Transmission Control and Treatment Access Challenges

1. Clinical and Biological Predictors of Treatment Outcomes

• Key Findings: Early culture conversion is the strongest predictor of successful MDR-TB treatment. Younger age, male sex, normal BMI, no prior TB treatment, and absence of HIV, CKD, or cavitary lung lesions improve outcomes. Prediabetes is linked to delayed sputum conversion and poor prognosis if HbA1c levels are high. See also: Lin TB Lab NTU
• Action Plan: Implement Early Monitoring Protocols: Introduce routine and rapid culture conversion testing within the first two months of treatment. Incorporate regular HbA1c testing for TB patients to identify and manage prediabetes early, potentially through lifestyle interventions or medications.

2. Diagnostic and Treatment Access Challenges in Urban Settings

• Key Issues: Low identification and diagnosis rates for DR-TB in urban Indonesia. Significant delays between diagnosis and treatment, particularly for marginalized populations. Limited diagnostic support and reliance on phenotypic drug susceptibility testing (pDST).
• Action Plan: Enhance Diagnostic Capacity and Accessibility: Scale up active case-finding strategies, particularly in high-density and underserved areas. Establish more accessible diagnostic centers with faster molecular testing (e.g., GeneXpert) to reduce delays and improve linkage to care.

3. Geographic Hotspots and Transmission Control

• Critical Insights: MDR-TB hotspots exhibit higher transmission rates and specific genotypic clustering (e.g., LAM sublineage). Direct transmission is a significant contributor to MDR-TB in hotspot regions, even among treatment-naïve individuals. Spatial clustering indicates the need for geographically targeted interventions.
• Action Plan: Targeted Public Health Interventions: Deploy resources to identified hotspots with enhanced screening, treatment adherence support, and community education. Integrate geographic and mathematical modeling to adapt strategies dynamically based on emerging data.

References:

1. Soeroto, A.Y., Pratiwi, C., Santoso, P. and Lestari, B.W., 2021. Factors affecting outcome of longer regimen multidrug-resistant tuberculosis treatment in West Java Indonesia: A retrospective cohort study. PloS one, 16(2), p.e0246284.
2. Lestari, B.W., Nijman, G., Larasmanah, A., Soeroto, A.Y., Santoso, P., Alisjahbana, B., Chaidir, L., Andriyoko, B., Van Crevel, R. and Hill, P.C., 2024. Management of drug-resistant tuberculosis in Indonesia: a four-year cascade of care analysis. The Lancet Regional Health-Southeast Asia, 22:100294.
3. Viswanathan, V., Devarajan, A., Kumpatla, S., Dhanasekaran, M., Babu, S. and Kornfeld, H., 2023. Effect of prediabetes on tuberculosis treatment outcomes: A study from South India. Diabetes & Metabolic Syndrome: Clinical Research & Reviews, 17(7), p.102801.
4. Zelner, J.L., Murray, M.B., Becerra, M.C., Galea, J., Lecca, L., Calderon, R., Yataco, R., Contreras, C., Zhang, Z., Manjourides, J. and Grenfell, B.T., 2016. Identifying hotspots of multidrug-resistant tuberculosis transmission using spatial and molecular genetic data. The Journal of infectious diseases, 213(2), pp.287-294.
5. Manjourides, J., Lin, H.H., Shin, S., Jeffery, C., Contreras, C., Santa Cruz, J., Jave, O., Yagui, M., Asencios, L., Pagano, M. and Cohen, T., 2012. Identifying multidrug resistant tuberculosis transmission hotspots using routinely collected data. Tuberculosis, 92(3), pp.273-279.
6. Wulandari, D.A., Hartati, Y.W., Ibrahim, A.U. and Pitaloka, D.A.E., 2024. Multidrug-resistant tuberculosis. Clinica Chimica Acta, 559, p.119701.

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