# Leveraging regulatory flexibility for methadone take-home dosing to improve retention in treatment for opioid use disorder: A stepped-wedge randomized trial to facilitate clinic level changes

> **NIH NIH R61** · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · 2022 · $598,795

## Abstract

Project Summary
Regulatory changes made during the COVID-19 public health emergency (PHE) that relaxed criteria for take-
home dosing (THD) of methadone offer an opportunity to improve retention in care with a lifesaving treatment.
Methadone is a highly effective medication for treating opioid use disorders (OUD) that is provided in opioid
treatment programs (OTPs). Yet, longstanding regulatory restrictions limit the availability of methadone as well
as create demands that heavily burden clients by requiring frequent visits to clinics. The rationale for these
regulations is to safeguard against diversion and overdoses from methadone. Yet, the history and application
of methadone regulations stem from stigmatized and racist notions of people with OUD. Most OTPs are
located within communities with predominantly Black/African American or Latinx populations. Consequently,
Black/African American and Latinx individuals have greater access to methadone than other, less restricted,
medications for OUD. Within OTPs, Black/African American and Latinx individuals are less likely to receive
adequate dosing levels of methadone and have lower retention than non-Hispanic White clients. More flexible
THD may help address disparities in care. Currently, there is a national debate about balancing safety
concerns over more flexible THD against the benefits of client retention and quality of life. Low offering of THD
in many OTPs suggests a need for new data-driven interventions to encourage changes in engrained clinical
workflows and long-standing stigmatizing beliefs about OUD clients. OTP leadership and staff express concern
about misapplying regulatory flexibility, of iatrogenic effects of greater THD, and about legal liability from
overdoses or diversion. Finally, financial concerns mount for organizations that have long based their business
models on billing for frequent in-person medication dispensing. This project stems from a well-established
academic-public partnership in New York State between the Office of Addiction Services and Supports
(OASAS) and research collaborators from New York University, Cornell University, and the University of
Connecticut. We propose a two-phased project to develop then test a multidimensional OTP intervention to
address clinical decision making, regulatory confusion, legal liability concerns, capacity for clinical practice
change, and financial barriers to THD. The intervention will include OTP THD specific dashboards drawn from
multiple State databases. The approach will be informed by the Health Equity Implementation Framework. In
phase 1 (R61), we will employ an explanatory sequential mixed method design to combine analysis of large
state administrative databases—Medicaid, treatment registry, THD reporting—with qualitative interviews to
refine the intervention. In phase 2 (R33), we will conduct a stepped-wedge trial with 36 OTPs (~10,800
Medicaid clients/yr) randomized to 6 cohorts of a six-month long clinic-level interve...

## Key facts

- **NIH application ID:** 10590040
- **Project number:** 1R61DA057683-01
- **Recipient organization:** NEW YORK UNIVERSITY SCHOOL OF MEDICINE
- **Principal Investigator:** Yuhua Bao
- **Activity code:** R61 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $598,795
- **Award type:** 1
- **Project period:** 2022-09-30 → 2023-09-29

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10590040

## Citation

> US National Institutes of Health, RePORTER application 10590040, Leveraging regulatory flexibility for methadone take-home dosing to improve retention in treatment for opioid use disorder: A stepped-wedge randomized trial to facilitate clinic level changes (1R61DA057683-01). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10590040. Licensed CC0.

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