Temporal trends and transmission dynamics of pre-treatment HIV-1 drug resistance within and between risk groups in Kenya, 1986-2020.

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.

Phylogeographic Assessment Reveals Geographic Sources of HIV-1 Dissemination Among Men Who Have Sex With Men in Kenya.

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.

Quantifying rates of HIV-1 flow between risk groups and geographic locations in Kenya: A country-wide phylogenetic study.

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 Role of Phylogenetics in Discerning HIV-1 Mixing among Vulnerable Populations and Geographic Regions in Sub-Saharan Africa: A Systematic Review.

To reduce global HIV-1 incidence, there is a need to understand and disentangle HIV-1 transmission dynamics and to determine the geographic areas and populations that act as hubs or drivers of HIV-1 spread. In Sub-Saharan Africa (sSA), the region with the highest HIV-1 burden, information about such transmission dynamics is sparse. Phylogenetic inference is a powerful method for the study of HIV-1 transmission networks and source attribution. In this review, we assessed available phylogenetic data on mixing between HIV-1 hotspots (geographic areas and populations with high HIV-1 incidence and prevalence) and areas or populations with lower HIV-1 burden in sSA. We searched PubMed and identified and reviewed 64 studies on HIV-1 transmission dynamics within and between risk groups and geographic locations in sSA (published 1995-2021). We describe HIV-1 transmission from both a geographic and a risk group perspective in sSA. Finally, we discuss the challenges facing phylogenetic inference in mixed epidemics in sSA and offer our perspectives and potential solutions to the identified challenges.

HIV-1 Transmission Patterns Within and Between Risk Groups in Coastal Kenya.

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.