Determinants of Late-Stage Diagnosis of HIV-Associated Kaposi Sarcoma in Africa
Objective: Despite the availability of life-saving antiretroviral therapy (ART) in sub-Saharan Africa, our group and others have observed that a substantial proportion of individuals with Kaposi sarcoma (KS) initiate ART only after progressing to advanced-stage disease. While early-stage KS is likely to respond to ART alone, late-stage KS relies on chemotherapy and a sophisticated medical system capable of providing supportive care, neither of which are currently available in Uganda. We have therefore shifted our efforts to detecting KS in its earliest stages, when patients are more responsive to ART alone. An effective early detection campaign requires an improved understanding of why patients with KS often present with advanced disease. We hypothesize that lack of training of medical providers is a significant contributor to late-stage presentation of KS, and that the majority of patients actually present to medical care early in their disease course. To address this hypothesis, our specific aims are to: 1) qualitatively determine reasons patients with KS are diagnosed in advanced stages, and 2) to then create and administer an instrument to quantitatively determine risk factors for late-stage diagnosis. Findings from this work will form the basis for a subsequent large-scale application to design the most effective targeted interventions to diagnose KS at its earliest stages and ultimately ameliorate the considerable morbidity and mortality posed by KS in Africa. Design and Duration: 20 patients with advanced disease will undergo in-depth interviews over a period of 3 months. This qualitative work will inform the creation of a quantitative questionnaire, which, over 8 months, will be administered to 100 patients with late-stage KS and 50 with early-stage KS, allowing systematic evaluation of the determinants of late-stage diagnosis. Statistical analysis: Qualitative data will be analyzed using grounded theory, and quantitative outcomes will be analyzed with adjusted multivariable logistic regression.