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Background: Evidence on the distribution of pre-treatment HIV-1 drug resistance (HIVDR) among risk groups is limited in Africa. We assessed the prevalence, trends and transmission dynamics of pre-treatment HIVDR within and between MSM, people who inject drugs (PWID), female sex workers (FSWs), heterosexuals (HETs) and perinatally infected children in Kenya., Methods: HIV-1 partial pol sequences from antiretroviral-naive individuals collected from multiple sources between 1986 and 2020 were used. Pre-treatment reverse transcriptase inhibitor (RTI), PI and integrase inhibitor (INSTI) mutations were assessed using the Stanford HIVDR database. Phylogenetic methods were used to determine and date transmission clusters., Results: Of 3567 sequences analysed, 550 (15.4%, 95% CI: 14.2-16.6) had at least one pre-treatment HIVDR mutation, which was most prevalent amongst children (41.3%), followed by PWID (31.0%), MSM (19.9%), FSWs (15.1%) and HETs (13.9%). Overall, pre-treatment HIVDR increased consistently, from 6.9% (before 2005) to 24.2% (2016-20). Among HETs, pre-treatment HIVDR increased from 6.6% (before 2005) to 20.2% (2011-15), but dropped to 6.5% (2016-20). Additionally, 32 clusters with shared pre-treatment HIVDR mutations were identified. The majority of clusters had R0 ≥ 1.0, indicating ongoing transmissions. The largest was a K103N cluster involving 16 MSM sequences sampled between 2010 and 2017, with an estimated time to the most recent common ancestor (tMRCA) of 2005 [95% higher posterior density (HPD), 2000-08], indicating propagation over 12 years., Conclusions: Compared to HETs, children and key populations had higher levels of pre-treatment HIVDR. Introduction of INSTIs after 2017 may have abrogated the increase in pre-treatment RTI mutations, albeit in the HET population only. Taken together, our findings underscore the need for targeted efforts towards equitable access to ART for children and key populations in Kenya., (© The Author(s) 2023. Published by Oxford University Press on behalf of British Society for Antimicrobial Chemotherapy.)

HIV-1 transmission dynamics involving men who have sex with men (MSM) in Africa are not well understood. We investigated the rates of HIV-1 transmission between MSM across three regions in Kenya: Coast, Nairobi, and Nyanza. We analyzed 372 HIV-1 partial pol sequences sampled during 2006-2019 from MSM in Coast ( N = 178, 47.9%), Nairobi ( N = 137, 36.8%), and Nyanza ( N = 57, 15.3%) provinces in Kenya. Maximum-likelihood (ML) phylogenetics and Bayesian inference were used to determine HIV-1 clusters, evolutionary dynamics, and virus migration rates between geographic regions. HIV-1 sub-subtype A1 (72.0%) was most common followed by subtype D (11.0%), unique recombinant forms (8.9%), subtype C (5.9%), CRF 21A2D (0.8%), subtype G (0.8%), CRF 16A2D (0.3%), and subtype B (0.3%). Forty-six clusters (size range 2-20 sequences) were found-half (50.0%) of which had evidence of extensive HIV-1 mixing among different provinces. Data revealed an exponential increase in infections among MSM during the early-to-mid 2000s and stable or decreasing transmission dynamics in recent years (2017-2019). Phylogeographic inference showed significant (Bayes factor, BF > 3) HIV-1 dissemination from Coast to Nairobi and Nyanza provinces, and from Nairobi to Nyanza province. Strengthening HIV-1 prevention programs to MSM in geographic locations with higher HIV-1 prevalence among MSM (such as Coast and Nairobi) may reduce HIV-1 incidence among MSM in Kenya., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Nduva, Otieno, Kimani, McKinnon, Cholette, Sandstrom, Graham, Price, Smith, Bailey, Hassan, Esbjörnsson and Sanders.)

In Kenya, HIV-1 key populations including men having sex with men (MSM), people who inject drugs (PWID) and female sex workers (FSW) are thought to significantly contribute to HIV-1 transmission in the wider, mostly heterosexual (HET) HIV-1 transmission network. However, clear data on HIV-1 transmission dynamics within and between these groups are limited. We aimed to empirically quantify rates of HIV-1 flow between key populations and the HET population, as well as between different geographic regions to determine HIV-1 'hotspots' and their contribution to HIV-1 transmission in Kenya. We used maximum-likelihood phylogenetic and Bayesian inference to analyse 4058 HIV-1 pol sequences (representing 0.3 per cent of the epidemic in Kenya) sampled 1986-2019 from individuals of different risk groups and regions in Kenya. We found 89 per cent within-risk group transmission and 11 per cent mixing between risk groups, cyclic HIV-1 exchange between adjoining geographic provinces and strong evidence of HIV-1 dissemination from (i) West-to-East (i.e. higher-to-lower HIV-1 prevalence regions), and (ii) heterosexual-to-key populations. Low HIV-1 prevalence regions and key populations are sinks rather than major sources of HIV-1 transmission in Kenya. Targeting key populations in Kenya needs to occur concurrently with strengthening interventions in the general epidemic., (© The Author(s) 2022. Published by Oxford University Press.)

Introduction: In April 2019, an HIV-1 outbreak among children occurred in Larkana, Pakistan, affecting more than a thousand children. It was assumed that the outbreak originated from a single source, namely a doctor at a private health facility. In this study, we performed subtype distribution, phylogenetic and drug-resistance analysis of HIV-1 sequences from 2019 outbreak in Larkana, Pakistan., Methods: A total of 401 blood samples were collected between April-June 2019, from children infected with HIV-1 aged 0-15 years recruited into a case-control study to investigate the risk factors for HIV-1 transmission. Partial HIV-1 pol sequences were generated from 344 blood plasma samples to determine HIV-1 subtype and drug resistance mutations (DRM). Maximum-likelihood phylogenetics based on outbreak and reference sequences was used to identify transmission clusters and assess the relationship between outbreak and key population sequences between and within the determined clusters. Bayesian analysis was employed to identify the time to the most recent common recent ancestor (tMRCA) of the main Pakistani clusters., Results: The HIV-1 circulating recombinant form (CRF) 02_AG and subtype A1 were most common among the outbreak sequences. Of the treatment-naïve participants, the two most common mutations were RT: E138A (8%) and RT: K219Q (8%). Four supported clusters within the outbreak were identified, and the median tMRCAs of the Larkana outbreak sequences were estimated to 2016 for both the CRF02_AG and the subtype A1 clusters. Furthermore, outbreak sequences exhibited no phylogenetic mixing with sequences from other high-risk groups of Pakistan., Conclusion: The presence of multiple clusters indicated a multi-source outbreak, rather than a single source outbreak from a single health practitioner as previously suggested. The multiple introductions were likely a consequence of ongoing transmission within the high-risk groups of Larkana, and it is possible that the so-called Larkana strain was introduced into the general population through poor infection prevention control practices in healthcare settings. The study highlights the need to scale up HIV-1 prevention programmes among key population groups and improving infection prevention control in Pakistan., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Abidi, Nduva, Siddiqui, Rafaqat, Mahmood, Siddiqui, Nathwani, Hotwani, Shah, Memon, Sheikh, Khan, Esbjörnsson, Ferrand and Mir.)

HIV-1 transmission patterns within and between populations at different risk of HIV-1 acquisition in Kenya are not well understood. We investigated HIV-1 transmission networks in men who have sex with men (MSM), injecting drug users (IDU), female sex workers (FSW) and heterosexuals (HET) in coastal Kenya. We used maximum-likelihood and Bayesian phylogenetics to analyse new (N = 163) and previously published (N = 495) HIV-1 polymerase sequences collected during 2005-2019. Of the 658 sequences, 131 (20%) were from MSM, 58 (9%) IDU, 109 (17%) FSW, and 360 (55%) HET. Overall, 206 (31%) sequences formed 61 clusters. Most clusters (85%) consisted of sequences from the same risk group, suggesting frequent within-group transmission. The remaining clusters were mixed between HET/MSM (7%), HET/FSW (5%), and MSM/FSW (3%) sequences. One large IDU-exclusive cluster was found, indicating an independent sub-epidemic among this group. Phylodynamic analysis of this cluster revealed a steady increase in HIV-1 infections among IDU since the estimated origin of the cluster in 1987. Our results suggest mixing between high-risk groups and heterosexual populations and could be relevant for the development of targeted HIV-1 prevention programmes in coastal Kenya.