Peer-reviewed article
Predicting the individualized risk of poor adherence to ART medication among adolescents living with HIV in Uganda: the Suubi+Adherence study
Rachel Brathwaite, Fred M. Ssewamala, Torsten B. Neilands, Moses Okumu, Massy Mutumba et al. ·
Summary
Written by AI, not yet AICS's view · claude-haiku-4-5-20251001This study developed a predictive model to identify adolescents living with HIV in Uganda who are at risk of poor adherence to antiretroviral therapy (ART). Using data from 637 adolescents followed over several years, researchers identified five key factors that predict poor adherence: previous adherence history, self-efficacy, family cohesion, poverty, and study group assignment. The model showed acceptable ability to distinguish between adolescents likely to have good or poor adherence, suggesting it could help healthcare workers identify those needing early support.
Key points
- Five predictors of poor ART adherence were identified: adherence history, adherence self-efficacy, family cohesion, child poverty, and group assignment
- The prediction model showed acceptable discriminatory ability with area under the curve of 69.9 (95% CI: 62.7, 72.8)
- The model demonstrated good overall performance with no evidence of miscalibration
- Multi-level factors (individual, household, community, economic) were assessed to create the prediction tool
- Authors recommend further refinement and external validation before implementation as a screening tool in practice
Limitations
This study developed and internally validated a prediction model but did not test it on an independent external population, which limits generalizability. The model was developed in Uganda and may not be directly applicable to other settings with different contexts.
Abstract
INTRODUCTION: Achieving optimal adherence to antiretroviral therapy (ART) among adolescents living with HIV (ALWHIV) is challenging, especially in low-resource settings. To help accurately determine who is at risk of poor adherence, we developed and internally validated models comprising multi-level factors that can help to predict the individualized risk of poor adherence among ALWHIV in a resource-limited setting such as Uganda. METHODS: We used data from a sample of 637 ALWHIV in Uganda who participated in a longitudinal study, "Suubi+Adherence" (2012 to 2018). The model was developed using the Least Absolute Shrinkage and Selection Operator (LASSO) penalized regression to select the best subset of multi-level predictors (individual, household, community or economic-related factors) of poor adherence in one year's time using 10-fold cross-validation. Seventeen potential predictors included in the model were assessed at 36 months of follow-up, whereas adherence was assessed at 48 months of follow-up. Model performance was evaluated using discrimination and calibration measures. RESULTS: For the model predicting poor adherence, five of the 17 predictors (adherence history, adherence self-efficacy, family cohesion, child poverty and group assignment) were retained. Its ability to discriminate between individuals with and without poor adherence was acceptable; area under the curve (AUC) = 69.9; 95% CI: 62.7, 72.8. There was no evidence of possible areas of miscalibration (test statistic = 1.20; p = 0.273). The overall performance of the model was good. CONCLUSIONS: Our findings support prediction modelling as a useful tool that can be leveraged to improve outcomes across the HIV care continuum. Utilizing information from multiple sources, the risk prediction score tool applied here can be refined further with the ultimate goal of being used in a screening tool by practitioners working with ALWHIV. Specifically, the tool could help identify and provide early interventions to adolescents at the highest risk of poor adherence and/or viral non-suppression. However, further fine-tuning and external validation may be required before wide-scale implementation.