# Sexual network structure and HIV testing and treatment in sub-Saharan Africa: understanding the implications for ending the HIV epidemic.

> **NIH NIH K01** · PENNSYLVANIA STATE UNIV HERSHEY MED CTR · 2024 · $186,095

## Abstract

PROJECT SUMMARY
Sub-Saharan Africa (SSA) is disproportionately impacted by the HIV epidemic, with ongoing substantial HIV
transmission despite large reductions in new HIV infections. Despite clear evidence at the couple-level that HIV
test-and-treat strategies are successful at reducing HIV transmission, population-level trials have shown mixed
effectiveness of test-and-treat strategies to reduce HIV incidence. There is evidence of disparities in testing
and treatment engagement, particularly among marginalized populations. Furthermore, these disparities are
frequently along the same characteristics as sexual network clustering. Therefore, a potential explanation for a
smaller than expected incidence impact is sexual network clustering, partnering of like-with-like, by HIV testing
and treatment. Indeed, my preliminary work showed that couples in South Africa who reported never testing for
HIV were nearly 2 times more likely (1.9, 95% CI=1.8-2.0) to be partnered with one another than expected by
random chance. But this finding needs replication, as there is little empirical evidence to support these claims.
Through network analyses and simulations, contextualized with community interviews, this career development
award will assess whether sexual network clustering impacts the effectiveness of HIV treatment as prevention.
These results will inform population-level treatment targets. Further, they will allow me to test the utility of
network-driven strategies to drive testing and treatment and provide clear guidance to maximize the impact of
these interventions. Research aims will include: 1) Identify the network context of HIV test-and-treat
interventions in SSA by (A.) Characterizing the sexual network position of people engaged in test-and-treat,
and (B.) Estimating the level of sexual network clustering by test-and-treat; 2) Evaluate the impact of network-
driven strategies of HIV interventions using network models parametrized with data on engagement in HIV
test-and-treat and sexual network context; 3) Translate modeling and network analyses to real-world settings.
This proposal will leverage previously collected data from Uganda, Malawi, and Nigeria: the Rakai Community
Cohort Study, the TRUST/RV368 Study, Population-based HIV Impact Assessments, and the Likoma Network
Study, and collect original qualitative data in Rakai, Uganda. These sources include data on both “general
populations” and “key populations,” specifically fisherfolk and men who have sex with men and transgender
women, at increased risk of HIV infection. This work will build on my existing global collaborations, while
developing my expertise in network science and translation through a mentorship team with extensive
experience in network data collection and conducting community-informed work. Building on these findings,
future funding will be sought to collect expanded sexual network data and develop network-level intervention
strategies. My training plan will include gaining e...

## Key facts

- **NIH application ID:** 11009107
- **Project number:** 1K01MH135767-01A1
- **Recipient organization:** PENNSYLVANIA STATE UNIV HERSHEY MED CTR
- **Principal Investigator:** Kathryn Risher
- **Activity code:** K01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $186,095
- **Award type:** 1
- **Project period:** 2024-07-16 → 2028-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/11009107

## Citation

> US National Institutes of Health, RePORTER application 11009107, Sexual network structure and HIV testing and treatment in sub-Saharan Africa: understanding the implications for ending the HIV epidemic. (1K01MH135767-01A1). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/11009107. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*