An Adaptive Strategy for Preventing and Treating Lapses of Retention in Adult HIV Care II (ADAPT-R II)

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Abstract This R34 application is best understood in the context of both a NIH-funded sequential multiple assignment randomized trial (SMART) “Adaptive Strategies to Prevent and Treat Lapses of Retention (ADAPT-1)” nearing completion and a future trial (ADAPT-3) motivated by observations from the ADAPT-1. Retention in HIV treatment over long periods of time represents an archetypal complex public health problem and requires innovative solutions. The diversity of intensities and types of barriers to engagement mean that no single intervention is needed by all nor will work for all in need. For example, counseling could help a patient experiencing stigma, but will not help an individual who wants to come but cannot afford transportation. To respond to this conundrum, we carried out a SMART (ADAPT-1) to test a family of adaptive retention strategies. By maintaining lower intensity interventions in those doing well, adaptive strategies optimize efficiency, while escalating in those not doing well enhances effectiveness. In ADAPT-1, we initially randomized patients to one of three lower intensity interventions (standard of care (SOC), SMS messages and a conditional cash transfer). Only those who fail to be consistently retained are re-randomized to one of three more intensive interventions (SOC outreach, SMS message with a conditional cash transfer, or a navigator). Emerging ADAPT-1 results (in forthcoming publications) confirm our original hypothesis that pegging the retention intervention to patient behavior improves outcomes, the study also revealed additional opportunities to extend a “precision public health” paradigm. Specifically, we observed that different patients (based on sociodemographic, clinical and laboratory characteristics) respond differentially to different adaptive retention strategies. This observation begs a further hypothesis: use of predictive analytics (optimized with cutting-edge machine learning techniques) to distribute each intervention (e.g., SOC, cash transfer, SMS) to those patients most likely to respond to that intervention can achieve further gains in effectiveness and efficiency over any single sequenced retention strategy, even if strategy is itself already adaptive. We plan a future R01 application to test a machine learning based distribution of retention interventions as compared to best single sequential adaptive interventions (from ADAPT-1). To prepare for the novel trial, we propose this R34, to (1) develop and test the information technology basis for delivering on-demand predictions to health care workers in the field, (2) refine the statistical foundations of machine learning ability to predict through simulations and (3) assess the fit of machine learning based recommendations in the organizational, policy and ethical context of health systems in Kenya.