A data-driven approach to identify putative tuberculosis among adolescents in resource-constrained settings

Abstract

<b>Introduction:</b> There has been recent evidence of low rates of tuberculosis (TB) testing in Southern Africa, and lack of evidence age-disaggregated data on pulmonary TB rates to identify TB outcomes among adolescents. <b>Aim/objective:</b> To evaluate the potential of self-reported tuberculosis (TB) clinic diagnosis and self-reported TB symptoms for estimating TB rates among adolescents living with HIV&nbsp;in a resource-constrained setting in South Africa. <b>Methods:</b> This study is based on data from three waves of a longitudinal study cohort of 1 060 ALHIV, ART-initiated adolescents in Eastern Cape province of South Africa. First, we use latent class analysis (LCA) and group-based trajectory modelling (GBTM) to model TB symptom trajectory groups based on past-year self-reported TB symptoms. Second, we then use multinomial logistic regression to assess the association between TB symptom trajectory groups and self-reported TB test results. <b>Results:</b> We identified three long-term TB symptomology groups namely: high TB symptomatic (16.6%), decreasing symptomatic (23.3%), and asymptomatic (60.1%). Both symptomatic groups were significantly associated with self-reported TB diagnosis. Baseline self-reported positive TB diagnosis (OR2.02 95%CI 1.42-2.89, p&lt;0.001) and HIV-positive status (OR1.94 95%CI 1.31-2.87, p&lt;0.001) were significantly associated with high TB symptomatic group controlling for age, gender, and rural residence. These findings were consistent over time. <b>Conclusion:</b> Self-reported symptom screening is useful for ruling out TB and identify cases in need of further diagnostic assessment for TB.&nbsp;Given high rates of TB symptomatic ALHIV better diagnostics are urgently needed.