# Characterizing the Philippines HIV Epidemic:  A Community-Informed Approach to Examine Substance use, HIV RIsk, and HIV Prevention Services Uptake among Transgender Women and Men Who Have Sex with Men

> **NIH NIH R36** · BROWN UNIVERSITY · 2020 · $52,445

## Abstract

PROJECT SUMMARY/ABSTRACT
 Substance use and condomless same-sex are two predominant modes of HIV transmission in the
Philippines, where one of the world’s fastest growing epidemics is taking place. Clans of transgender women
(TW) and men who have sex with men (MSM) are highly impacted and account for four-fifths (80%) of new
cases per year. However, coverage of HIV prevention (HIV-P) and harm reduction services (e.g., HIV testing,
condoms, Pre- and non-occupational Post-Exposure Prophylaxes, and needle and syringe exchange
programs) in the Philippines is sub-par, with only a-third of TW and MSM individuals being reached by HIV-P
programs. This low uptake of HIV-P services partially stems from the currently limited research in this context
and setting, as well as the underexplored social and cultural factors such as “clan memberships” and clan
activities that could be facilitate protective factors (e.g., social support, sharing of HIV/drug-related information)
as well as risk (e.g., inconsistent condom use, and sex with multiple partners across and within clan members),
and multiple structural policies and healthcare-related barriers that impact uptake of HIV-P and harm reduction
services.
 To fill in this gap in HIV prevention and lay the groundwork for future studies in the Philippines, the current
dissertation proposal builds on my formative, community-informed qualitative research, and aims to
characterize substance use, co-occurring HIV-risk behaviors, and uptake of HIV-P services on a sample of
sexually active, HIV-negative TW and MSM clan members who are living in Manila. Specifically, this study will
use an integrated Socio-Ecological Model and Situated Information-Motivation-Behavioral skills Model, with
principles based on community-informed research, to (1) assess via multiple regression and structural equational
modeling the substance use, HIV-risk behaviors, and other multi-level socio-ecological risk and predictors of uptake
of HIV-P services using secondary data from Project Lakambini, the largest online survey of Filipina/o TW and
MSM clans in Manila to date (n=300, 35% self-identifying as TW); and (2) qualitatively identify and characterize via
Pagkikipagkwentuhan (story sharing), an indigenous Filipino qualitative method, to situate the context of TW and
MSM clan members’ substance use-related informational knowledge, motivations, and behavioral skills (IMB)
across social, personal, and structural factors that impact uptake of HIV-P services within and across of Filipina/o
clans members through 4 focus groups (5-8 participants per group; approx. 16 TW and 16 MSM clan members).
 The study results will provide evidence for the kinds of social context-specific (e.g, clan memberships) risk
and protective factors that influence this important behavioral outcome (i.e., uptake of HIV-P services) in preventing
HIV infection among clans of TW and MSM in Manila. A better understanding of how to improve uptake of HIV-P
services can help inf...

## Key facts

- **NIH application ID:** 9962358
- **Project number:** 5R36DA048682-02
- **Recipient organization:** BROWN UNIVERSITY
- **Principal Investigator:** Arjee Javellana Restar
- **Activity code:** R36 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $52,445
- **Award type:** 5
- **Project period:** 2019-07-01 → 2022-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9962358

## Citation

> US National Institutes of Health, RePORTER application 9962358, Characterizing the Philippines HIV Epidemic:  A Community-Informed Approach to Examine Substance use, HIV RIsk, and HIV Prevention Services Uptake among Transgender Women and Men Who Have Sex with Men (5R36DA048682-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/9962358. Licensed CC0.

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