# Harnessing Bioinformatics for HIV Prevention: Understanding Persistence in Comprehensive HIV Prevention Services

> **NIH NIH K23** · COLUMBIA UNIVERSITY HEALTH SCIENCES · 2022 · $191,976

## Abstract

Project Summary/Abstract:
Pre-exposure prophylaxis (PrEP) is effective at reducing the acquisition of HIV; however, achieving the full
impact of this intervention is contingent on maintaining engagement in care throughout periods of high and low
risk. Currently, in the United States, only 270,000 out of 1.2 million individuals with indications for PrEP receive
it. Additionally, even for those who start PrEP, adherence, and persistence in comprehensive HIV prevention
care is poor, characterized by multiple discontinuations and restarts. Furthermore, HIV risk changes over time
and individual-level reasons for engagement, very early disengagement (<2 visits after initiation), early
disengagement (2-4 visits after initiation), and late disengagement (>4 visits after initiation) are not well
described; social and behavioral determinants of health (SBDH) like insurance, housing status, substance use,
and mental health, are increasingly recognized as key factors. As PrEP programs expand to meet the
projected need, the cost of providing support services will be substantial and thus identifying patients at highest
risk of loss-to-follow-up and selecting optimal services to support persistence in care and PrEP adherence is
essential. Furthermore, many individuals receiving HIV prevention services are known to receive highly
fragmented care typified by multiple providers and institutions, thereby creating a challenge to accurately
characterize persistence in care. Guided by the information-motivation-behavioral skills (IMB) model, we
propose to address this critical area of research by focusing on factors that are associated with very early,
early, and late disengagement from HIV prevention care. We will identify baseline factors through a
comprehensive questionnaire conducted at the time of enrollment into comprehensive HIV prevention care. We
will layer onto that longitudinal factors, such as social and behavioral determinants of health (SBDH), which
can vary over time, to get a more comprehensive and precise picture of factors affecting persistence in
prevention care. To address the issue of fragmented care and better characterize persistence in care, we will
utilize information contained in a large Health Information Exchange (HIE), Healthix, to capture all health care
visits in New York. We propose to use machine-learning methods to design predictive models of
disengagement from HIV prevention care. This comprehensive assessment of persistence in HIV prevention
care will inform the development of high quality, scalable models of HIV prevention care, making it possible to
target limited resources towards individuals at the highest risk of disengagement.

## Key facts

- **NIH application ID:** 10468000
- **Project number:** 5K23AI150378-03
- **Recipient organization:** COLUMBIA UNIVERSITY HEALTH SCIENCES
- **Principal Investigator:** Jason Zucker
- **Activity code:** K23 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $191,976
- **Award type:** 5
- **Project period:** 2020-08-01 → 2025-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10468000

## Citation

> US National Institutes of Health, RePORTER application 10468000, Harnessing Bioinformatics for HIV Prevention: Understanding Persistence in Comprehensive HIV Prevention Services (5K23AI150378-03). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10468000. Licensed CC0.

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