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Organizing Committee: QAI 2025

2025 IEEE International Conference on Quantum Artificial Intelligence (QAI) · 2025

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<h4>Background</h4>We compared the performance of self-reports, pill counts, and electronic adherence measures in monitoring antiretroviral therapy (ART) adherence and association with viral suppression among adolescents living with HIV (ALHIV).<h4>Setting</h4>This study was conducted among 39 study clinics in the Greater Masaka region in Southern Uganda.<h4>Methods</h4>We used baseline data from 702 ALHIV aged 10-16 years receiving ART. Self-report was assessed using a three-item tool and pill counts were unannounced, while Wisepill captured device openings over 30 days. We determined the sensitivity, specificity, and area under the receiver-operator characteristic (ROC) curve for each adherence measure in discriminating ALHIV based on their viral suppression (<200 copies/mL) status. We also fit multilevel logistic regression models to determine the association between each adherence measure and viral suppression.<h4>Results</h4>On average, 73% of ALHIV reported good (≥90%) ART adherence, while 67.1% of ALHIV achieved viral suppression. We found disagreement between adherence measures, with agreement coefficient ranging between 0.410 (self-report vs. Wisepill) and 0.545 for pill counts vs. Wisepill. All 3 adherence measures had low ability to predict viral suppression, with AUCs ranging between 0.560 for pill counts and 0.616 for self-reported adherence. Only self-reported adherence had a significant relationship with viral suppression, with OR = 2.16 (95% CI: 1.25 to 3.81) and P = 0.006.<h4>Conclusions</h4>Substantial discrepancies exist across adherence measures. In this setting, low self-reported adherence can help in identifying adolescents at risk of virologic failure, although high self-reported adherence does not reliably exclude viremia.

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2025 IEEE International Conference on Quantum Artificial Intelligence (QAI)
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2025-01-01
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(2025). Organizing Committee: QAI 2025. 2025 IEEE International Conference on Quantum Artificial Intelligence (QAI). https://doi.org/10.1097/qai.0000000000003899
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