Prevalence estimates of trafficking in persons using statistical definitions: a cross-sectional high-risk community survey in Cape Town, South Africa

ObjectivesCurrent research on trafficking in persons (TIP) relies heavily on legal and prosecutorial definitions. A public health approach has called for population-level assessment; however, identification of TIP victims lacks a standardised operational definition. This study applied the Prevalence Reduction Innovation Forum (PRIF) statistical definitions, developed by the US Department of State, to a community survey in Cape Town, South Africa.DesignsA high-risk sampling strategy was used. TIP screening questions from two instruments were matched with PRIF domain indicators to generate prevalence estimates. Sensitivity, specificity and receiver operating characteristics analyses were conducted to assess the performance of the two screeners.SettingCross-sectional survey conducted in Cape Town, South Africa, from January to October 2021.ParticipantsSouth Africans and immigrants from other nations residing in Cape Town and its surrounding areas, aged 18 or older, who met the study inclusion criteria for a set of experiences that were identified as TIP risk factors.Primary and secondary outcome measuresPrimary outcome measures were PRIF lifetime and past 12-month TIP positivity. Secondary outcome measures included individual and summary measures from the two screeners.ResultsOur PRIF algorithm yielded a TIP lifetime prevalence rate of 17.0% and past 12-month rate of 2.9%. Summary measures from each TIP screener showed an excellent range of predictive utility. The summary screener measures yielded statistically significant differences among some demographic and background categories. Several screener items were shown less predictive of the PRIF statistical definition criteria than others.ConclusionsPrevalence estimates of probable TIP were higher than those reported elsewhere. Our TIP screeners yielded an excellent range of predictive utility for the statistical definitions, promising the potential for wider applications in global and regional TIP research and policymaking. A more systematic sampling strategy is needed even if statistical definitions become widely used.

Automated summary

Current research on trafficking in persons (TIP) often lacks a standardized operational definition. This study applied the Prevalence Reduction Innovation Forum (PRIF) statistical definitions to assess TIP prevalence in Cape Town, South Africa. The study found that the PRIF algorithm yielded higher prevalence estimates compared to previous reports, and the TIP screeners showed promising predictive utility for the statistical definitions. The findings suggest the potential for wider applications in global and regional TIP research and policymaking.