TB remains a leading cause of mortality and morbidity in sub-Saharan Africa, due to the HIV epidemic. As TB treatment is lengthy, the completion of the full course of treatment may be especially challenging for young people. We therefore aimed to identify the extent of and reasons underlying loss to follow-up from TB treatment among young people in Cape Town. Accordingly, we reviewed the outcomes of young people treated for TB in Cape Town during 2009–2013, across three age groups: younger adolescents (10–14 years); older adolescents; (15–19 years) and young adults (20–24 years). We employed logistic regression analysis to identify risk factors for loss from TB care. 23,737 patients aged 10–24 were treated for drug sensitive TB over the study period. Of these, the HIV co-infection prevalence was 18.5% for younger adolescents, 12.9% for older adolescents and 33.1% for young adults. From age 16, HIV prevalence increased disproportionately among young women: by age 22, over 50% of women were TB/HIV co-infected compared to 14% of men. TB treatment success (cure plus completion) was 84.4%, while 1.7% of patients died, 9.5% were lost-to follow-up and 0.4% failed treatment. Being an older adolescent (aOR 1.75 [95% CI: 1.38–2.21]) or young adult (aOR: 1.96 [95% CI: 1.57–2.45]) increased the risk of loss-to-follow up, relative to being a younger adolescent. Further risk factors for loss from TB care were male gender (aOR: 1.33 [95% CI:1.20–1.46]), being a TB/HIV co-infected young person (aOR 1.74 [95% CI: 1.57–1.93]) and having had prior treatment for TB (aOR 3.17 [95% CI 2.87–3.51]). We identified risk factors for loss to follow-up and highlighted the need to focus on HIV prevention and retention in TB care among young people. TB care tailored to the needs of young people could improve patient retention, similar to improved outcomes reported by youth friendly HIV clinics.

Respondent-driven sampling (RDS) is widely used to estimate HIV prevalence in men who have sex with men (MSM). Mathematical models that are calibrated to these data may be compromised if they fail to account for selection biases in RDS surveys. To quantify the potential extent of this bias, an agent-based model of HIV in South Africa was calibrated to HIV prevalence and sexual behaviour data from South African studies of MSM, first reweighting the modelled MSM population to match the younger age profile of the RDS surveys (age-adjusted analysis) and then without reweighting (unadjusted analysis). The model estimated a median HIV prevalence in South African MSM in 2015 of 34.6% (inter-quartile range (IQR): 31.4-37.2%) in the age-adjusted analysis, compared with 26.1% (IQR: 24.1-28.4%) in the unadjusted analysis. The median lifetime risk of acquiring HIV in exclusively homosexual men was 88% (IQR: 82-92%) in the age-adjusted analysis, compared with 76% (IQR: 64-85%) in the unadjusted analysis. These results suggest that RDS studies may under-estimate the exceptionally high HIV prevalence rates in South African MSM because of over-sampling of younger MSM. Mathematical models that are calibrated to these data need to control for likely over-sampling of younger MSM.