DOI: 10.12688/f1000research.187371.1 ISSN: 2046-1402

Beyond Biomedical Tuberculosis Care: Reimagining Tuberculosis Control through an Integrated Health Economics, Clinical Governance, and Community Engagement Framework in Rural Eastern Cape

Ntandazo Dlatu, Onke Mnyaka, Ncomeka Sineke, Lindiwe Faye
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Background Tuberculosis (TB) treatment outcomes remain suboptimal in many high-burden settings because biomedical care alone does not adequately address the socioeconomic, community, and health system barriers influencing treatment adherence and completion. This study investigated socioeconomic and clinical determinants of TB treatment outcomes and treatment duration and modelled the potential impact of integrated health economics, clinical governance, and community engagement interventions in rural Eastern Cape, South Africa. Methods A retrospective observational study analysed routinely collected TB programme data from 538 patients in O.R. Tambo District. Multivariable logistic regression identified predictors of treatment success, while multiple linear regression and Cox proportional hazards models assessed determinants of treatment duration. Logistic Regression, Random Forest, and Extreme Gradient Boosting (XGBoost) models were developed to predict treatment outcomes. Scenario-based health systems modelling estimated the potential impact of integrated interventions under model assumptions. Results Overall, 411 patients (76.4%) achieved successful treatment outcomes, while 127 (23.6%) had unsuccessful outcomes. Socioeconomic vulnerability was substantial, with 77.5% reporting no regular income and 66.4% being unemployed. After adjustment, long treatment regimen remained the only independent predictor of treatment success, reducing the odds of success by 58% compared with short-course therapy (aOR 0.42; 95% CI 0.20–0.89; p  = 0.024). Absence of income, unemployment, HIV co-infection, and long treatment regimen were associated with prolonged treatment duration and delayed completion. XGBoost demonstrated the strongest predictive performance (AUC = 0.89; accuracy = 85%). Scenario-based modelling projected that the integrated intervention framework could increase treatment success from 76.4% to 95.3% and reduce unsuccessful outcomes from 23.6% to 4.7%. Conclusions TB treatment outcomes in rural South Africa are shaped by the interaction of socioeconomic, clinical, community, and health system factors. Although prospective validation is required, integrating socioeconomic support with strengthened clinical governance and community engagement may improve treatment outcomes, reduce programme inefficiencies, and advance patient-centred TB care in resource-constrained settings.

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