Super learner analysis of real-time electronically monitored adherence to antiretroviral therapy under constrained optimization and comparison to non-differentiated care approaches for persons living with HIV in rural Uganda.

Journal: Journal of the International AIDS Society
PMID: 32202067

Abstract

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.

Authors

  • Alejandra E Benitez
    Division of Biostatistics, School of Public Health, University of California Berkeley, Berkeley, CA, USA.
  • Nicholas Musinguzi
    Global Health Collaborative, Mbarara University of Science and Technology, Mbarara, Uganda.
  • David R Bangsberg
  • Mwebesa B Bwana
    Department of Internal Medicine, Mbarara University of Science & Technology, Mbarara, Uganda.
  • Conrad Muzoora
    Mbarara University of Science and Technology, Mbarara, Uganda.
  • Peter W Hunt
    Department of Medicine, the University of California, San Francisco, School of Medicine, San Francisco, California.
  • Jeffrey N Martin
    Departments of Epidemiology and Biostatistics, University of California at San Francisco, San Francisco, California, USA.
  • Jessica E Haberer
  • Maya L Petersen

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