Explanatory Modelling of Tuberculosis Treatment Outcomes: The Role of Community Engagement and Clinical Governance

Background Treatment adherence and outcomes for drug-resistant tuberculosis (DR-TB) continue to be sub-par in rural South Africa, where structural health system limitations, comorbid conditions, and diverse resistance patterns make clinical management more challenging. This study aimed to assess how demographic, clinical, and programmatic factors, including a Community Engagement–Clinical Governance (CE–CG) implementation period, affect DR-TB treatment outcomes using explanatory predictive modelling. Methods A retrospective cohort study was conducted using routine programme data from 694 DR-TB patients. Complete-case analysis was performed for multivariable modelling (n = 282). Logistic regression and decision tree models were used to examine the relationships between treatment success and selected predictors, including age, sex, treatment regimen, resistance phenotype, comorbidities, and the CE–CG implementation period. Model discrimination and performance were evaluated using receiver operating characteristic (ROC) curves, pseudo-R² statistics, likelihood ratio tests, and multicollinearity diagnostics. Results The cohort had a mean age of 40.7 years, and 58.8% of patients were male. Overall treatment success was 59.9%. Severe resistance phenotypes were rare (1.7%) but clinically significant. Comparative analysis showed no notable demographic or outcome differences between included and excluded patients, indicating minimal selection bias. In adjusted models, treatment initiation during the CE–CG implementation period was significantly linked to lower odds of treatment success (adjusted odds ratio [aOR] = 0.443; 95% CI: 0.240–0.818; p = 0.009). Severe resistance phenotypes were strongly negatively associated with treatment success (aOR = 0.303; p = 0.056). Logistic regression models had limited discriminatory ability (AUC: 0.523–0.548), while the decision tree model showed modest improvement (AUC: 0.626). Overall, the model’s explanatory power was limited (pseudo-R² = 0.029), although no evidence of multicollinearity was found. Conclusions Programmatic implementation durations and resistance severity were key factors influencing treatment outcomes in this rural DR-TB cohort. Although the predictive accuracy was modest, the modelling approach revealed structural and programmatic vulnerabilities that impacted treatment success. Enhancing clinical governance, improving program documentation, and expanding community-engaged adherence strategies may improve DR-TB results. Future predictive models should incorporate programmatic indicators alongside longitudinal adherence data and social determinants of health to boost explanatory power and guide targeted interventions.