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Additionally, we assessed clustering potential according to time since <jats:styled-content style=\"fixed-case\">HIV<\/jats:styled-content> acquisition.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Individuals with a recent <jats:styled-content style=\"fixed-case\">HIV<\/jats:styled-content> diagnosis at the largest <jats:styled-content style=\"fixed-case\">HIV<\/jats:styled-content> clinic in Mexico City were invited to participate, completing self\u2010administered questionnaires on sociodemographic, clinical, and behavioural characteristics. Blood samples were collected for analysis of the <jats:styled-content style=\"fixed-case\">HIV<\/jats:styled-content> <jats:italic>pol<\/jats:italic> gene using next\u2010generation sequencing. The stage of infection at diagnosis was determined using an algorithm that includes antibody avidity tests. Genetic transmission network analysis used the Seguro <jats:styled-content style=\"fixed-case\">HIV<\/jats:styled-content>\u2010<jats:styled-content style=\"fixed-case\">TRACE<\/jats:styled-content> tool.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Of 6703 participants, 561 (8.4%) were identified as people newly living with <jats:styled-content style=\"fixed-case\">HIV<\/jats:styled-content> (<jats:styled-content style=\"fixed-case\">PNLH<\/jats:styled-content>). Transmission network analysis identified 896 clusters; 30.2% had at least one <jats:styled-content style=\"fixed-case\">PNLH<\/jats:styled-content>. Among all individuals, 43.5% formed clusters, with 11.8% being <jats:styled-content style=\"fixed-case\">PNLH<\/jats:styled-content>. <jats:styled-content style=\"fixed-case\">PNLH<\/jats:styled-content> added to a cluster showed higher odds for higher education, engaging in commercial sex, use of dating apps, annual <jats:styled-content style=\"fixed-case\">HIV<\/jats:styled-content> screening, and engaging in high\u2010risk sexual practices (<jats:italic>p<\/jats:italic>\u2009&lt;\u20090.05). Clusters with <jats:styled-content style=\"fixed-case\">PNLH<\/jats:styled-content> exhibited greater growth rates than those without <jats:styled-content style=\"fixed-case\">PNLH<\/jats:styled-content> (<jats:italic>p<\/jats:italic>\u2009&lt;\u20090.05).<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>The presence of <jats:styled-content style=\"fixed-case\">PNLH<\/jats:styled-content> in clusters was associated with a higher growth rate. Tailored prevention strategies are crucial, including using dating apps for risk communication, promoting <jats:styled-content style=\"fixed-case\">PrEP<\/jats:styled-content> use, and safe sexual practices in sex venues, and enhancing harm reduction related to drug use. <jats:styled-content style=\"fixed-case\">PNLH<\/jats:styled-content> could be key candidates for interventions aimed at breaking transmission chains, including contact tracing.<\/jats:p><\/jats:sec>","DOI":"10.1111\/hiv.70029","type":"journal-article","created":{"date-parts":[[2025,5,8]],"date-time":"2025-05-08T01:09:48Z","timestamp":1746666588000},"page":"1046-1059","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["<scp>HIV<\/scp> molecular network in Mexico City (2021\u20132022): Understanding transmission dynamics through the role of newly diagnosed 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