# The impact of alcohol use and alcohol-interactive toxicity beliefs on pre-exposure prophylaxis (PrEP) adherence among gay, bisexual, and other men who have sex with men: A social network approach

> **NIH NIH F31** · UNIVERSITY OF HOUSTON · 2022 · $37,425

## Abstract

PROJECT SUMMARY/ABSTRACT
 Gay, bisexual, and other men who have sex with men (GBM) in the U.S. experience striking health disparities
related to alcohol use and HIV. Heavy alcohol use is a significant threat to adherence to antiretrovirals (e.g.,
antiretroviral therapy [ART]) among GBM managing HIV. However, it is unclear if alcohol use impacts adherence
to Pre-Exposure Prophylaxis (PrEP) for preventing HIV. PrEP is used by >35% of U.S. GBM and can be taken
either daily or taken in sequence around a sexual event (i.e., event-driven PrEP). PrEP is a highly effective oral
antiretroviral but studies have demonstrated suboptimal adherence for both PrEP regimens. Heavy alcohol use
and specific beliefs related to mixing alcohol and PrEP may contribute to suboptimal adherence. For instance,
>60% of GBM interested in taking PrEP falsely believe that mixing alcohol with PrEP results in a toxic
combination (i.e., interactive toxicity beliefs). Like other erroneous beliefs (e.g., HIV is manmade), interactive
toxicity beliefs may diffuse across the social network and, subsequently, influence adherence. The proposed
study will employ an egocentric social network interview and a semi-weekly diary phase over five weeks to (1)
examine associations between heavy alcohol use and PrEP adherence, and (2) to test alcohol-interactive toxicity
beliefs and social network characteristics as risk factors for PrEP non-adherence. GBM (N=100) who are taking
PrEP will be recruited via brick-and-mortar entities (e.g., LGBTQ+ centers and bars), local health clinics, and
online advertising in Harris County, TX (i.e., the greater Houston area). At baseline, participants will complete
demographics and measures of interactive toxicity beliefs. Participants will also complete an egocentric social
network interview designed to obtain information on up to 20 alters (i.e., people in the social network). The
interview will capture alter expression of interactive toxicity beliefs, PrEP and alcohol use, closeness, trust,
overall connectedness, demographics, and norms. Participants will then complete five weeks of semi-weekly
diaries that assess alcohol use, PrEP use, and HIV risk behaviors. This research can inform future PrEP-related
counseling techniques and interventions aimed at correcting PrEP misconceptions and increasing adherence.
The project supports the 2017-2021 strategic plan of NIAAA by advancing research in two areas. First, the project
has the goal of identifying ways in which alcohol can influence the use of HIV-related medications, specifically
PrEP (Goal 1C). Second, the project may enhance the public health impact of NIAAA-supported research, by
focusing on how alcohol contributes to suboptimal PrEP adherence among a population with alcohol-related
disparities (Goal 5). Moreover, the project contributes to research on health disparities of minority populations,
which is a cross-cutting NIAAA research theme. Through this project, I will obtain the knowledge, skills, and
...

## Key facts

- **NIH application ID:** 10538253
- **Project number:** 1F31AA029633-01A1
- **Recipient organization:** UNIVERSITY OF HOUSTON
- **Principal Investigator:** Jace DeWayne Pierce
- **Activity code:** F31 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $37,425
- **Award type:** 1
- **Project period:** 2022-09-01 → 2024-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10538253

## Citation

> US National Institutes of Health, RePORTER application 10538253, The impact of alcohol use and alcohol-interactive toxicity beliefs on pre-exposure prophylaxis (PrEP) adherence among gay, bisexual, and other men who have sex with men: A social network approach (1F31AA029633-01A1). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10538253. Licensed CC0.

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