# Site-randomized Trial of a Novel Social Network Recruitment Intervention to Locate More Undiagnosed Positive Cases of HIV, Increase HIV Testing among Men, and Reduce HIV-related Stigma in South Africa

> **NIH NIH R01** · UNIVERSITY OF ILLINOIS AT CHICAGO · 2024 · $689,321

## Abstract

PROJECT SUMMARY/ABSTRACT
 In South Africa's large generalized HIV epidemic, male-to-female transmission comprises a large
proportion of transmission events, but men are much less likely to seek HIV testing, and disproportionately
remain undiagnosed. HIV-related stigma is a key barrier to recruiting men to HIV testing in South Africa, as
they report feeling blamed by their partners and communities for HIV transmission. This barrier must be
addressed to increase testing among men and other testing-avoidant people in order to locate “hard-to-reach”
undiagnosed cases and make progress towards 95-95-95 goals. Peer recruitment via social networks is an
effective mechanism for promoting HIV testing, because people seek health information from peers, and peers
influence health behavior norms within networks. However, standard risk network recruitment is limited in that:
1) recruiting one's own risk partners can trigger stigma and blame for HIV; and 2) it excludes people who have
not engaged in HIV risk behavior recently and/or who no longer have contact with their risk partners. We
developed an expanded social network recruitment to HIV testing (E-SNRHT) intervention to address
these limitations and reduce stigma as a barrier to testing. E-SNRHT asks newly diagnosed HIV+ (NDP)
“seeds” (i.e., initial participants) to recruit their expanded social network members (i.e., anyone they know) who
they think could benefit from HIV testing or could be HIV+ unaware, tests these network members, and refers
them to ART (if positive) or follow-up testing (if negative). By asking participants to recruit non-risk partners, E-
SNRHT is designed to increase their comfort and likelihood of recruiting others, especially those who have
avoided testing due to stigma. Our two pilot studies of E-SNRHT found that it recruits men to HIV testing at
much higher rates than standard risk network recruitment; locates previously undiagnosed cases (NDP) at a
much higher rate per seed than standard risk network recruitment; and recruits people who have not tested in
years, have never tested, and/or have not engaged in HIV risk behavior recently but are HIV positive-unaware.
We also found that E-SNRHT reduces HIV-related stigma and increases HIV-related social support among
networks; and that 76% of E-SNRHT NDP started ART within 10 weeks. As participants recruit each other,
their discussions help to normalize talking about HIV, thereby improving levels of stigma and support, which in
turn should increase HIV service use and improve HIV care cascade outcomes. We will conduct a site-
randomized trial of E-SNRHT, with 32 Department of Health clinics in KwaZulu-Natal, South Africa serving as
sites. We will compare E-SNRHT clinics to business-as-usual control clinics on: their rates of recruiting men to
testing (Aim 1a) and locating NDP (1b); participants' reports of HIV-related stigma and social support (Aim 2a);
and treatment cascade outcomes (2b). We will also use implementation science m...

## Key facts

- **NIH application ID:** 10923360
- **Project number:** 1R01MH136871-01
- **Recipient organization:** UNIVERSITY OF ILLINOIS AT CHICAGO
- **Principal Investigator:** Leslie Denise Williams
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $689,321
- **Award type:** 1
- **Project period:** 2024-07-15 → 2029-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10923360

## Citation

> US National Institutes of Health, RePORTER application 10923360, Site-randomized Trial of a Novel Social Network Recruitment Intervention to Locate More Undiagnosed Positive Cases of HIV, Increase HIV Testing among Men, and Reduce HIV-related Stigma in South Africa (1R01MH136871-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10923360. Licensed CC0.

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