Factors influencing adherence to antiretroviral therapy among people living with HIV in an urban and rural setting, Tanzania

Adherence is one of the most crucial determinants of treatment response to antiretroviral therapy (ART). An analytical cross-sectional study was conducted in 24 Care and Treatment Centres (CTC) in Dar es Salaam and Iringa regions in Tanzania. Data was collected using questionnaire and appointments records. A total of 943 patients attending at the care and treatment sites in Dar es Salaam and Iringa were recruited. Adherence based on keeping appointments and on four days recall was 65% and 70%, respectively. Adherence based on taking ART more than 95% of the time in one month was 83%. Satisfaction with health services, having treatment support, having knowledge on the use of ART, early presentation to CTC, and being on ART for more than one year, were associated with good adherence. Being in the urban region, using traditional medicine, medicine side effects and alcohol consumption problems negatively associated with adherence to ART.Keywords: Adherence barriers, antiretroviral therapy, HIV, Tanzania, rural, urba

HIV-1 fitness landscape models for indinavir treatment pressure using observed evolution in longitudinal sequence data are predictive for treatment failure

We previously modeled the in vivo evolution of human immunodeficiency virus-1 (HIV-1) under drug selective pressure from cross-sectional viral sequences. These fitness landscapes (FLs) were made by using first a Bayesian network (BN) to map epistatic substitutions, followed by scaling the fitness landscape based on an HIV evolution simulator trying to evolve the sequences from treatment naïve patients into sequences from patients failing treatment. In this study, we compared four FLs trained with different sequence populations. Epistatic interactions were learned from three different cross-sectional BNs, trained with sequence from patients experienced with indinavir (BNT), all protease inhibitors (PIs) (BNP) or all PI except indinavir (BND). Scaling the fitness landscape was done using cross-sectional data from drug naïve and indinavir experienced patients (Fcross using BNT) and using longitudinal sequences from patients failing indinavir (FlongT using BNT, FlongP using BNP, FlongD using BND). Evaluation to predict the failing sequence and therapy outcome was performed on independent sequences of patients on indinavir. Parameters included estimated fitness (LogF), the number of generations (GF) or mutations (MF) to reach the fitness threshold (average fitness when a major resistance mutation appeared), the number of generations (GR) or mutations (MR) to reach a major resistance mutation and compared to genotypic susceptibility score (GSS) from Rega and HIVdb algorithms. In pairwise FL comparisons we found significant correlation between fitness values for individual sequences, and this correlation improved after correcting for the subtype. Furthermore, FLs could predict the failing sequence under indinavir-containing combinations. At 12 and 48 weeks, all parameters from all FLs and indinavir GSS (both for Rega and HIVdb) were predictive of therapy outcome, except MR for FlongT and FlongP. The fitness landscapes have similar predictive power for treatment response under indinavir-containing regimen as standard rules-based algorithms, and additionally allow predicting genetic evolution under indinavir selective pressure