Characteristics and growth of the genetic HIV transmission network of Mexico City during 2020

Abstract

IntroductionMolecular surveillance systems could provide public health benefits to focus strategies to improve the HIV care continuum. Here, we infer the HIV genetic network of Mexico City in 2020, and identify actively growing clusters that could represent relevant targets for intervention.MethodsAll new diagnoses, referrals from other institutions, as well as persons returning to care, enrolling at the largest HIV clinic in Mexico City were invited to participate in the study. The network was inferred from HIV pol sequences, using pairwise genetic distance methods, with a locally hosted, secure version of the HIV‐TRACE tool: Seguro HIV‐TRACE. Socio‐demographic, clinical and behavioural metadata were overlaid across the network to design focused prevention interventions.ResultsA total of 3168 HIV sequences from unique individuals were included. One thousand and one‐hundred and fifty (36%) sequences formed 1361 links within 386 transmission clusters in the network. Cluster size varied from 2 to 14 (63% were dyads). After adjustment for covariates, lower age (adjusted odds ratio [aOR]: 0.37, p<0.001; >34 vs. <24 years), being a man who has sex with men (MSM) (aOR: 2.47, p = 0.004; MSM vs. cisgender women), having higher viral load (aOR: 1.28, p<0.001) and higher CD4+ T cell count (aOR: 1.80, p<0.001; ≥500 vs. <200 cells/mm3) remained associated with higher odds of clustering. Compared to MSM, cisgender women and heterosexual men had significantly lower education (none or any elementary: 59.1% and 54.2% vs. 16.6%, p<0.001) and socio‐economic status (low income: 36.4% and 29.0% vs. 18.6%, p = 0.03) than MSM. We identified 10 (2.6%) clusters with constant growth, for prioritized intervention, that included intersecting sexual risk groups, highly connected nodes and bridge nodes between possible sub‐clusters with high growth potential.ConclusionsHIV transmission in Mexico City is strongly driven by young MSM with higher education level and recent infection. Nevertheless, leveraging network inference, we identified actively growing clusters that could be prioritized for focused intervention with demographic and risk characteristics that do not necessarily reflect the ones observed in the overall clustering population. Further studies evaluating different models to predict growing clusters are warranted. Focused interventions will have to consider structural and risk disparities between the MSM and the heterosexual populations.

HIV Pretreatment Drug Resistance Trends in Mexico City, 2017–2020

“…Unexpectedly, the odds of having PDR were not specifically higher in cisgender women, as observed in previous studies in Mexico and in other countries [5,9]. In the context of Mexico City, contrasted with other regions of Mexico, the epidemic is highly concentrated in MSM [15] who are generally younger, have a higher education level, and arrive earlier to clinical care than the heterosexual population [16,17]. In this context, MSM had significantly higher odds of efavirenz/nevirapine PDR compared to heterosexual cisgender men, possibly reflecting ongoing transmission of NNRTI resistance mutations such as K103NS and E138A.…”

“…Sequences were obtained by next generation sequencing from a single amplicon including the complete protease (PR), reverse transcriptase (RT) and integrase (IN) genes (HXB2: PR 1-99, RT 1-560 and IN 1-288), using an in-house-validated method with Illumina sequencing technology on a MiSeq instrument (San Diego, CA, USA), as previously described [16,17]. A minority of the specimens in which amplification of this longer amplicon was not successful, as well as clinically indicated HIV genotypes for the ADR analysis, were amplified using a validated protocol developed by the US Centers for Disease Control and Prevention for the PR-RT region (HXB2 positions: PR 6-99, RT 1-251) [18] and an in-house developed and validated protocol for IN (HXB2: IN 1-288) [19].…”

“…The network was defined using a genetic matrix method based on PR-RT sequences, with Seguro HIV-TRAnsmission Cluster Engine (Seguro HIV-TRACE) [16], a locally adapted and secured version of the HIV-TRACE tool [28]. Clusters were defined when sequences showed pairwise Tamura-Nei 93 genetic distance < 1.5%.…”

HIV-1 drug resistance and genetic transmission network among newly diagnosed people living with HIV/AIDS in Ningbo, China between 2018 and 2021

“…The sequences were submitted to the Stanford University HIV Resistance Database (http://hivdb.stanford.edu/) to describe and interpret HIV-1 TDR. TDR level was classi ed according to the Stanford Penalty Score as high (60), intermediate , or low (15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29) to the following drugs: PI, Protease inhibitor; NRTI, Nucleoside reverse transcriptase inhibitor; NNRTI, Non-nucleoside reverse transcriptase inhibitor; NFV: Nel navir; ABC: Abacavir; AZT: Zidovudine; D4T: Stavudine; DDI: Didanosine; FTC: Emtricitabine; 3TC, Lamivudine; TDF: Tenofovir; DOR: Doravirine; EFV: Efavirenz; ETR: Etravirine; RPV: Rilpivirine. Drug resistance mutations were analyzed using the CRP tool (http://cpr.stanford.edu/cpr.cgi).…”

“…Genetic transmission network, which constructed based on the genetic information of people infected with HIV through gene distance between sequences, widely used in the study of early HIV cases detection, long-term monitoring of drug resistance and targeted precision intervention [14][15][16][17][18]. Real-time genetic transmission network have been improved and further combined with social transmission network to judge new transmission events, identify high-risk spreaders and potential infected individuals and evaluate the effect of intervention measures [19][20][21]. Clustering analyses based on HIV drug resistance surveillance would rapidly detect and respond to emerging clusters of HIV infection to further reduce new transmissions, which is one pillar of the ending the HIV Epidemic plan in the United States [22].…”