Background: Despite the efficacy of antiretroviral therapy (ART), public health efforts to treat persons living with HIV must address issues with patient retention in order to achieve lasting epidemic control. People living in African countries with high rates of HIV infection face different barriers to retention, including structural barriers (e.g. transport to clinic), psychosocial barriers (e.g., stigma), and others (e.g., long waiting times). This diversity of barriers contains a critical implication: no single behavioral intervention will help all patients remain in care. Given the absence of a ?one-size-fits-all? solution to HIV treatment maintenance, Drs. Petersen and Geng (the current project's sponsors) co-lead a nearly completed NIH-funded trial (ADAPT-R; NCT02338739) that identifies adaptive strategies for patient retention. In ADAPT-R, HIV-positive patients initiating ART are randomized to a low-intensity intervention to prevent retention lapses. Patients are re-randomized to a high-intensity intervention to facilitate their return to care, only if initial retention is poor. By responding to a single aspect of individual patient behavior (days in treatment), this adaptive strategy is actually more efficient (patients succeeding avoid more costly interventions) and effective (patients not well-retained receive more intensive support) than intervention assignment strategies in traditional trials that assume ?one size fits all.? Approach: The current proposal uses recent advances in statistics, machine learning, and causal inference to develop an analysis plan that not only leverages, but also innovates, the parent trial. This project proposes to use ADAPT-R data to design algorithms that assign each patient his/her personalized interventions based on patient characteristics measured at baseline (e.g., demographics, distance from clinic, stigma) and over time (e.g., lapses in care, updated clinical data measures, past interventions). By making optimal use of all patient characteristics measured in ADAPT-R (i.e., not just early lapses in care) to assign interventions, it is hypothesized that these algorithms will be most efficient and effective at retaining patients, compared to assignment methods used in ADAPT-R and ?one-size- fits-all? traditional trials.
Specific Aims : This F31 Diversity grant aims to: 1) design and test an algorithm that assigns each patient a personalized low-intensity intervention for remaining in HIV care; 2) design and test an algorithm that assigns each patient a personalized low- and high-intensity intervention, in sequence, for remaining in HIV care. Impact: This project provides an opportunity to learn more effective ways of administering existing interventions to improve HIV retention in rural Africa. At the broadest level, this work aims to advance the toolkit for developing Precision Public Health strategies. Fellowship information: This project is the dissertation of Ms. Lina Montoya, a PhD student in Biostatistics at University of California, Berkeley. Ms. Montoya has chosen one primary sponsor and two co-sponsors with complementary expertise and collaboration in the fields of statistics, HIV, global health and implementation science. Her training will consist of 2 years of research, coursework, and professional development.
HIV-positive patients in sub-Saharan Africa face diverse barriers to receiving treatment; thus, there does not exist a ?one-size-fits-all? behavioral intervention to help patients remain in care. Leveraging state-of-the-art advances in machine learning and biostatistics, this project proposes to design and evaluate algorithms that assign individualized interventions to patients, in order to optimize patient retention in HIV care. At the broadest level, this work aims to advance the toolkit for developing effective Precision Public Health strategies.