Longitudinal typing of molecular HIV clusters in a statewide epidemic. (1st September 2021) - Novitsky, Vlad; Steingrimsson, Jon; Howison, Mark; Dunn, Casey; Gillani, Fizza S.; Manne, Akarsh; Li, Yuanning; Spence, Matthew; Parillo, Zoanne; Fulton, John; Marak, Theodore; Chan, Philip; et al.
Abstract : Background: HIV molecular epidemiology is increasingly integrated into public health prevention. We conducted cluster typing to enhance characterization of a densely sampled statewide epidemic towards informing public health. Methods: We identified HIV clusters, categorized them into types, and evaluated their dynamics between 2004 and 2019 in Rhode Island. We grouped sequences by diagnosis year, assessed cluster changes between paired phylogenies, t 0 and t 1, representing adjacent years and categorized clusters as stable (cluster in t 0 phylogeny = cluster in t 1 phylogeny) or unstable (cluster in t 0 ≠ cluster in t 1 ). Unstable clusters were further categorized as emerging ( t 1 phylogeny only) or growing (larger in t 1 phylogeny). We determined proportions of each cluster type, of individuals in each cluster type, and of newly diagnosed individuals in each cluster type, and assessed trends over time. Results: A total of 1727 individuals with available HIV-1 subtype B pol sequences were diagnosed in Rhode Island by 2019. Over time, stable clusters and individuals in them dominated the epidemic, increasing over time, with reciprocally decreasing unstable clusters and individuals in them. Conversely, proportions of newly diagnosed individuals in unstable clusters significantly increased. Within unstable clusters, proportions of emerging clusters and of individuals in them declined; whereas proportions of newly diagnosed individuals in growing clusters significantly increased over time. Conclusion: Distinct molecular cluster types were identified in the Rhode Island epidemic. Cluster dynamics demonstrated increasing stable and decreasing unstable clusters driven by growing, rather than emerging clusters, suggesting consistent in-state transmission networks. Cluster typing could inform public health beyond conventional approaches and direct interventions. Abstract : Supplemental Digital Content is available in the text