Clinical, Psychosocial, and Structural Factors Associated with the Detection of HIV Drug Resistance in Children Living with HIV in Kisumu, Kenya: Secondary Analysis of Data from the Opt4Kids Study

Abstract

Background: HIV drug resistance (DR) mutations can compromise antiretroviral therapy (ART) success among children living with HIV (CLHIV). We conducted a secondary analysis using data from a randomized control trial for ART monitoring among CLHIV in Kisumu County, Kenya from 2019 to 2023, to assess clinical, psychosocial, and structural factors associated with HIV DR.

Methods: 704 CLHIV were followed for 12+ months, with characteristics captured at enrollment and follow-up visits in the "parent" randomized-controlled-trial (of point-of-care plasma viral load testing and for viremias ≥ 1000 copies/mL HIV genotyping for DR vs. standard-of-care) and an observational "extension" substudy (of participants on a dolutegravir-containing ART with genotyping performed on viremic specimens ≥ 200 copies/mL). A multivariate modified Poisson regression model was used to analyze factors associated with sequences yielding a Stanford HIVDR database DR penalty score (DR-PS) ≥ 30 to a nucleos(t)ides and/or non-nucleoside reverse transcriptase inhibitor, protease inhibitor (PI), and/or integrase inhibitor (INSTI).

Results: Among 113 (16.1%) participants who underwent genotyping, 93 (82.3%) had a DR-PS ≥ 30. DR-PS ≥ 30 were associated with age 1-5 years (adjusted risk ratio (ARR) = 1.84; 95% confidence interval (CI): 1.07, 3.14), history of viremia ≥ 1000 copies/mL (ARR = 4.18; 95% CI: 2.77, 6.31), prescription of a PI- (ARR = 6.05; 95% CI: 3.43, 10.68) or INSTI-containing regimen (ARR = 1.83; 95% CI: 1.08, 3.11), poor adherence to ART (ARR = 1.91; 95% CI: 1.32, 2.76), lack of caregiver confidence in ART administration (ARR = 1.89; 95% CI: 1.11, 3.22), and mid-sized clinic populations (ARR = 0.55; 95% CI: 0.33, 0.92).

Conclusion: Addressing social factors associated with DR-PS ≥ 30 may improve ART success among CLHIV.

Keywords: HIV; Kenya; children; drug resistance mutation (DRM); viral failure.

Figure 1

Flow diagram of participants included for analysis from Opt4Kids parent and extension studies (n = 704).

Figure 2

Distribution of HIV drug resistance mutations defined in Stanford HIVdb taken from sequence with greatest drug resistance penalty score N = number of children with mutations to the respective drug class(es). (a) Distribution of all drug resistance mutations by drug class and select codons; (b) distribution of NRTI mutations; (c) distribution of NNRTI mutations; (d) distribution of PI mutations; and (e) distribution of INSTI mutations. (NRTIs—nucleoside reverse transcriptase inhibitors; NNRTIs—non-nucleoside reverse transcriptase inhibitors; INSTI—integrase strand transfer inhibitors; PIs—protease inhibitors). Note: Mutations categorized as “Other” had a prevalence below the 1.3% cutoff threshold established by our team and were therefore excluded from the figure.

Figure 3

Prevalence (and 95% confidence intervals) of Stanford HIVdb drug resistance penalty scores ≥ 30 to available antiretroviral agents derived from mutations detected in participants Note: Abbreviations: NRTIs—nucleoside reverse transcriptase inhibitors; NNRTIs—non-nucleoside reverse transcriptase inhibitors; INSTI—integrase strand transfer inhibitors; PIs—protease inhibitors.

Figure 4

Forest plot of factors associated with a drug resistance penalty score ≥ 30 to any antiretroviral agent assessed by Stanford HIVdb detected in children enrolled in Opt4Kids parent and/or extension studies (n = 704) from the multivariate model output. Note: Abbreviations: ART—antiretroviral therapy; DRM—drug resistance mutation; NNRTI—non-nucleoside reverse transcriptase inhibitor; INSTI—integrase strand transfer inhibitor; PI—protease inhibitor.