Antiretroviral treatment sharing among highly mobile Ugandan fisherfolk living with HIV: a qualitative study.
Joseph Gregory RosenNeema NakyanjoDauda IsabiryeMaria J WawerFred NalugodaSteven J ReynoldsGertrude NakigoziM Kathryn GrabowskiCaitlin Elizabeth KennedyPublished in: AIDS care (2019)
Antiretroviral treatment (ART) diversion from prescribed to non-prescribed users (i.e., sharing or selling) is an understudied dimension of HIV treatment adherence. We sought to explore ART diversion patterns in high-prevalence fishing communities on Lake Victoria, Uganda. We implemented a qualitative study in two fishing communities on Lake Victoria in south-central Uganda to identify facilitators of and pathways to ART diversion. We conducted 25 semi-structured interviews with HIV-positive fishermen (n = 25) and female sex workers (n = 10) covering personal and community experiences with ART selling/sharing, reasons for medication diversion, and potential solutions to reduce diversion. Data were analyzed using an adapted framework analysis approach. Participants reported frequent ART sharing within occupational networks, but no selling. Mobility was the principal driver of ART sharing and was associated with other barriers to treatment access including stigma, fear of negative health provider interactions, and transportation. ART sharing appears to emerge in response to short-term treatment interruptions in this setting. Future studies should explore characteristics and drivers of ART diversion in other high-burden settings and identify how these practices are correlated with key health outcomes like virologic failure and drug resistance.
Keyphrases
- antiretroviral therapy
- hiv infected
- hiv positive
- human immunodeficiency virus
- hiv aids
- healthcare
- social media
- mental health
- hiv infected patients
- primary care
- south africa
- robot assisted
- men who have sex with men
- public health
- type diabetes
- combination therapy
- skeletal muscle
- emergency department
- risk assessment
- risk factors
- depressive symptoms
- replacement therapy
- machine learning
- human health
- smoking cessation