The Role of Epidemiologic Surveillance in the Control of Drug-Resistant Tuberculosis (TB): A Literature Review
DOI:
https://doi.org/10.55606/ijhs.v5i2.5513Keywords:
Epidemiological Surveillance, Drug-Resistant Tuberculosis, MDR-TB, Disease Control, Literature ReviewAbstract
Drug-resistant tuberculosis (DR-TB) poses a serious threat to global public health and is a major barrier to achieving TB elimination targets. Epidemiological surveillance plays a central role in its control, yet its implementation faces various challenges, leading to gaps in detection and response. This literature review aims to synthesize the latest scientific evidence on the role, methods, and challenges of DR-TB surveillance to formulate a comprehensive overview as a basis for program improvement recommendations. A systematic literature search was conducted in PubMed and Google Scholar for articles published between 2015 and 2025. From an initial 347 articles, 6 relevant studies from various countries were selected based on inclusion and exclusion criteria for narrative analysis. The analysis reveals a significant gap between the estimated disease burden and detected cases, with the sensitivity of surveillance systems in some countries (e.g., Brazil) reported as low (~46.4%) and massive diagnostic failures (59% in Madagascar). Strong evidence indicates that active community transmission is a key driver of the epidemic, demonstrated by high primary resistance rates (9% in Bhutan) and the presence of genetic transmission clusters (37% of cases in Pará, Brazil). The studies also highlight the potential of innovative methods such as statistical correction to improve estimation accuracy and genomic surveillance for detecting new drug resistance and mapping transmission. Conventional DR-TB surveillance is no longer adequate to address the current complexity of the epidemic. Future effective control demands a dual approach: strengthening fundamental health systems (diagnostics and logistics) integrated with the adoption of innovative, data-driven surveillance methods such as statistical modeling and genomics for a more accurate, timely, and precise response.
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