Prevalence and Correlates of Viral Load Suppression and Human Immunodeficiency Virus (HIV) Drug Resistance Among Children and Adolescents in South Rift Valley and Kisumu, Kenya

Abstract

Background: Children and adolescents living with HIV (CALHIV) face unique challenges, including poorer treatment outcomes, risk for drug-resistance mutations (HIVDRMs), and limited drug formulations. We estimated viral suppression (VS) prevalence and evaluated predictors of VS and HIVDRMs in Kenya.

Methods: From 2018-2020, CALHIV 1-19 years on antiretroviral therapy (ART) >6 months were enrolled in this cross-sectional study. Participants underwent viral load (VL) testing; those with VL ≥1000 copies/mL had HIVDRM testing. Sociodemographic questionnaires and medical record abstraction were completed. VS prevalence (VL <1000 copies/mL) was estimated; robust Poisson regression models were used to estimate prevalence ratios (PRs) and 95% CIs for associations between potential predictors of VS.

Results: Nine hundred and sixty-nine participants were enrolled. VS prevalence was .80 (95% CI: .78-.83). Being on ART >24 months (adjusted PR [aPR]: 1.22; 95% CI: 1.06-1.41), an integrase strand transfer inhibitor-containing regimen (1.13; 1.02-1.26), and attending a level 3 health facility (1.23; 1.11-1.36) were associated with VS. Missing ≥3 doses of ART in the past month (aPR: .73; 95% CI: .58-.92), having a viremic mother with HIV (.72; .53-.98), and having 3-7 (.90; .83-.97), 8-13 (.89; .82-.97), or ≥14 (.84; .77-.92) compared with <2 adherence counseling referrals were inversely associated with VS. A high proportion (n = 119, 81.5%) of unsuppressed participants had evidence of any major HIVDRM.

Conclusions: HIV treatment programs should target interventions for pediatric patients at risk for treatment failure-namely, those with a caregiver with failed VS and those struggling with adherence.

Keywords: HIV; Kenya; drug resistance; viral load.

Figure 1.

Frequency of NNRTI, NRTI, and PI resistance mutations among participants with viral load >1000 copies/mL. Plasma samples from participants with viral load >1000 copies/mL underwent sequencing of the Pol region using a laboratory-validated modification to the ViroSeq HIV-1 Genotyping System v2.0 (Abbott Molecular, Chicago, IL). Sequences were evaluated for major mutations conferring resistance to NRTIs, NNRTIs, and PIs using the SmartGene Integrated Database Network System (SmartGene, Zug, Switzerland) to access mutation lists from the Stanford HIV Drug Resistance Database version 8.8.0 (Stanford University, Stanford, CA). The prevalence of specific drug-resistance mutations and categories of drug-resistance mutations were calculated by dividing the number of participants with 1 or more mutations by the total number of participants genotyped. Abbreviations: NNRTI, non-nucleoside reverse transcriptase inhibitor; NRTI, nucleoside reverse transcriptase inhibitor; PI, protease inhibitor.

Figure 2.

HIV-1 subtypes. HIV-1 subtype was inferred from the consensus evolutionary tree from SmartGene Integrated Database Network System, which utilizes the neighbor-joining method in MEGA4 software. The evolutionary distances were computed using the maximum composite likelihood method in units of the number of base substitutions per site. The tree was then generated by the neighbor-joining method from a nucleotide alignment. Abbreviations: CRF, circulating recombinant form; HIV, human immunodeficiency virus.