Tuberculosis Treatment Adherence and Outcomes
Summary
Tuberculosis treatment adherence—consistent and complete intake of prescribed anti-tubercular drugs—is central to achieving favourable outcomes, including cure, prevention of relapse and avoidance of drug resistance. Non-adherence extends infectious periods, fuels the emergence of multidrug-resistant tuberculosis (MDR-TB) and undermines global eradication efforts. Determinants of adherence span patient-level factors (knowledge, belief in curability, medication burden, side-effect experience), socioeconomic constraints (transport costs, food insecurity, loss of income) and health-system attributes (access to care, quality of counselling, delivery models such as community or clinic directly observed therapy). Recent advances have integrated quantitative risk modelling, patient-reported outcome measures and qualitative enquiry to capture the dynamic interplay of these factors over the intensive and continuation phases of therapy. Patient-centred interventions—ranging from behavioural counselling guided by theoretical models to digital adherence technologies and decentralised drug delivery—have demonstrated improvements in completion rates and reductions in default. Embedding such approaches within broader social support and health-system strengthening is vital to safeguard treatment efficacy, reduce transmission and meet international targets for TB elimination.
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Tuberculosis Treatment Adherence and Outcomes publication trend
The graph below shows the total number of articles in tuberculosis treatment adherence and outcomes across all publications each year (not limited to Nature Index journals).
Technical terms
Treatment adherence: The extent to which a patient takes medications exactly as prescribed throughout the therapy course.
Treatment outcomes: Clinical end-points of therapy, including cure, treatment failure, relapse or development of drug resistance.
Multidrug-resistant tuberculosis (MDR-TB): Infection caused by Mycobacterium tuberculosis strains resistant to at least isoniazid and rifampicin, the two most potent first-line drugs.
Medication-related burden (MRB): The cumulative impact of treatment regimens on patients’ daily lives, encompassing side effects, regimen complexity and practical challenges.
Directly Observed Therapy, Short-course (DOTS): A TB control strategy in which a healthcare worker or trained volunteer observes each dose to ensure correct intake.
Machine learning algorithm: A computational method that learns patterns from data to make predictions or classifications, such as identifying patients at risk of non-adherence.
References
- Prognostication of treatment non-compliance among patients with multidrug-resistant tuberculosis in the course of their follow-up: a logistic regression–based machine learning algorithm. Frontiers in Digital Health (2023).
- Medication-related burden and its association with medication adherence among elderly tuberculosis patients in Guizhou, China: a cross-sectional study. Frontiers in Pharmacology (2024).
- Barriers to tuberculosis treatment adherence in high-burden tuberculosis settings in Ashanti region, Ghana: a qualitative study from patient’s perspective. BMC Public Health (2023).
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