Re-conceptualizing trafficking-in-persons victimization using latent class analysis: Results from a community study in Cape Town, South Africa

The impact of human trafficking upon the lives and livelihoods of those subjected to exploitative and illegal labor and commercial sex practices includes violence and threats of violence, deleterious health and mental health sequelae, and social and economic marginalization. Global estimates of human trafficking's prevalence are elusive given that it is shrouded in secrecy and often affects subgroups with little voice of their own, such as migrants and child abuse victims. The difficulty of reaching a clandestine population is complicated by the lack of standardized definitions and culturally responsive assessments to identify victims and route them to appropriate care. This gap in knowledge persists on the African continent as elsewhere in the world. An interdisciplinary, international research team thus launched a study to estimate the prevalence of human trafficking using a computational algorithm in the Western Cape of South Africa. In this paper, we use latent class analysis to identify and empirically categorize 652 individuals at risk for human trafficking based on their response to two sets of indicators for human trafficking experiences. Our findings revealed three distinct subtypes ranging from very high risk of exploitation to relatively low risk. Experiences of violence, health and mental health concerns, and substance abuse were commonplace in this high-risk sample. A modified screening tool based on domains of trafficking identified by the inaugural Prevalence Reduction Innovation Forum (PRIF) was most robust in identifying and classifying victims. We conclude by calling for a shift from a binary prosecutorial definition to a dimensional approach of identifying trafficking, guided by the understanding that such risks exist on a spectrum influenced by one's experience of human trafficking exploitation, and behavioral and social environment.