ArticleAIDS research and human retroviruses2021
Characterization of HIV Risk Behaviors and Clusters Using HIV-Transmission Cluster Engine Among a Cohort of Persons Living with HIV in Washington, DC.
Article in AIDS research and human retroviruses, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 10 citations in OpenAlex.
- Behavioral and Demographic Profiles of HIV Transmission and Exposure Networks in Florida: Network Analysis of HIV Contact Tracing Data.JMIR public health and surveillance · 2025Article
- Sequencing HIV Diagnostic Samples to Detect Genetic Clusters and Assess Sequence Coverage Gaps.Open forum infectious diseases · 2025Article
- Profile of Persons Recently Infected with HIV-1 in Belgium: New Insights to Tailor Prevention Efforts.AIDS and behavior · 2024Article
- Prevalence and risk factors for post-COVID conditions of COVID-19 among persons with HIV in Washington, DC.AIDS care · 2024Article
- Mixed Methods Analysis of Telehealth Experience, Satisfaction, and Quality of Care During the COVID Pandemic Among Persons with HIV in Washington, DC.AIDS and behavior · 2024Article
- Psychosocial impacts of the COVID-19 pandemic from a cross-sectional Survey of people living with HIV in Washington, DC.AIDS research and therapy · 2023Article
Corrections and comments
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Authors and funding
9 authors at 4 institutions in 1 country.
Funding
Abstract
Molecular epidemiology (ME) is one tool used to end the HIV epidemic in the United States. We combined clinical and behavioral data with HIV sequence data to identify any overlap in clusters generated from different sequence datasets; to characterize HIV transmission clusters; and to identify correlates of clustering among people living with HIV (PLWH) in Washington, District of Columbia (DC). First, Sanger sequences from DC Cohort participants, a longitudinal HIV study, were combined with next-generation sequences (NGS) from participants in a ME substudy to identify clusters. Next, demographic and self-reported behavioral data from ME substudy participants were used to identify risks of secondary transmission. Finally, we combined NGS from ME substudy participants with Sanger sequences in the DC Molecular HIV Surveillance database to identify clusters. Cluster analyses used HIV-Transmission Cluster Engine to identify linked pairs of sequences (defined as distance ≤1.5%). Twenty-eight clusters of ≥3 sequences (size range: 3-12) representing 108 (3%) participants were identified. None of the five largest clusters (size range: 5-12) included newly diagnosed PLWH. Thirty-four percent of ME substudy participants (
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