Acceptance and Completion of Rifapentine-Based TB Preventive Therapy (3HP) Among People Living with HIV (PLHIV) in Kampala, Uganda – Patient and health Worker Perspectives.

Abstract

BackgroundA 12-dose, once-weekly regimen of isoniazid and rifapentine (3HP) is effective in preventing tuberculosis (TB) among people living with HIV (PLHIV). We sought to identify potential barriers to and facilitators of 3HP implementation from the perspective of PLHIV and health workers in a routine HIV care setting in Kampala, Uganda.MethodsWe conducted semi-structured interviews with 25 PLHIV and 10 health workers at an HIV/AIDS clinic in Kampala, Uganda. For both groups, we explored their understanding and interpretations of TB and TB preventive therapy (TPT), and perceptions about social and contextual factors that might influence willingness of PLHIV to initiate and complete 3HP. We analyzed the data using an inductive thematic approach and aligned the emergent themes to the Behavior Change Wheel framework to identify sources of behavior and targeted behavior change interventions.ResultsFacilitators for uptake and completion of 3HP among PLHIV were fear of contracting TB, awareness of being potentially at risk of getting TB, willingness to take TPT, trust in health workers, and the perceived benefits of DOT and SAT. Barriers included inadequate understanding of TPT, fear of potential side effects, concerns about effectiveness of 3HP as well as the perceived challenges of DOT or SAT. Among health workers, perceived facilitators included knowledge that TB is a common cause of mortality, fear of getting TB, patient trust in their health workers, potential for once-weekly dosing and the benefits of TPT delivery. Health workers perceived potential barriers for PLHIV as being: inadequate understanding of TB and TPT, TB associated stigma, potential side effects, pill burden and challenges of taking TPT. Additional barriers included a lack of experience among health workers with the use of digital technology to monitor patient care.Conclusions Using a formative qualitative and comprehensive theoretical approach, we identified key sources of behavior that could be used to guide selection of appropriate intervention to optimize 3HP scale up among PLHIV in high burden settings.

A machine learning model to explore individual risk factors for tuberculosis treatment non-adherence in Mukono district

“…Various studies have reported on the significance of patient risk groups and residency as key determinants in treatment adherence (11,50,68,69). In our modeling, we equally found the two characteristics to be predictive of whether a patient will adhere to treatment or not.…”

“…Further, we found out that some patients who were not residents of our study site catchment areas attended the health facilities located within Mukono. This could be because of the stigma associated with tuberculosis as was discussed by (69). In a similar scenario for covid-19 treatment seeking, a study by Muttamba et.…”