Authors: 

Ahmed I, Tefera F, Bekele A, Ayalew J, Tessema F, Abera G, Ahmed J, Mekonnen A, Haile A, Yohannes F, Getachew M, Abdella S, Shah M.

Abstract:

Despite the success in scaling-up antiretroviral therapy (ART) services in Ethiopia, suboptimal adherence to ART has been an existing challenge. There is a dearth of evidence on the status of adherence to ART following the adoption of test and treat strategy in Ethiopia. This study aimed to investigate on the magnitude of suboptimal adherence and its predictors among patients taking ART. A multicenter prospective cohort study was conducted among adults aged 15 years and above who started ART between March and June 2019 in 39 health facilities (HFs) in Ethiopia. Measurements on sociodemographic, behavioral, and clinical characteristics were taken at baseline and 6- and 12-months following ART initiation. Multivariable logistic regression model using generalized estimating equations was used to identify factors associated with suboptimal adherence. In total, 1229 individuals who started ART were included in the study. The proportion of suboptimal adherence was 8.0% and 7.9% at 6- and 12-months, respectively. Younger age (adjusted odds ratio (AOR) = 2.28 (95% confidence interval (CI) 1.10, 4.74)), being single (AOR = 2.08 (95% CI 1.25, 3.48)), and being a farmer (AOR = 3.21 (95% CI 1.84, 5.61)) were associated with increased risk for suboptimal adherence. Similarly, alcohol intake (AOR = 3.31 (95% CI 2.14, 5.11)), missing clinic appointment (AOR = 5.73 (95% CI 3.76, 8.75)), having opportunistic infections (AOR = 2.86 (95% CI 1.67, 4.88)) and presence of comorbidities (AOR = 3.51 (95% CI 1.89, 6.53)) were associated with higher risk for suboptimal adherence. We observed lower rate of suboptimal adherence to ART following the implementation of test and treat strategy in Ethiopia. Various sociodemographic, clinical, and behavioral factors were found to be independent predictors of suboptimal adherence. The findings highlight the importance of person-centered adherence support based on individual characteristics.