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Introduction: Real‐time electronic adherence monitoring (EAM) systems could inform on‐going risk assessment for HIV viraemia and be used to personalize viral load testing schedules. We evaluated the potential of real‐time EAM (transferred via cellular signal) and standard EAM (downloaded via USB cable) in rural Uganda to inform individually differentiated viral load testing strategies by applying machine learning approaches. Methods: We evaluated an observational cohort of persons living with HIV and treated with antiretroviral therapy (ART) who were monitored longitudinally with standard EAM from 2005 to 2011 and real‐time EAM from 2011 to 2015. Super learner, an ensemble machine learning method, was used to develop a tool for targeting viral load testing to detect viraemia (>1000 copies/ml) based on clinical (CD4 count, ART regimen), viral load and demographic data, together with EAM‐based adherence. Using sample‐splitting (cross‐validation), we evaluated area under the receiver operating characteristic curve (cvAUC), potential for EAM data to selectively defer viral load tests while minimizing delays in viraemia detection, and performance compared to WHO‐recommended testing schedules. Results: In total, 443 persons (1801 person‐years) and 485 persons (930 person‐years) contributed to standard and real‐time EAM analyses respectively. In the 2011 to 2015 dataset, addition of real‐time EAM (cvAUC: 0.88; 95% CI: 0.83, 0.93) significantly improved prediction compared to clinical/demographic data alone (cvAUC: 0.78; 95% CI: 0.72, 0.86; p = 0.03). In the 2005 to 2011 dataset, addition of standard EAM (cvAUC: 0.77; 95% CI: 0.72, 0.81) did not significantly improve prediction compared to clinical/demographic data alone (cvAUC: 0.70; 95% CI: 0.64, 0.76; p = 0.08). A hypothetical testing strategy using real‐time EAM to guide deferral of viral load tests would have reduced the number of tests by 32% while detecting 87% of viraemia cases without delay. By comparison, the WHO‐recommended testing schedule would have reduced the number of tests by 69%, but resulted in delayed detection of viraemia a mean of 74 days for 84% of individuals with viraemia. Similar rules derived from standard EAM also resulted in potential testing frequency reductions. Conclusions: Our machine learning approach demonstrates potential for combining EAM data with other clinical measures to develop a selective testing rule that reduces number of viral load tests ordered, while still identifying those at highest risk for viraemia. [ABSTRACT FROM AUTHOR]

This study demonstrates how the linear constrained optimization approach can be used to design a health benefits package (HBP) which maximises the net disability adjusted life years (DALYs) averted given the health system constraints faced by a country, and how the approach can help assess the marginal value of relaxing health system constraints. In the analysis performed for Uganda, 45 interventions were included in the HBP in the base scenario, resulting in a total of 26.7 million net DALYs averted. When task shifting of pharmacists' and nutrition officers' tasks to nurses is allowed, 73 interventions were included in the HBP resulting in a total of 32 million net DALYs averted (a 20% increase). Further, investing only $58 towards hiring additional nutrition officers' time could avert one net DALY; this increased to $60 and $64 for pharmacists and nurses respectively, and $100,000 for expanding the consumable budget, since human resources present the main constraint to the system., (© 2023 The Authors. Health Economics published by John Wiley & Sons Ltd.)