CROI 2024 Abstract eBook

Abstract eBook

Poster Abstracts

informing future research and alternative approaches to define the added value of routine, near-real-time use of cluster analyses to benefit public health and disrupt HIV transmission. A strong academic-public health partnership, statewide epidemic representation, and comprehensive cluster inference tools are instrumental to achieve that goal. Antoine Chaillon 1 , Alan Wells 1 , Tom Chen 2 , Ravi Goyal 1 , Samantha Tweeten 3 , Sanjay R. Mehta 1 , Susan J Little 1 1 University of California San Diego, San Diego, CA, USA, 2 Harvard Pilgrim Health Care Institute, Boston, MA, USA, 3 Public Health Services - County of San Diego, San Diego, CA, USA Background: Molecular HIV surveillance (MHS) is routinely used by U.S. public health departments to monitor HIV transmission dynamics within populations and regions. We analyzed MHS data to identify potential drivers of transmission by investigating genetically related infections (transmission clusters). Methods: De-identified data were obtained from the epidemiology unit of the Health and Human Services Agency (HHSA) of San Diego County. A baseline HIV genetic network was inferred based on genetic distance of 0.5% (proxy for recent linkage) for all HIV diagnoses from 2006-2016, and links from newly diagnosed people with HIV (PWH) were added to the network from 2017-2023. The presence of a negative HIV test within 6 months of a new HIV diagnosis was used to indicate incident HIV infection. Cox proportional hazards models were used to identify factors associated with the rate of transmission cluster growth. These results were used to predict the possible impact of hypothetical interventions focused on attributes of clustering PWH. Results: Among 3,676 individuals with diagnosed HIV, 1,938 PWH had at least one HIV sequence, collected a mean of 109 days following the date of HIV diagnosis (IQR 36). Overall, 16% of sequences were linked in 115 clusters (median cluster size 2; range 2-9). Cluster growth was strongly associated with larger size of the cluster (HR 2.5; 95% CI: 2.1, 2.8), proportion of PWH in the cluster with incident infection (HR 3.0; 95% CI: 1.6, 5.4), and proportion with a bacterial sexually transmitted infection (STI, including gonorrhea and syphilis) within 1 year of HIV diagnosis (HR 1.7; 95% CI: 1.1, 2.6) in univariate analysis. Only larger cluster size (HR 2.3; 95% CI: 2.0, 2.7) and incident infections (HR 2.4; 95% CI: 1.4, 4.4) were still associated with cluster growth in multivariable analysis (Table). Proportion of PWH with unsuppressed viremia (VL>200 copies/ ml) in a cluster was not associated with growth of that cluster. None of the hypothetical interventions evaluated were shown to significantly reduce the predicted rate of cluster growth over the subsequent three years. Conclusion: This is the first use of public health data demonstrating that incident infection is a driver of community HIV cluster growth. No special testing for incident infection was required, only the reliance on previous negative HIV test results. In contrast to published reports, these data suggest that incident infection may drive ongoing community HIV transmissions.

surveillance estimates of higher community prevalence of HIV in San Francisco County than in Santa Clara County in 2021 (1334.1 and 190.6 PLWH per 100,000 population, respectively). We observed similar concentrations of HIV-1 NA in analyses with and without a reverse transcription step during PCR suggesting that most, if not all, of measured NA in wastewater is HIV-1 DNA rather than RNA. Conclusion: Our findings demonstrate the feasibility of monitoring HIV concentrations in communal wastewater and show good concordance with local surveillance data on HIV prevalence. Results from wastewater can be used to obtain information on HIV at a localized, community level and could serve as a complementary approach to existing HIV surveillance frameworks helping identify priority areas for intervention. Further work by our group will investigate dynamics of viral shedding in PLWH, develop assays for measuring antiretroviral drug-resistance in wastewater, and identify the optimal uses of wastewater surveillance of HIV-1.

1064 Incident HIV Infection Drives Community HIV Cluster Growth

Poster Abstracts

1063 Statewide Real-Time Integration of Molecular and Contact Tracing Data to Disrupt HIV Transmission Rami Kantor 1 , Jon Steingrimsson 1 , John Fulton 1 , Vlad Novitsky 1 , Mark Howison 2 , Fizza Gillani 1 , Lila Bhattarai 3 , Meghan MacAskill 3 , Joel Hague 1 , August Guang 1 , Aditya Khanna 1 , Casey Dunn 4 , Joseph Hogan 1 , Thomas Bertrand 3 , Utpala Bandy 3 1 Brown University, Providence, RI, USA, 2 Research Improving People's Lives, Providence, RI, USA, 3 Rhode Island Department of Health, Providence, RI, USA, 4 Yale University, New Haven, CT, USA Background: Tools beyond contact tracing are still needed to disrupt HIV transmission. Molecular cluster analysis helps stop outbreaks, but its precise benefit to routine public health actions is an existing knowledge gap. We hypothesized that integration of statewide molecular data with contact tracing by routinely re-interviewing new diagnoses who cluster molecularly will increase motivation and enhance contact tracing. Methods: To address the hypothesis we (1) built an academic-governmental partnership in Rhode Island (RI); (2) maximized statewide representation of HIV-1 pol sequences; (3) developed an automated bioinformatics pipeline to aggregate de-identified data across systems; and (4) used phylogenetic tools to infer clusters in near-real-time, identify all new diagnoses who cluster, routinely attempt to re-interview and inform them of clustering, and assess this intervention's impact (1st interviews of all RI new diagnoses occur before sequence availability). Results: In a 2-year (Jan '21-Dec '22) study, of 100 new RI diagnoses, 52 were in molecular clusters. Re-interviews were feasible for only 22/52 (42%), revealing an important gap. Of the 22, only one provided new data, rejecting our hypothesis. Persons were less engaged in re- vs. 1st interviews showing confusion, apprehensiveness and annoyance, raising concerns for damaged rapport with public health. For 15/52 (29%) 1st interviews were not feasible and they had a 'combined' interview and were therefore not eligible. The remaining 15/52 (29%) were unavailable for re-interview (unlocatable, refused, or out-of state). Substantially higher clustering (52 vs. 22) was seen with phylogenetic vs. distance-only methods, highlighting importance of statewide comprehensive analyses. Of 3,720 RI persons with HIV in 2004-2022, 688 (18%) had no sequence availability, more in earlier year diagnoses. Sequencing 118 of those missing resulted in 11 new clusters, 14/118 joined known clusters, 9/118 formed new clusters and 8 previously-unclustered persons joined clusters. Conclusion: Molecular epidemiology triggered re-interviewing of all new RI HIV diagnoses who cluster had no effect on HIV transmission disruption,

1065 Contribution of HIV Transmission Bursts to Future HIV Infections, United States Rachael Billock 1 , Anne Marie France 1 , Neeraja Saduvala 2 , Nivedha Panneer 1 , Alexandra M. Oster 1 , Camden J. Hallmark 1 , Joel O. Wertheim 3 1 Centers for Disease Control and Prevention, Atlanta, GA, USA, 2 SeKON Enterprise, Inc, Atlanta, GA, USA, 3 University of California San Diego, La Jolla, CA, USA Background: HIV clusters show heightened transmission rates and may contribute disproportionally to new infections. However, the influence of these periods of rapid transmission on future HIV infections and the populations that they affect are incompletely characterized. Methods: Using subtype B HIV pol sequences from the U.S. National HIV Surveillance System reported through 2022 for people with HIV (PWH) diagnosed during 2014–2019, we inferred separate, time-scaled phylogenetic trees with ETE3, FastTree2, and TreeTime for six geographic regions composed of 14 jurisdictions with >50% sequence completeness. We detected transmission bursts, defined as ≥3 connected internal nodes (or transmission events) in a

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