Community <scp>HIV</scp>‐1 drug resistance is associated with transmitted drug resistance

Tilghman, MW; Pérez-Santiago, Josué; Osorio, Georgina; Little, SJ; Dd, Richman; Mathews, W. Christopher; Haubrich, R; Smith, Davey M.

doi:10.1111/hiv.12122

Cited by 11 publications

(13 citation statements)

References 19 publications

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“…Twenty-five clusters (18.1%) had two or more individuals sharing one or more drug resistance mutations, accounting for 78.0% of the clustering individuals with resistance. The most common drug resistance mutation was K103N, which was represented in 11.5% of entire cohort and 10.9% of clustering individuals, and is similar to the prevalence of K103N seen in newly infected individuals in San Diego ( Tilghman et al, 2014 ).…”

Section: Resultssupporting

confidence: 56%

HIV Transmission Networks in the San Diego–Tijuana Border Region

et al. 2015

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BackgroundHIV sequence data can be used to reconstruct local transmission networks. Along international borders, like the San Diego–Tijuana region, understanding the dynamics of HIV transmission across reported risks, racial/ethnic groups, and geography can help direct effective prevention efforts on both sides of the border.MethodsWe gathered sociodemographic, geographic, clinical, and viral sequence data from HIV infected individuals participating in ten studies in the San Diego–Tijuana border region. Phylogenetic and network analysis was performed to infer putative relationships between HIV sequences. Correlates of identified clusters were evaluated and spatiotemporal relationships were explored using Bayesian phylogeographic analysis.FindingsAfter quality filtering, 843 HIV sequences with associated demographic data and 263 background sequences from the region were analyzed, and 138 clusters were inferred (2–23 individuals). Overall, the rate of clustering did not differ by ethnicity, residence, or sex, but bisexuals were less likely to cluster than heterosexuals or men who have sex with men (p = 0.043), and individuals identifying as white (p ≤ 0.01) were more likely to cluster than other races. Clustering individuals were also 3.5 years younger than non-clustering individuals (p < 0.001). Although the sampled San Diego and Tijuana epidemics were phylogenetically compartmentalized, five clusters contained individuals residing on both sides of the border.InterpretationThis study sampled ~ 7% of HIV infected individuals in the border region, and although the sampled networks on each side of the border were largely separate, there was evidence of persistent bidirectional cross-border transmissions that linked risk groups, thus highlighting the importance of the border region as a “melting pot” of risk groups.FundingNIH, VA, and Pendleton Foundation.

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Section: Resultssupporting

confidence: 56%

HIV Transmission Networks in the San Diego–Tijuana Border Region

et al. 2015

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“…Clinicians should then switch treatment. Delaying treatment switching leads to accumulation of resistance mutations [3][4][5], unfavourable patient outcomes and increased risk of transmission of drug-resistant strains [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21]. If the second consecutive VL is re-suppressed, patients usually remain on the same treatment regimen assuming that treatment adherence is restored without major drug resistance (DR).…”

Section: Introductionmentioning

confidence: 99%

Successes and challenges in optimizing the viral load cascade to improve antiretroviral therapy adherence and rationalize second‐line switches in Swaziland

et al. 2018

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IntroductionAs antiretroviral therapy (ART) is scaled up, more patients become eligible for routine viral load (VL) monitoring, the most important tool for monitoring ART efficacy. For HIV programmes to become effective, leakages along the VL cascade need to be minimized and treatment switching needs to be optimized. However, many HIV programmes in resource‐constrained settings report significant shortfalls.MethodsFrom a public sector HIV programme in rural Swaziland, we evaluated the VL cascade of adults (≥18 years) on ART from the time of the first elevated VL (>1000 copies/mL) between January 2013 and June 2014 to treatment switching by December 2015. We additionally described HIV drug resistance for patients with virological failure. We used descriptive statistics and Kaplan–Meier estimates to describe the different steps along the cascade and regression models to determine factors associated with outcomes.Results and DiscussionOf 828 patients with a first elevated VL, 252 (30.4%) did not receive any enhanced adherence counselling (EAC). Six hundred and ninety‐six (84.1%) patients had a follow‐up VL measurement, and the predictors of receiving a follow‐up VL were being a second‐line patient (adjusted hazard ratio (aHR): 0.72; p = 0.051), Hlathikhulu health zone (aHR: 0.79; p = 0.013) and having received two EAC sessions (aHR: 1.31; p = 0.023). Four hundred and ten patients (58.9%) achieved VL re‐suppression. Predictors of re‐suppression were age 50 to 64 (adjusted odds ratio (aOR): 2.02; p = 0.015) compared with age 18 to 34 years, being on second‐line treatment (aOR: 3.29; p = 0.003) and two (aOR: 1.66; p = 0.045) or three (aOR: 1.86; p = 0.003) EAC sessions. Of 278 patients eligible to switch to second‐line therapy, 120 (43.2%) had switched by the end of the study. Finally, of 155 successfully sequenced dried blood spots, 144 (92.9%) were from first‐line patients. Of these, 133 (positive predictive value: 92.4%) had resistance patterns that necessitated treatment switching.ConclusionsPatients on ART with high VLs were more likely to re‐suppress if they received EAC. Failure to re‐suppress after counselling was predictive of genotypically confirmed resistance patterns requiring treatment switching. Delays in switching were significant despite the ability of the WHO algorithm to predict treatment failure. Despite significant progress in recent years, enhanced focus on quality care along the VL cascade in resource‐limited settings is crucial.

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“…These categories of resistance may provide an indication of how well a city is maintaining treatment and adherence in its infected population. Additionally, Tilghman et al found that high levels of CDR at specific gene locations predicted TDR in the same locations [ 33 ].…”

Section: Introductionmentioning

confidence: 99%

Prevalence and trends in transmitted and acquired antiretroviral drug resistance, Washington, DC, 1999–2014

2017

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BackgroundDrug resistance limits options for antiretroviral therapy (ART) and results in poorer health outcomes among HIV-infected persons. We sought to characterize resistance patterns and to identify predictors of resistance in Washington, DC.MethodsWe analyzed resistance in the DC Cohort, a longitudinal study of HIV-infected persons in care in Washington, DC. We measured cumulative drug resistance (CDR) among participants with any genotype between 1999 and 2014 (n = 3411), transmitted drug resistance (TDR) in ART-naïve persons (n = 1503), and acquired drug resistance (ADR) in persons with genotypes before and after ART initiation (n = 309). Using logistic regression, we assessed associations between patient characteristics and transmitted resistance to any antiretroviral.ResultsPrevalence of TDR was 20.5%, of ADR 40.5%, and of CDR 45.1% in the respective analysis groups. From 2004 to 2013, TDR prevalence decreased for nucleoside and nucleotide analogue reverse transcriptase inhibitors (15.0 to 5.5%; p = 0.0003) and increased for integrase strand transfer inhibitors (INSTIs) (0.0–1.4%; p = 0.04). In multivariable analysis, TDR was not associated with age, race/ethnicity, HIV risk group, or years from HIV diagnosis.ConclusionsIn this urban cohort of HIV-infected persons, almost half of participants tested had evidence of CDR; and resistance to INSTIs was increasing. If this trend continues, inclusion of the integrase-encoding region in baseline genotype testing should be strongly considered.

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Community HIV‐1 drug resistance is associated with transmitted drug resistance

Cited by 11 publications

References 19 publications

HIV Transmission Networks in the San Diego–Tijuana Border Region

HIV Transmission Networks in the San Diego–Tijuana Border Region

Successes and challenges in optimizing the viral load cascade to improve antiretroviral therapy adherence and rationalize second‐line switches in Swaziland

Prevalence and trends in transmitted and acquired antiretroviral drug resistance, Washington, DC, 1999–2014

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