<![CDATA[Introduction: Tuberculosis (TB) control remains a major public health challenge in Indonesia, with persistent transmission despite established programs. Conventional approaches often overlook the role of social structures in shaping treatment adherence and case detection. This study aimed to evaluate the effectiveness of a community-based TB intervention informed by social network analysis (SNA) in improving treatment adherence and case detection in Baubau City. Research Methodology: A quasi-experimental design with a non-equivalent control group was employed. A total of 240 participants (120 in the intervention group; 120 in the control group) were selected through purposive and snowball sampling. Data were collected through structured interviews, medical records, and network mapping. Bivariate analysis was conducted using chi-square tests, followed by multivariate logistic regression to estimate adjusted effects. Statistical significance was set at α = 0.05. Results: The intervention significantly improved treatment adherence (AOR = 2.52; 95% CI: 1.39–4.56; p = 0.002). High degree centrality was also associated with increased adherence (AOR = 2.83; 95% CI: 1.49–5.38; p = 0.001), as was peer support (AOR = 2.26; 95% CI: 1.21–4.20; p = 0.010). Network density was associated with case detection in bivariate analysis (OR = 1.71; 95% CI: 1.05–2.79; p = 0.032), but this association was not significant after adjustment. Sociodemographic variables were not significant predictors. Conclusion: Community-based TB interventions integrating social network analysis significantly enhance treatment adherence and case detection. Targeting influential network actors and strengthening peer support systems offer a scalable, evidence-based strategy to improve TB control outcomes.]]>
Copyrights © 2026
