# Evaluating and Optimizing Care for Opioid Use Disorder using a Structured Data-Science Approach

> **NIH NIH K99** · YALE UNIVERSITY · 2024 · $160,520

## Abstract

PROJECT SUMMARY/ABSTRACT
Opioid use disorder (QUO) is a public health challenge that affects millions of people worldwide. Evidence
supporting the efficacy of three FDA-approved medications for OUD (MOUD), including methadone,
buprenorphine and naltrexone, is well established. MOUD also constitutes an important intervention in patients
who are dually diagnosed with OUD and HIV infection; successful use of MOUD is associated with improved
HIV viral suppression. However, most patients with OUD remain untreated. Expanding access to MOUD has
been hampered by various challenges in the cascade of OUD care (i.e., diagnosis, treatment initiation and
retention, and recovery). Important questions remain about which factors may enhance retention in treatment
and reduce both dropout from and cycling on and off MOUD therapy. Furthermore, enhancing knowledge
about real-world effectiveness and guiding optimal use of MOUD in the clinical care of patients is also of
critical importance. This proposal will use data from the Veterans Health Administration, the largest integrated
healthcare system in the United States, along with rigorous analytic techniques, to address this urgent
knowledge gap centering around OUD care. In Aim 1 (K99), I will characterize dynamic cascades of care
among patients with OUD and among patients with both OUD and HIV, and identify predictors of treatment
engagement, disengagement, and re-entry into treatment. In Aim 2 (K99/R00), I will evaluate real-world
effectiveness of clinical management strategies on OUD and HIV, including the effect of buprenorphine
prescription in primary care settings versus in substance use specialty settings on OUD outcomes, and the
effect of MOUDs on HIV viral suppression among patients with both OUD and HIV. In Aim 3 (R00), I will
estimate optimal treatment strategies for MOUD to guide personalized treatment decisions. This proposed
research will provide a structured model for evaluation and optimization of OUD care.
I received my Ph.D. in Epidemiology from the University of North Carolina Gillings School of Global Public
Health, with research focus on HIV/AIDS. I am currently a postdoctoral associate in the Department of
Epidemiology of Microbial Diseases at the Yale School of Public Health. The career development plan, which
aligns with the research proposal and a multidisciplinary mentorship team, outlines a comprehensive strategy
for acquiring expert knowledge and analytical skills for my research career. Specifically, I will receive training
in: 1) science in substance use research and pharmacoepidemiology, 2) expert knowledge on the intersection
of OUD and HIV, 3) skills in dynamic modelling and stochastic processes, and 4) integration of machine
learning methods and causal inference theory. Successful completion of this training plan will provide me with
skills and knowledge necessary to become an independent investigator and epidemiologist studying infectious
diseases and substance use with advanced c...

## Key facts

- **NIH application ID:** 10770542
- **Project number:** 5K99DA057487-02
- **Recipient organization:** YALE UNIVERSITY
- **Principal Investigator:** Haidong Lu
- **Activity code:** K99 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $160,520
- **Award type:** 5
- **Project period:** 2023-02-01 → 2025-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10770542

## Citation

> US National Institutes of Health, RePORTER application 10770542, Evaluating and Optimizing Care for Opioid Use Disorder using a Structured Data-Science Approach (5K99DA057487-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10770542. Licensed CC0.

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