# Intersectional stigma among rural buprenorphine providers as a barrier to diffusion of harm reduction strategies and interventions to enhance engagement in treatment for HIV and opioid use disorders

> **NIH NIH R21** · WASHINGTON UNIVERSITY · 2022 · $256,396

## Abstract

Missouri (MO) is an Ending the HIV Epidemic (EHE) priority state because of the disproportionate occurrence of
HIV as well as poor rates of engagement along the HIV care continuum in rural areas. Intersecting stigmas
remain a significant barrier to ending the HIV epidemic in MO and other rural areas by impeding technical
capacity building and integration of comprehensive evidence-based harm reduction strategies and medication
treatment for opioid use disorders (MOUD) into rural primary care. Intersecting enacted drug, HIV and sexual
orientation stigmas are drivers of lower rates of engagement along the HIV care continuum among African
American people with HIV and OUD accessing care in Missouri. Intersecting enacted stigmas present barriers
to scaling up comprehensive harm reduction by impeding providers' willingness to see value in and compatibility
of integrating harm reduction strategies into their existing practices. Research is lacking that examines the role
of intersecting stigmas in shaping providers' decisions to write prescriptions for buprenorphine, naltrexone and
PrEP in Missouri. To address these gaps in the research, we will use this R21 Exploratory/Development award
to 1) identify mechanisms through which intersectional drug, HIV, and sexuality-based stigmas influence
buprenorphine providers' diffusion, acceptance and adoption of OUD and HIV prevention harm reduction
practices (Aim 1a); buprenorphine, naltrexone and PrEP prescribing (Aim 1b) using fixed-choice surveys
administered to 420 rural providers and; Aim 2) conduct in-depth interviews with key stakeholders to elucidate
multi-level intersectional stigmas at the individual-, provider-, clinic-, community and policy-levels, and possible
strategies to reduce stigma with (a) providers (n=30), (b) people living with HIV and OUD (n=24), (c) community
advocates (n=6) and (d) regional leaders in OUD and HIV infection, (n=6). The main hypothesis of this study is
that intersectional stigmas inhibit technical capacity building that is necessary for rural MO to offer patients high-
quality, comprehensive harm reduction services and integration of EBIs and keep them engaged in care. The
proposed application embeds the use of key stakeholders into part of the research process first through extensive
buy-in from 9 state associations and government agencies then through qualitative interviews to generate
avenues for possible intervention and sustainability. Funding for this grant application will break new ground in
rural MO and provide critical assistance to advancing the scaling up of harm reduction and treatment approaches
in a geographical area where services are lacking. The proposed research will lead to the development of a
stigma reduction intervention to increase adoption and diffusion of harm reduction strategies and buprenorphine
prescribing. Devising novel strategies is key to optimizing
and
treatment,
community-based treatments for opioid use disorders
 HIV infection among rural ...

## Key facts

- **NIH application ID:** 10548072
- **Project number:** 1R21DA057136-01
- **Recipient organization:** WASHINGTON UNIVERSITY
- **Principal Investigator:** Phillip L Marotta
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $256,396
- **Award type:** 1
- **Project period:** 2022-07-01 → 2024-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10548072

## Citation

> US National Institutes of Health, RePORTER application 10548072, Intersectional stigma among rural buprenorphine providers as a barrier to diffusion of harm reduction strategies and interventions to enhance engagement in treatment for HIV and opioid use disorders (1R21DA057136-01). Retrieved via AI Analytics 2026-06-02 from https://api.ai-analytics.org/grant/nih/10548072. Licensed CC0.

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