Sociodemographic, Ecological, and Spatiotemporal Factors Associated with Human Immunodeficiency Virus Drug Resistance in Florida: A Retrospective Analysis

Acquired Human Immunodeficiency Virus Type 1 Drug Resistance in Rhode Island, USA, 2004-2021

BACKGROUND

Human immunodeficiency virus type 1 (HIV-1) acquired drug resistance (ADR) compromises antiretroviral therapy (ART).

METHODS

We aggregated all HIV-1 protease-reverse transcriptase-integrase sequences over 2004-2021 at the largest HIV center in Rhode Island and evaluated ADR extent, trends, and impact using Stanford Database tools. Trends were measured with Mann-Kendall statistic, and multivariable regressions evaluated resistance predictors.

RESULTS

Sequences were available for 914 ART-experienced persons. Overall ADR to any drug decreased from 77% to 49% (-0.66 Mann-Kendall statistic); nucleoside reverse transcriptase inhibitors 65% to 32%, nonnucleoside reverse transcriptase inhibitors 53% to 43%, and protease inhibitors 28% to 7% (2004-2021), and integrase strand transfer inhibitors 16% to 13% (2017-2021). Multiclass resistance decreased from 44% to 12% (2-class) and 12% to 6% (3-class). In 2021, 94% had at least one 3-drug or 2-drug one-pill-once-daily (OPOD) option. Males and those exposed to more ART regimens were more likely to have ≥2-class resistance, and higher regimen exposure was also associated with fewer OPOD options.

CONCLUSIONS

Comprehensive analyses within a densely-sampled HIV epidemic over 2004-2021 demonstrated decreasing ADR. Continued ADR monitoring is important to maintain ART success, particularly with rising INSTI use in all lines of therapy and 2-drug and long-acting formulations.

Network typologies predict future molecular linkages in the network of HIV transmission

OBJECTIVE

HIV molecular transmission network typologies have previously demonstrated associations to transmission risk; however, few studies have evaluated their predictive potential in anticipating future transmission events. To assess this, we tested multiple models on statewide surveillance data from the Florida Department of Health.

DESIGN

This was a retrospective, observational cohort study examining the incidence of new HIV molecular linkages within the existing molecular network of persons with HIV (PWH) in Florida.

METHODS

HIV-1 molecular transmission clusters were reconstructed for PWH diagnosed in Florida from 2006 to 2017 using the HIV-TRAnsmission Cluster Engine (HIV-TRACE). A suite of machine-learning models designed to predict linkage to a new diagnosis were internally and temporally externally validated using a variety of demographic, clinical, and network-derived parameters.

RESULTS

Of the 9897 individuals who received a genotype within 12 months of diagnosis during 2012-2017, 2611 (26.4%) were molecularly linked to another case within 1 year at 1.5% genetic distance. The best performing model, trained on two years of data, was high performing (area under the receiving operating curve = 0.96, sensitivity = 0.91, and specificity = 0.90) and included the following variables: age group, exposure group, node degree, betweenness, transitivity, and neighborhood.

CONCLUSIONS

In the molecular network of HIV transmission in Florida, individuals' network position and connectivity predicted future molecular linkages. Machine-learned models using network typologies performed superior to models using individual data alone. These models can be used to more precisely identify subpopulations for intervention.

Genetic characterization of HIV-1 viruses among cases with antiretroviral therapy failure in Suzhou City, China

BACKGROUND

This retrospective study aimed to characterize the distribution of HIV-1 genotypes and the prevalence of drug resistance mutations in people with antiretroviral treatment (ART) failure in Suzhou City, China.

METHODS

Pol gene of HIV-1 viruses in blood samples of EDTA anticoagulants from 398 patients with failed antiviral treatment was successfully amplified by using an in-house assay. Drug resistance mutations were analyzed by using the Stanford HIV Drug Resistance Database system ( https://hivdb.stanford.edu/hivdb/by-mutations/ ). HIV-1 genotypes were determined by the REGA HIV subtyping tool (version 3.46, https://www.genomedetective.com/app/typingtool/hiv ). Near full-length genomes (NFLG) of HIV-1 viruses were obtained by next generation sequencing method.

RESULTS

Sequences analysis of the pol gene revealed that CRF 01_AE (57.29%, 228/398) was the dominant subtype circulating in Suzhou City, followed by CRF 07_BC (17.34%, 69/398), subtype B (7.54%, 30/398), CRF 08_BC (6.53%, 26/398), CRF 67_01B (3.02%, 12/398) and CRF55_01B (2.51%, 10/398). The overall prevalence of drug-resistant mutations in cases with ART failure was 64.57% (257/398), including 45.48% (181/398) for nucleotide reverse transcriptase inhibitors (NRTIs) mutations, 63.32% (252/398) for non-nucleoside reverse transcriptase inhibitors (NNRTIs) mutations, and 3.02% (12/398) for protease inhibitors (PIs) mutations. Ten near full-length genomes (NFLG) of HIV-1 viruses were identified, including six recombinants of CRF 01_AE and subtype B, two recombinants of CRF 01_AE, subtype B and subtype C sequences, one recombinant of CRF 01_AE and subtype C and one recombinant of CRF 01_AE, subtype A1 and subtype C.

CONCLUSIONS

The high prevalence of drug-resistant HIV-1 viruses was a serious challenge for HIV prevention and treatment of people with HIV infection. Treatment regimens for ART failure patients should be adjusted over time based on the outcome of drug resistance tests. NFLG sequencing facilitates the identification of new recombinants of HIV-1.

Molecular Epidemiology of HIV-1 Subtype B Infection across Florida Reveals Few Large Superclusters with Metropolitan Origin

Florida is considered an epicenter of HIV in the United States. The U.S. federal plan for Ending the HIV Epidemic (EHE) within 10 years prioritizes seven of Florida's 67 counties for intervention. We applied molecular epidemiology methods to characterize the HIV infection networks in the state and infer whether the results support the EHE. HIV sequences (N = 34,446) and associated clinical/demographic metadata of diagnosed people with HIV (PWH), during 2007 to 2017, were retrieved from the Florida Department of Health. HIV genetic networks were investigated using MicrobeTrace. Associates of clustering were identified through boosted logistic regression. Assortative trait mixing was also assessed. Bayesian phylogeographic methods were applied to evaluate evidence of imported HIV-1 lineages and illustrate spatiotemporal flows within Florida. We identified nine large clusters spanning all seven EHE counties but little evidence of external introductions, suggesting-in the absence of undersampling-an epidemic that evolved independently from the rest of the country or other external influences. Clusters were highly assortative by geography. Most of the sampled infections (82%) did not cluster with others in the state using standard molecular surveillance methods despite satisfactory sequence sampling in the state. The odds of being unclustered were higher among PWH in rural regions, and depending on demographics. A significant number of unclustered sequences were observed in counties omitted from EHE. The large number of missing sequence links may impact timely detection of emerging transmission clusters and ultimately hinder the success of EHE in Florida. Molecular epidemiology may help better understand infection dynamics at the population level and underlying disparities in disease transmission among subpopulations; however, there is also a continuous need to conduct ethical discussions to avoid possible harm of advanced methodologies to vulnerable groups, especially in the context of HIV stigmatization. IMPORTANCE The large number of missing phylogenetic linkages in rural Florida counties and among women and Black persons with HIV may impact timely detection of ongoing and emerging transmission clusters and ultimately hinder the success of epidemic elimination goals in Florida.

Early versus delayed antiretroviral therapy based on genotypic resistance test: Results from a large retrospective cohort study

Rapid start of antiretroviral therapy (ART) pending genotypic resistance test (GRT) has been recently proposed, but the effectiveness of this strategy is still debated. The rate of virological success (VS), defined as HIV‐RNA < 50 copies/ml, with and without GRT was compared in drug‐naïve individuals enrolled in the Italian ARCA cohort who started ART between 2015 and 2018. 521 individuals started ART: 397 without GRT (pre‐GRT group) and 124 following GRT (post‐GRT group). Overall, 398 (76%) were males and 30 (6%) were diagnosed with AIDS. In the pre‐GRT group, baseline CD4+ cell counts were lower (p < 0.001), and viral load was higher (p < 0.001) than in the post‐GRT group. The estimated probability of VS in pre‐GRT versus post‐GRT group was 72.54% (CI₉₅: 67.78–76.60) versus 66.94% (CI₉₅: 57.53–74.26) at Week 24 and 92.40% (CI₉₅: 89.26–94.62) versus 92.92% (CI₉₅: 86.35–96.33) at Week 48, respectively (p = 0.434). At Week 48, VS was less frequent among individuals with baseline CD4+ cell counts <200 versus >500 (90.33% vs. 97.33%), log viral load <5.00 versus >5.70 log₁₀ cps/ml (97.17% vs 78.16%; p < 0.001), and those treated with protease inhibitors or non‐nucleoside reverse transcriptase inhibitors versus those treated with integrase strand transfer inhibitors (p < 0.001). The rate of VS does not seem to be affected by an early ART initiation pending GRT results, but it could be influenced by the composition of the ART regimen, as well as immuno‐virological parameters.

Statewide Longitudinal Trends in Transmitted HIV-1 Drug Resistance in Rhode Island, USA

Background

HIV-1 transmitted drug resistance (TDR) remains a global challenge that can impact care, yet its comprehensive assessment is limited and heterogenous. We longitudinally characterized statewide TDR in Rhode Island.

Methods

Demographic and clinical data from treatment-naïve individuals were linked to protease, reverse transcriptase, and integrase sequences routinely obtained over 2004-2020. TDR extent, trends, impact on first-line regimens, and association with transmission networks were assessed using the Stanford Database, Mann-Kendall statistic, and phylogenetic tools.

Results

In 1123 individuals, TDR to any antiretroviral increased from 8% (2004) to 26% (2020), driven by non-nucleotide reverse transcriptase inhibitor (NNRTI; 5%-18%) and, to a lesser extent, nucleotide reverse transcriptase inhibitor (NRTI; 2%-8%) TDR. Dual- and triple-class TDR rates were low, and major integrase strand transfer inhibitor resistance was absent. Predicted intermediate to high resistance was in 77% of those with TDR, with differential suppression patterns. Among all individuals, 34% were in molecular clusters, some only with members with TDR who shared mutations. Among clustered individuals, people with TDR were more likely in small clusters.

Conclusions

In a unique (statewide) assessment over 2004-2020, TDR increased; this was primarily, but not solely, driven by NNRTIs, impacting antiretroviral regimens. Limited TDR to multiclass regimens and pre-exposure prophylaxis are encouraging; however, surveillance and its integration with molecular epidemiology should continue in order to potentially improve care and prevention interventions.

The Determinants of the Low COVID-19 Transmission and Mortality Rates in Africa: A Cross-Country Analysis

Background: More than 1 year after the beginning of the international spread of coronavirus 2019 (COVID-19), the reasons explaining its apparently lower reported burden in Africa are still to be fully elucidated. Few studies previously investigated the potential reasons explaining this epidemiological observation using data at the level of a few African countries. However, an updated analysis considering the various epidemiological waves and variables across an array of categories, with a focus on African countries might help to better understand the COVID-19 pandemic on the continent. Thus, we investigated the potential reasons for the persistently lower transmission and mortality rates of COVID-19 in Africa.

Methods: Data were collected from publicly available and well-known online sources. The cumulative numbers of COVID-19 cases and deaths per 1 million population reported by the African countries up to February 2021 were used to estimate the transmission and mortality rates of COVID-19, respectively. The covariates were collected across several data sources: clinical/diseases data, health system performance, demographic parameters, economic indicators, climatic, pollution, and radiation variables, and use of social media. The collinearities were corrected using variance inflation factor (VIF) and selected variables were fitted to a multiple regression model using the R statistical package.

Results: Our model (adjusted R-squared: 0.7) found that the number of COVID-19 tests per 1 million population, GINI index, global health security (GHS) index, and mean body mass index (BMI) were significantly associated (P < 0.05) with COVID-19 cases per 1 million population. No association was found between the median life expectancy, the proportion of the rural population, and Bacillus Calmette–Guérin (BCG) coverage rate. On the other hand, diabetes prevalence, number of nurses, and GHS index were found to be significantly associated with COVID-19 deaths per 1 million population (adjusted R-squared of 0.5). Moreover, the median life expectancy and lower respiratory infections rate showed a trend towards significance. No association was found with the BCG coverage or communicable disease burden.

Conclusions: Low health system capacity, together with some clinical and socio-economic factors were the predictors of the reported burden of COVID-19 in Africa. Our results emphasize the need for Africa to strengthen its overall health system capacity to efficiently detect and respond to public health crises.

Integrase Strand Transfer Inhibitor Resistance in Integrase Strand Transfer Inhibitor-Naive Persons

There has been no systematic review of the prevalence of transmitted integrase strand transfer inhibitor (INSTI) resistance. We systematically searched the English-language PubMed database and GenBank to identify studies published since 2010 reporting 50 or more INSTI-naive HIV-1-infected adults undergoing integrase genotyping. We extracted data related to country, sample year, specimen type, sequencing method, and subtype. For studies with sequences in GenBank, we determined the prevalence of three categories of INSTI-associated resistance mutations: (1) nonpolymorphic INSTI-selected drug resistance mutations (DRMs) that we refer to as surveillance DRMs; (2) rarely selected nonpolymorphic INSTI-associated DRMs; and (3) common polymorphic accessory INSTI-selected DRMs. A total of 103 studies met inclusion criteria including 75 studies in GenBank containing integrase sequences from 16,481 INSTI-naive persons. The median sample year was 2013 (interquartile range: 2008-2014). The prevalence of INSTI surveillance DRMs, rarely selected DRMs, and common polymorphic accessory INSTI-selected DRMs were 0.5%, 0.8%, and 6.2%, respectively. There was no association between the presence of nonpolymorphic surveillance DRM and region, sample year, or subtype. Two surveillance DRMs, E138K and R263K occurred in 0.15% and 0.10% of naive sequences, respectively. Several lines of evidence suggested that the 0.5% prevalence of surveillance DRMs partly reflects the cumulative natural occurrence of these mutations in the absence of selective drug pressure. There was an unexplained temporal increase in the proportion of sequences with polymorphic accessory mutations. The prevalence of INSTI-associated surveillance DRMs is low even in regions where INSTIs have been a major component of antiretroviral therapy for several years. The presence of INSTI-associated surveillance DRMs in INSTI-naive persons likely results from actual cases of transmitted INSTI resistance and from a low background level reflecting the cumulative rare natural occurrence of several nonpolymorphic mutations.