C, Wang S, et al. A comprehensive study on tuberculosis prediction models: Integrating machine learning into epidemiological analysis. J Theor Biol. 2024;578:111681.

Chen X, Li M, Wang J, et al. Machine learning algorithms to predict treatment success for patients with pulmonary tuberculosis. PLoS One. 2024;19(8):e0309151.

Yang S, Wang L, Zhang M, et al. Circulating cell-free RNA in blood as a host response biomarker for detection of tuberculosis. Nat Commun. 2024;15(1):4523.

World Health Organization. Global Tuberculosis Report 2024. Geneva: WHO; 2024. Available from: https://www.who.int/teams/global-programme-on-tuberculosis-and-lung-health/tb-reports/global-tuberculosis-report-2024

Bagcchi S. Insights from the 2024 WHO Global Tuberculosis Report – More Comprehensive Action, Innovation, and Investments required for achieving WHO End TB goals. Int J Infect Dis. 2024;149:107238.

Zhang L, Cheng Y, Wang X, et al. Decoding the WHO Global Tuberculosis Report 2024: A Critical Analysis of Global and Chinese Key Data. Zoonoses. 2024;4(2):20240061.

Telenti A, Imboden P, Marchesi F, et al. Detection of rifampicin-resistance mutations in Mycobacterium tuberculosis. Lancet. 1993;341(8846):647-50.

Cohen T, Sommers B, Murray M. The effect of drug resistance on the fitness of Mycobacterium tuberculosis. Lancet Infect Dis. 2003;3(1):13-21.

World Health Organization. Catalogue of mutations in Mycobacterium tuberculosis complex and their association with drug resistance, 2nd ed. Geneva: WHO; 2023. Available from: https://www.who.int/publications/i/item/9789240082410

Wang L, Zhang Y, Chen H, et al. Prediction of tuberculosis using an automated machine learning platform for models trained on synthetic data. Comput Struct Biotechnol J. 2022;20:2473-85.

Pienaar E, Fluitt A, Whitney SE, et al. Mathematical modeling suggests heterogeneous replication of Mycobacterium tuberculosis in rabbits. PLoS Comput Biol. 2024;20(11):e1012563.

Briggs A, Sculpher M, Claxton K. Decision Modelling for Health Economic Evaluation. Oxford: Oxford University Press; 2006.

Phelan JE, O'Sullivan DM, Machado D, et al. GenTB: A user-friendly genome-based predictor for tuberculosis resistance powered by machine learning. Genome Med. 2021;13(1):138.

Kulldorff M. A spatial scan statistic. Commun Stat Theory Methods. 1997;26(6):1481-96.

Isaakidis P, Casas EC, Das M, et al. Impact and cost-effectiveness of the 6-month BPaLM regimen for rifampicin-resistant tuberculosis in Moldova: A mathematical modeling analysis. PLoS Med. 2024;21(5):e1004401.

Tupasi TE, Garfin AM, Mangan JM, et al. Cost and cost-effectiveness of BPaL regimen used in drug-resistant TB treatment in the Philippines. Trop Med Infect Dis. 2024;9(7):148.

Fuady A, Houweling TA, Mansyur M, et al. A systematic review and meta-analysis of the catastrophic costs incurred by tuberculosis patients. Sci Rep. 2022;12(1):558.

Raviglione M, Marais B, Floyd K, et al. Scaling up interventions to achieve global tuberculosis control: progress and new developments. Lancet. 2012;379(9829):1902-13.

Zishiri VK, Lesosky M, Mudavanhu M, et al. Cost-effectiveness of targeted next-generation sequencing (tNGS) for detection of tuberculosis drug resistance in India, South Africa and Georgia: a modeling analysis. eClinicalMedicine. 2024;78:102924.

Vassall A, van Kampen S, Sohn H, et al. Rapid diagnosis of tuberculosis with the Xpert MTB/RIF assay in high burden countries: a cost-effectiveness analysis. PLoS Med. 2011;8(11):e1001120.

Nathavitharana RR, Cudahy PG, Schumacher SG, et al. Accuracy of line probe assays for the diagnosis of pulmonary and extrapulmonary tuberculosis: a systematic review and meta-analysis. BMJ Open. 2017;7(1):e014216.

Rufai SB, Kumar P, Singh A, et al. Comparison of Xpert MTB/RIF with line probe assay for detection of rifampin-monoresistant Mycobacterium tuberculosis. J Clin Microbiol. 2014;52(5):1846-52.

World Health Organization. Definitions and reporting framework for tuberculosis -- 2013 revision. Geneva: WHO; 2013.

Husereau D, Drummond M, Augustovski F, et al. Consolidated Health Economic Evaluation Reporting Standards 2022 (CHEERS 2022) statement: updated reporting guidance for health economic evaluations. Value Health. 2022;25(1):3-9.

Skrahina A, Hurevich H, Zalutskaya A, et al. Alarming levels of drug-resistant tuberculosis in Belarus: results of a survey in Minsk. Eur Respir J. 2012;39(6):1425-31.

Casali N, Nikolayevskyy V, Balabanova Y, et al. Evolution and transmission of drug-resistant tuberculosis in a Russian population. Nat Genet. 2014;46(3):279-86.

Ahmad N, Ahuja SD, Akkerman OW, et al. Treatment correlates of successful outcomes in pulmonary multidrug-resistant tuberculosis: an individual patient data meta-analysis. Lancet. 2018;392(10150):821-34.

World Health Organization. Tuberculosis SEARO. Geneva: WHO; 2024. Available from: https://www.who.int/southeastasia/health-topics/tuberculosis

Laxminarayan R, Klein E, Darley S, et al. Ending TB in South-East Asia: flagship priority and response transformation. Lancet Reg Health Southeast Asia. 2023;18:100161

..

Downloads

Published

2025-06-10

How to Cite

1.
Pallathadka H, Roy PD, Deka BC, Mishra DK, Saha J, Sarkar R, Roy M, Banerjee M. Unprecedented Molecular Epidemiology of Extreme Drug-Resistant Tuberculosis with Predictive Modeling Analysis: A Comprehensive Investigation in Northeastern India. J Neonatal Surg [Internet]. 2025Jun.10 [cited 2025Jun.20];14(31S):748-66. Available from: https://jneonatalsurg.com/index.php/jns/article/view/7259