Patients' and Providers' Views on Optimal Evidence-Based and Scalable Interventions for Individuals at High Risk of HIV Treatment Failure: Sequential Explorations Among Key Stakeholders in Cape Town, South Africa.
Lora L SabinAllen L GiffordJessica E HabererKelsee HarveyNatalya SarkisovaKyle MartinRebecca L WestJessie StephensClare KillianNafisa HalimNatacha BerkowitzKaren JenningsLauren JenningsCatherine OrrellPublished in: AIDS and behavior (2022)
To support translation of evidence-based interventions into practice for HIV patients at high risk of treatment failure, we conducted qualitative research in Cape Town, South Africa. After local health officials vetted interventions as potentially scalable, we held 41 in-depth interviews with patients with elevated viral load or a 3-month treatment gap at community clinics, followed by focus group discussions (FGDs) with 20 providers (physicians/nurses, counselors, and community health care workers). Interviews queried treatment barriers, solutions, and specific intervention options, including motivational text messages, data-informed counseling, individual counseling, peer support groups, check-in texts, and treatment buddies. Based on patients' preferences, motivational texts and treatment buddies were removed from consideration in subsequent FGDs. Patients most preferred peer support groups and check-in texts while individual counseling garnered the broadest support among providers. Check-in texts, peer support groups, and data-informed counseling were also endorsed by provider sub-groups. These strategies warrant attention for scale-up in South Africa and other resource-constrained settings.
Keyphrases
- south africa
- hiv positive
- healthcare
- end stage renal disease
- primary care
- mental health
- newly diagnosed
- randomized controlled trial
- chronic kidney disease
- ejection fraction
- public health
- prognostic factors
- human immunodeficiency virus
- peritoneal dialysis
- electronic health record
- social media
- patient reported outcomes
- optical coherence tomography
- deep learning
- advance care planning