Hierarchical Risk Profiles in Tuberculosis Treatment Outcomes: The Role of Drug Resistance, Age, and Socio-Economic Factors

Background: Tuberculosis (TB) outcomes remain suboptimal in high-burden, resource-constrained settings. Clinical and socio-economic factors contribute to loss to follow-up, failure, and mortality, yet their relative importance remains underexplored. Methods: We analyzed a retrospective cohort of patients treated for pulmonary TB in the Eastern Cape, South Africa. Treatment outcomes were dichotomized as success (cured or treatment completed) versus unsuccessful (loss to follow-up, failure, or death), excluding transfers and patients still on treatment. Predictors included age, gender, income, occupation, comorbidities, HIV status, previous treatment history, patient category, and drug resistance status. Regularized logistic regression was used to estimate odds ratios, while the best decision tree model was applied to identify hierarchical risk profiles. Results: Logistic regression demonstrated high accuracy (86%) and identified drug susceptibility, age, income stability, and comorbidity burden as the strongest predictors of treatment success. The decision tree achieved lower accuracy (65%) but improved detection of unsuccessful outcomes, highlighting a clear hierarchy of risk: (1) drug resistance status, (2) age, (3) income source, and (4) comorbidities. Patients with drug-resistant TB, older age, no income or reliance on grants, and coexisting conditions were at the highest risk of poor outcomes. Conclusions: Drug resistance, age, income, and comorbidity burden shape a hierarchical risk profile for TB treatment outcomes in rural South Africa. Logistic regression offered robust overall classification, while the decision tree provided transparent stratification of at-risk groups. These findings underscore the need for integrated clinical and socio-economic support strategies to improve outcomes in high-burden settings.