Integrating HIV Cluster Analysis in Everyday Public Health Practice: Lessons Learned From a Public Health--Academic Partnership.

BACKGROUND: The use of molecular HIV cluster analysis to supplement public health contact tracing has shown promise in addressing HIV outbreaks. However, the potential of HIV cluster analysis as an adjunct to daily, person-by-person HIV prevention efforts remains unknown. We documented lessons learned within a unique public health-academic partnership while guiding workaday HIV prevention efforts with near-real-time molecular cluster analysis. SETTING: A public health-academic partnership in the State of Rhode Island, the United States. METHODS: We recorded perceptions of our team of academicians and public health practitioners that were encountered in an 18-month study evaluating the integration of molecular cluster analysis with HIV contact tracing for public health benefit. The focus was on monthly conferences where molecular clustering of each new statewide diagnosis was discussed to facilitate targeted interventions and on attempted reinterviews of all newly HIV-diagnosed persons statewide whose HIV sequences clustered to increase partner naming. RESULTS: Three main themes emerged: First, multidisciplinary conferences are substantially beneficial for gleaning actionable inferences from integrating molecular cluster analysis and public health data. Second, universal reinterviews were perceived to potentially have negative consequences but may be selectively beneficial. Third, the translation of cluster analysis into public health action is hampered by jurisdictional surveillance boundaries and within-jurisdictional data silos, across which data sharing is problematic. CONCLUSIONS: Insights from a statewide public health-academic partnership support integration of molecular HIV cluster analyses with public health efforts, which can guide public health activities to prevent transmission while identifying substantial barriers to integration, informing continued research.

Acquired Human Immunodeficiency Virus Type 1 Drug Resistance in Rhode Island, USA, 2004-2021.

BACKGROUND: Human immunodeficiency virus type 1 (HIV-1) acquired drug resistance (ADR) compromises antiretroviral therapy (ART). METHODS: We aggregated all HIV-1 protease-reverse transcriptase-integrase sequences over 2004-2021 at the largest HIV center in Rhode Island and evaluated ADR extent, trends, and impact using Stanford Database tools. Trends were measured with Mann-Kendall statistic, and multivariable regressions evaluated resistance predictors. RESULTS: Sequences were available for 914 ART-experienced persons. Overall ADR to any drug decreased from 77% to 49% (-0.66 Mann-Kendall statistic); nucleoside reverse transcriptase inhibitors 65% to 32%, nonnucleoside reverse transcriptase inhibitors 53% to 43%, and protease inhibitors 28% to 7% (2004-2021), and integrase strand transfer inhibitors 16% to 13% (2017-2021). Multiclass resistance decreased from 44% to 12% (2-class) and 12% to 6% (3-class). In 2021, 94% had at least one 3-drug or 2-drug one-pill-once-daily (OPOD) option. Males and those exposed to more ART regimens were more likely to have ≥2-class resistance, and higher regimen exposure was also associated with fewer OPOD options. CONCLUSIONS: Comprehensive analyses within a densely-sampled HIV epidemic over 2004-2021 demonstrated decreasing ADR. Continued ADR monitoring is important to maintain ART success, particularly with rising INSTI use in all lines of therapy and 2-drug and long-acting formulations.

An Automated Bioinformatics Pipeline Informing Near-Real-Time Public Health Responses to New HIV Diagnoses in a Statewide HIV Epidemic.

Molecular HIV cluster data can guide public health responses towards ending the HIV epidemic. Currently, real-time data integration, analysis, and interpretation are challenging, leading to a delayed public health response. We present a comprehensive methodology for addressing these challenges through data integration, analysis, and reporting. We integrated heterogeneous data sources across systems and developed an open-source, automatic bioinformatics pipeline that provides molecular HIV cluster data to inform public health responses to new statewide HIV-1 diagnoses, overcoming data management, computational, and analytical challenges. We demonstrate implementation of this pipeline in a statewide HIV epidemic and use it to compare the impact of specific phylogenetic and distance-only methods and datasets on molecular HIV cluster analyses. The pipeline was applied to 18 monthly datasets generated between January 2020 and June 2022 in Rhode Island, USA, that provide statewide molecular HIV data to support routine public health case management by a multi-disciplinary team. The resulting cluster analyses and near-real-time reporting guided public health actions in 37 phylogenetically clustered cases out of 57 new HIV-1 diagnoses. Of the 37, only 21 (57%) clustered by distance-only methods. Through a unique academic-public health partnership, an automated open-source pipeline was developed and applied to prospective, routine analysis of statewide molecular HIV data in near-real-time. This collaboration informed public health actions to optimize disruption of HIV transmission.

Not all clusters are equal: dynamics of molecular HIV-1 clusters in a statewide Rhode Island epidemic.

OBJECTIVES: Molecular epidemiology is a powerful tool to characterize HIV epidemics and prioritize public health interventions. Typically, HIV clusters are assumed to have uniform patterns over time. We hypothesized that assessment of cluster evolution would reveal distinct cluster behavior, possibly improving molecular epidemic characterization, towards disrupting HIV transmission. DESIGN: Retrospective cohort. METHODS: Annual phylogenies were inferred by cumulative aggregation of all available HIV-1 pol sequences of individuals with HIV-1 in Rhode Island (RI) between 1990 and 2020, representing a statewide epidemic. Molecular clusters were detected in annual phylogenies by strict and relaxed cluster definition criteria, and the impact of annual newly-diagnosed HIV-1 cases to the structure of individual clusters was examined over time. RESULTS: Of 2153 individuals, 31% (strict criteria) - 47% (relaxed criteria) clustered. Longitudinal tracking of individual clusters identified three cluster types: normal, semi-normal and abnormal. Normal clusters (83-87% of all identified clusters) showed predicted growing/plateauing dynamics, with approximately three-fold higher growth rates in large (15-18%) vs. small (∼5%) clusters. Semi-normal clusters (1-2% of all clusters) temporarily fluctuated in size and composition. Abnormal clusters (11-16% of all clusters) demonstrated collapses and re-arrangements over time. Borderline values of cluster-defining parameters explained dynamics of non-normal clusters. CONCLUSIONS: Comprehensive tracing of molecular HIV clusters over time in a statewide epidemic identified distinct cluster types, likely missed in cross-sectional analyses, demonstrating that not all clusters are equal. This knowledge challenges current perceptions of consistent cluster behavior over time and could improve molecular surveillance of local HIV epidemics to better inform public health strategies.

Beyond HIV outbreaks: protocol, rationale and implementation of a prospective study quantifying the benefit of incorporating viral sequence clustering analysis into routine public health interventions.

INTRODUCTION: HIV continues to have great impact on millions of lives. Novel methods are needed to disrupt HIV transmission networks. In the USA, public health departments routinely conduct contact tracing and partner services and interview newly HIV-diagnosed index cases to obtain information on social networks and guide prevention interventions. Sequence clustering methods able to infer HIV networks have been used to investigate and halt outbreaks. Incorporation of such methods into routine, not only outbreak-driven, contact tracing and partner services holds promise for further disruption of HIV transmissions. METHODS AND ANALYSIS: Building on a strong academic-public health collaboration in Rhode Island, we designed and have implemented a state-wide prospective study to evaluate an intervention that incorporates real-time HIV molecular clustering information with routine contact tracing and partner services. We present the rationale and study design of our approach to integrate sequence clustering methods into routine public health interventions as well as related important ethical considerations. This prospective study addresses key questions about the benefit of incorporating a clustering analysis triggered intervention into the routine workflow of public health departments, going beyond outbreak-only circumstances. By developing an intervention triggered by, and incorporating information from, viral sequence clustering analysis, and evaluating it with a novel design that avoids randomisation while allowing for methods comparison, we are confident that this study will inform how viral sequence clustering analysis can be routinely integrated into public health to support the ending of the HIV pandemic in the USA and beyond. ETHICS AND DISSEMINATION: The study was approved by both the Lifespan and Rhode Island Department of Health Human Subjects Research Institutional Review Boards and study results will be published in peer-reviewed journals.