Abstract
INTRODUCTION: Engaging at-risk men in HIV prevention programs and services is a current priority, yet there are few effective ways to identify which men are at highest risk or how to best reach them. In this study we generated multi-factor profiles of HIV acquisition/transmission risk for men in Durban, South Africa, to help inform targeted programming and service delivery. METHODS: Data come from surveys with 947 men ages 20 to 40 conducted in two informal settlements from May to September 2017. Using latent class analysis (LCA), which detects a small set of underlying groups based on multiple dimensions, we identified classes based on nine HIV risk factors and socio-demographic characteristics. We then compared HIV service use between the classes. RESULTS: We identified four latent classes, with good model fit statistics. The older high-risk class (20% of the sample; mean age 36) were more likely married/cohabiting and employed, with multiple sexual partners, substantial age-disparity with partners (eight years younger on-average), transactional relationships (including more resource-intensive forms like paying for partner's rent), and hazardous drinking. The younger high-risk class (24%; mean age 27) were likely unmarried and employed, with the highest probability of multiple partners in the last year (including 42% with 5+ partners), transactional relationships (less resource-intensive, e.g., clothes/transportation), hazardous drinking, and inequitable gender views. The younger moderate-risk class (36%; mean age 23) were most likely unmarried, unemployed technical college/university students/graduates. They had a relatively high probability of multiple partners and transactional relationships (less resource-intensive), and moderate hazardous drinking. Finally, the older low-risk class (20%; mean age 29) were more likely married/cohabiting, employed, and highly gender-equitable, with few partners and limited transactional relationships. Circumcision (status) was higher among the younger moderate-risk class than either high-risk class (p < 0.001). HIV testing and treatment literacy score were suboptimal and did not differ across classes. CONCLUSIONS: Distinct HIV risk profiles among men were identified. Interventions should focus on reaching the highest-risk profiles who, despite their elevated risk, were less or no more likely than the lower-risk to use HIV services. By enabling a more synergistic understanding of subgroups, LCA has potential to enable more strategic, data-driven programming and evaluation.