Background Tuberculosis (TB) remains a major global health challenge, particularly in low‐ and middle‐income countries (LMICs). Community health workers (CHWs) are widely deployed to enhance TB care delivery, yet their roles, implementation models and contextual determinants have not been comprehensively synthesized. Objective To systematically review CHW‐led TB interventions in LMICs and to examine contextual determinants influencing implementation and effectiveness. Methods A systematic review was conducted following PRISMA 2020 guidelines. PubMed, Scopus and Web of Science were searched for studies published up to 31 December 2025. Quantitative, qualitative and mixed‐method studies evaluating CHW‐led TB interventions in LMICs were included. Study quality was appraised using Joanna Briggs Institute tools. Data were synthesized thematically and mapped to the Consolidated Framework for Implementation Research (CFIR). Findings were organized according to intervention models (community‐based DOT, family‐supported care and digital/hybrid approaches). Results Ten studies were included: nine from Sub‐Saharan Africa (Ethiopia, Rwanda, Cameroon, Uganda and South Africa) and South/Southeast Asia (Indonesia, India, and Vietnam), and one multi‐country analysis of TB‐REACH projects. CHWs contributed to health education, treatment adherence support, active case finding and system navigation. Implementation models varied across community‐based DOT, family‐supported and digital/hybrid approaches. Effectiveness was influenced by patient knowledge, social stigma, CHW training and integration into health systems. Several studies reported improvements in treatment adherence and case detection, including one large Ethiopian pre–post study reporting treatment success improvement from 76% to 95%. Conclusion CHWs are critical in TB care in LMICs, but effectiveness is context‐dependent. Strengthening CHW capacity, culturally responsive implementation and systematic evaluation frameworks are essential. Evidence from comparative and implementation studies suggests that hybrid and digitally supported models show promise for improving scalability and coverage.
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Sadipun et al. (2026) studied this question.
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