# Applying Critical Race Theory to investigate the impact of COVID-19-related policy changes on racial/ethnic disparities in medication treatment for opioid use disorder

> **NIH NIH R01** · UNIVERSITY OF WASHINGTON · 2022 · $662,421

## Abstract

Opioid use disorder (OUD) is a common and often fatal chronic condition that can be effectively treated with
medications (MOUD). Agonists (methadone, buprenorphine) are first-line treatments that reduce overdose risk.
Black and Hispanic/Latinx patients are less likely to receive buprenorphine than non-Hispanic White patients.
This raises equity concerns, as buprenorphine may be safer, easier to access and less stigmatizing than
methadone for many patients. The novel coronavirus (COVID-19) pandemic resulted in increased flexibility in
the provision of MOUD, including telemedicine initiations for buprenorphine. These policies may reduce
existing disparities by lowering care barriers but could exacerbate disparities if they are not equally beneficial
across groups. COVID-19-related policy changes present an unprecedented opportunity to examine impacts of
a structural intervention—relaxed MOUD restrictions—on disparities generated by structural racism and
discrimination (SRD). Guided by Public Health Critical Race praxis, which posits that racial/ethnic disparities in
healthcare access are produced by SRD, this project will use mixed methods to evaluate how disparities in
MOUD access may have changed in response to COVID-19-related policies in the Veterans Health
Administration (VA), the nation's largest provider of substance use care, and how SRD contributes. Unequal
access to buprenorphine is a significant problem nationally—studies estimate that Black patients with OUD are
50-60% less likely to access buprenorphine compared to White patients with similar disparities observed
among Hispanic/Latinx patients. The proposed research can guide national and health-system-specific policy
decisions regarding the continuation of relaxed MOUD prescribing guidelines post- COVID-19 and where to
target resources to address SRD and its sequelae. Lessons learned from this historical event can influence
future MOUD policy and practice, and it is essential that the impact on disparities and mechanisms underlying
disparities be understood to optimize policy changes with regard to equity. This study aims to: 1) examine how
changes in receipt of MOUD and retention following COVID-19 MOUD policies differ between Black and
Hispanic/Latinx compared to non-Hispanic White patients with OUD; 2) examine how community-level
sequelae of structural racism influence pre/post COVID-19 changes in MOUD receipt for Black and
Hispanic/Latinx patients with OUD; and 3) qualitatively examine experiences of OUD care and perceptions of
implementation of COVID-19-related policies among a sample of Black and Hispanic/Latinx patients with OUD.
Aims 1-2 are observational cohort studies using national VA electronic health record (EHR) data for Black,
Hispanic/Latinx, and non-Hispanic White patients with OUD. Aim 3 uses a qualitative study design involving
semi-structured phone interviews with Black and Hispanic/Latinx VA patients with OUD. A stratified random
sample will be balanced on gender ...

## Key facts

- **NIH application ID:** 10473098
- **Project number:** 1R01DA056232-01
- **Recipient organization:** UNIVERSITY OF WASHINGTON
- **Principal Investigator:** Jessica Ann Chen
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $662,421
- **Award type:** 1
- **Project period:** 2022-04-01 → 2027-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10473098

## Citation

> US National Institutes of Health, RePORTER application 10473098, Applying Critical Race Theory to investigate the impact of COVID-19-related policy changes on racial/ethnic disparities in medication treatment for opioid use disorder (1R01DA056232-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10473098. Licensed CC0.

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