Who does tracing work for? Characteristics of clients successfully re-engaged in ART care in sub-Saharan Africa after a tracing intervention: A systematic review

Abstract

Tracing HIV treatment clients who have interrupted or disengaged from care is a common, guideline-recommended practice globally. Most guidelines prioritize tracing based on clinical condition or HIV transmission risk, not likelihood of client traits that may affect return to care after tracing. Targeting tracing to those most likely to return could increase efficiency substantially. We conducted a systematic review to identify characteristics of clients most likely to return after tracing. We searched PubMed, EMBASE, and Web of Science for studies published between 1/2004 and 7/2025 that reported outcomes of tracing interventions in sub-Saharan Africa. Eligible studies reported characteristics of clients who interrupted care, were eligible for a tracing intervention with the intent to return them to care (i.e., not solely research to determine client outcomes after interruption) and were subsequently traced or had tracing attempted. Our primary outcome was client characteristics associated with return to care after tracing, compared to those who did not return after tracing or attempted tracing. We identified 13,208 articles; 9 met the inclusion criteria. Older age and female sex were the most consistent predictors of return after tracing. Earlier tracing (relative to last missed visit) was associated with return in 3 studies; 1 found the opposite. Frequent contact attempts, rural location, and psychosocial factors (stigma, disclosure) were also associated with return. Clinical characteristics (CD4 counts and WHO stage) showed mixed or null associations with tracing effectiveness. Characteristics of clients who return to care after tracing, compared to those who are traced or for whom tracing is attempted and do not return, are rarely reported, making it difficult to evaluate this intervention. Using a “high-benefit” approach to targeting tracing—i.e., prioritizing based on likely benefit generated by a successful response, rather than clinical need—may potentially improve the efficiency of HIV programming.