Search

PurposeThis study aims to describe the cohort of Adolescent Medicine Trials Network for HIV/AIDS Interventions (ATN) research program participants and evaluate whether the ATN's recently completed 5-year cycle recruited study participants who parallel the populations most impacted by HIV in the United States.MethodsHarmonized measures across ATN studies collected at baseline were aggregated for participants aged 13-24 years. Pooled means and proportions stratified by HIV status (at risk for or living with HIV) were calculated using unweighted averages of study-specific aggregate data. Medians were estimated using a weighted median of medians method. Public use 2019 Centers for Disease Control and Prevention surveillance data for state-level new HIV diagnoses and HIV prevalence among US youth aged 13-24 years were obtained for use as reference populations for ATN at-risk youth and youth living with HIV (YLWH), respectively.ResultsData from 3,185 youth at-risk for HIV and 542 YLWH were pooled from 21 ATN study phases conducted across the United States. Among ATN studies tailored to at-risk youth, a higher proportion of participants were White and a lower proportion were Black/African American and Hispanic/Latinx compared to youth newly diagnosed with HIV in the United States in 2019. Participants in ATN studies tailored to YLWH were demographically similar to YLWH in the United States.DiscussionThe development of data harmonization guidelines for ATN research activities facilitated this cross-network pooled analysis. These findings suggest the ATN's YLWH are representative, but that future studies of at-risk youth should prioritize recruitment strategies to enroll more participants from African American and Hispanic/Latinx populations.

BackgroundPersistent HIV infection of long-lived resting CD4 T cells, despite antiretroviral therapy (ART), remains a barrier to HIV cure. Women have a more robust type 1 interferon response during HIV infection relative to men, contributing to lower initial plasma viremia. As lower viremia during acute infection is associated with reduced frequency of latent HIV infection, we hypothesized that women on ART would have a lower frequency of latent HIV compared to men.MethodsART-suppressed, HIV seropositive women (n = 22) were matched 1:1 to 22 of 39 ART-suppressed men. We also compared the 22 women to all 39 men, adjusting for age and race as covariates. We measured the frequency of latent HIV using the quantitative viral outgrowth assay, the intact proviral DNA assay, and total HIV gag DNA. We also performed activation/exhaustion immunophenotyping on peripheral blood mononuclear cells and quantified interferon-stimulated gene (ISG) expression in CD4 T cells.ResultsWe did not observe evident sex differences in the frequency of persistent HIV in resting CD4 T cells. Immunophenotyping and CD4 T-cell ISG expression analysis revealed marginal differences across the sexes.ConclusionsDifferences in HIV reservoir frequency and immune activation appear to be small across sexes during long-term suppressive therapy.

Studies intended to estimate the effect of a treatment, like randomized trials, may not be sampled from the desired target population. To correct for this discrepancy, estimates can be transported to the target population. Methods for transporting between populations are often premised on a positivity assumption, such that all relevant covariate patterns in one population are also present in the other. However, eligibility criteria, particularly in the case of trials, can result in violations of positivity when transporting to external populations. To address nonpositivity, a synthesis of statistical and mathematical models can be considered. This approach integrates multiple data sources (e.g. trials, observational, pharmacokinetic studies) to estimate treatment effects, leveraging mathematical models to handle positivity violations. This approach was previously demonstrated for positivity violations by a single binary covariate. Here, we extend the synthesis approach for positivity violations with a continuous covariate. For estimation, two novel augmented inverse probability weighting estimators are proposed. Both estimators are contrasted with other common approaches for addressing nonpositivity. Empirical performance is compared via Monte Carlo simulation. Finally, the competing approaches are illustrated with an example in the context of two-drug vs. one-drug antiretroviral therapy on CD4 T cell counts among women with HIV. [ABSTRACT FROM AUTHOR]

Iterated conditional expectation (ICE) g‐computation is an estimation approach for addressing time‐varying confounding for both longitudinal and time‐to‐event data. Unlike other g‐computation implementations, ICE avoids the need to specify models for each time‐varying covariate. For variance estimation, previous work has suggested the bootstrap. However, bootstrapping can be computationally intense. Here, we present ICE g‐computation as a set of stacked estimating equations. Therefore, the variance for the ICE g‐computation estimator can be consistently estimated using the empirical sandwich variance estimator. Performance of the variance estimator was evaluated empirically with a simulation study. The proposed approach is also demonstrated with an illustrative example on the effect of cigarette smoking on the prevalence of hypertension. In the simulation study, the empirical sandwich variance estimator appropriately estimated the variance. When comparing runtimes between the sandwich variance estimator and the bootstrap for the applied example, the sandwich estimator was substantially faster, even when bootstraps were run in parallel. The empirical sandwich variance estimator is a viable option for variance estimation with ICE g‐computation. [ABSTRACT FROM AUTHOR]

Background Women in Africa disproportionately acquire human immunodeficiency virus type 1 (HIV-1). Understanding which women are most likely to acquire HIV-1 can guide focused prevention with preexposure prophylaxis (PrEP). Our objective was to identify women at the highest risk of HIV-1 and estimate PrEP efficiency at different sensitivity levels. Methods Nationally representative data were collected from 2015 through 2019 from 15 population-based household surveys. This analysis included women aged 15–49 who tested HIV-1 seronegative or had recent HIV-1. Least absolute shrinkage and selection operator regression models were fit with 28 variables to predict recent HIV-1. Models were trained on the full population and internally cross-validated. Performance was evaluated using area under the receiver operating characteristic curve (AUC), sensitivity, and number needed to treat (NNT) with PrEP to avert 1 infection. Results Among 209 012 participants, 248 had recent HIV-1 infection, representing 118 million women and 402 000 (95% confidence interval [CI], 309 000–495 000) annual infections. Two variables were retained: living in a subnational area with high HIV-1 viremia and having a sexual partner living outside the home. The full-population AUC was 0.80 (95% CI,.76–.84); cross-validated AUC was 0.79 (95% CI,.75–.84). At 33% sensitivity, 130 000 cases could be averted if 7.9 million women were perfectly adherent to PrEP; NNT would be 61. At 67% sensitivity, 260 000 cases could be averted if 25.1 million women were perfectly adherent; NNT would be 96. Conclusions This risk assessment tool was generalizable, predictive, and parsimonious with trade-offs between reach and efficiency. [ABSTRACT FROM AUTHOR]

Background: There are well-documented inequities in smoking between sexual and gender minority (SGM; e.g., lesbian, gay, bisexual, and transgender [LGBT]) and straight and cisgender people. However, there is less information about risk for and resilience against smoking among SGM people. Such information is critical for understanding etiology and developing interventions. Aims: To conduct a within-group assessment of risks and resiliencies relating to smoking status. Method: In 2017, we conducted a cross-sectional telephone survey with a national, probability-based sample of SGM adults (N = 453). We assessed theory-informed risks (adverse childhood events, substance use-oriented social environment, mental distress, stigma, discrimination, social isolation, and identity concealment) and resiliencies (advertising skepticism, identity centrality, social support, and SGM community participation). We applied survey weights, standardized predictor variables, and fit logistic regression models predicting smoking status. We stratified by age and SGM identity. Results: Patterns of risk and resilience differ by age and identity. Effects were consistently in the same direction for all groups for participating in substance use-oriented social environments, pointing to a potential risk factor for all groups. Advertising skepticism and having people you can talk to about being LGBTQ were potential protective factors. Discussion: Intervention development should address risk and resilience that differs by SGM identity. Additionally, our findings suggest interventionists should consider theoretical frameworks beyond minority stress. Conclusion: While much of the literature has focused on the role of stress from stigma and discrimination in tobacco use, addressing social norms and bolstering protective factors may also be important in SGM-targeted interventions.

Background While noninferiority of tenofovir alafenamide and emtricitabine (TAF/FTC) as preexposure prophylaxis (PrEP) for the prevention of human immunodeficiency virus (HIV) has been shown, interest remains in its efficacy relative to placebo. We estimate the efficacy of TAF/FTC PrEP versus placebo for the prevention of HIV infection. Methods We used data from the DISCOVER and iPrEx trials to compare TAF/FTC to placebo. DISCOVER was a noninferiority trial conducted from 2016 to 2017. iPrEx was a placebo-controlled trial conducted from 2007 to 2009. Inverse probability weights were used to standardize the iPrEx participants to the distribution of demographics and risk factors in the DISCOVER trial. To check the comparison, we evaluated whether risk of HIV infection in the shared tenofovir disoproxil fumarate and emtricitabine (TDF/FTC) arms was similar. Results Notable differences in demographics and risk factors occurred between trials. After standardization, the difference in risk of HIV infection between the TDF/FTC arms was near zero. The risk of HIV with TAF/FTC was 5.8 percentage points lower (95% confidence interval [CI], −2.0% to −9.6%) or 12.5-fold lower (95% CI,.02 to.31) than placebo standardized to the DISCOVER population. Conclusions There was a reduction in HIV infection with TAF/FTC versus placebo across 96 weeks of follow-up. Clinical Trials Registration NCT02842086 and NCT00458393. [ABSTRACT FROM AUTHOR]

While randomized controlled trials (RCTs) are critical for establishing the efficacy of new therapies, there are limitations regarding what comparisons can be made directly from trial data. RCTs are limited to a small number of comparator arms and often compare a new therapeutic to a standard of care which has already proven efficacious. It is sometimes of interest to estimate the efficacy of the new therapy relative to a treatment that was not evaluated in the same trial, such as a placebo or an alternative therapy that was evaluated in a different trial. Such dual‐study comparisons are challenging because of potential differences between trial populations that can affect the outcome. In this article, two bridging estimators are considered that allow for comparisons of treatments evaluated in different trials, accounting for measured differences in trial populations. A "multi‐span" estimator leverages a shared arm between two trials, while a "single‐span" estimator does not require a shared arm. A diagnostic statistic that compares the outcome in the standardized shared arms is provided. The two estimators are compared in simulations, where both estimators demonstrate minimal empirical bias and nominal confidence interval coverage when the identification assumptions are met. The estimators are applied to data from the AIDS Clinical Trials Group 320 and 388 to compare the efficacy of two‐drug vs four‐drug antiretroviral therapy on CD4 cell counts among persons with advanced HIV. The single‐span approach requires weaker identification assumptions and was more efficient in simulations and the application. [ABSTRACT FROM AUTHOR]

Studies designed to estimate the effect of an action in a randomized or observational setting often do not represent a random sample of the desired target population. Instead, estimates from that study can be transported to the target population. However, transportability methods generally rely on a positivity assumption, such that all relevant covariate patterns in the target population are also observed in the study sample. Strict eligibility criteria, particularly in the context of randomized trials, may lead to violations of this assumption. Two common approaches to address positivity violations are restricting the target population and restricting the relevant covariate set. As neither of these restrictions is ideal, we instead propose a synthesis of statistical and simulation models to address positivity violations. We propose corresponding g-computation and inverse probability weighting estimators. The restriction and synthesis approaches to addressing positivity violations are contrasted with a simulation experiment and an illustrative example in the context of sexually transmitted infection testing uptake. In both cases, the proposed synthesis approach accurately addressed the original research question when paired with a thoughtfully selected simulation model. Neither of the restriction approaches was able to accurately address the motivating question. As public health decisions must often be made with imperfect target population information, model synthesis is a viable approach given a combination of empirical data and external information based on the best available knowledge. [ABSTRACT FROM AUTHOR]