Beyond Clinical Prediction: Unexplained Tuberculosis Treatment Outcomes Reveal a Need for Molecular Investigation in Rural South Africa

Tuberculosis (TB) treatment outcomes may differ among individuals with similar routinely recorded demographic and clinical characteristics, suggesting limitations in the explanatory scope of programme surveillance data. This study evaluated the capacity of routinely collected demographic, clinical, socioeconomic, and behavioural variables to explain TB treatment outcomes and used identified evidence gaps to define priorities for complementary molecular research. We conducted a retrospective secondary analysis of anonymised routine TB programme data from 422 adults treated in public healthcare facilities in rural OR Tambo District Municipality, Eastern Cape, South Africa, between January 2018 and December 2022. Treatment success was defined as cure or treatment completion, whereas unsuccessful outcomes included death, treatment failure, or loss to follow-up. The explanatory and predictive performance of logistic regression, Poisson regression, and Random Forest models was evaluated, alongside a structured evidence-gap analysis comparing variables available in routine surveillance with molecular determinants of treatment response and drug resistance identified in the literature. Previous TB treatment was associated with lower odds of treatment success in univariable analysis (OR 0.48, 95% CI 0.29–0.78; p=0.003), but the association attenuated after adjustment (OR 0.64, 95% CI 0.38–1.09; p=0.10). HIV status was not independently associated with treatment success in logistic (OR 0.80, 95% CI 0.49–1.29; p=0.36) or Poisson regression (aRR 0.95, 95% CI 0.66–1.37; p=0.79). Random Forest achieved a higher average precision than logistic regression (0.907 versus 0.810), demonstrating improved prediction from routinely available variables; however, molecular mechanisms could not be evaluated because genomic, transcriptomic, immunological, and pharmacological measures were absent from the dataset. These findings define an important boundary between programme-level prediction and mechanistic explanation. We propose a program-to-molecular translational framework in which routine surveillance identifies clinically relevant patterns and knowledge gaps that can subsequently be investigated using genomic, transcriptomic, and functional molecular approaches. This integration may strengthen mechanistic understanding of heterogeneous TB treatment responses and inform future precision-oriented TB research.