HIV Prevention Among Men Who Have Sex With Men: Tenofovir Alafenamide Combination Preexposure Prophylaxis Versus Placebo.

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.

An HIV-1 risk assessment tool for women aged 15-49 in African countries: A pooled analysis across 15 nationally representative surveys.

BACKGROUND: Women in Africa disproportionately acquire HIV-1. Understanding which women are most likely to acquire HIV-1 can guide focused prevention with pre-exposure prophylaxis (PrEP). Our objective is to identify women at highest risk of HIV-1 and estimate PrEP efficiency at different sensitivity levels. METHODS: Nationally representative data were collected from 2015-2019 from 15 population-based household surveys. This analysis included women aged 15-49 who tested HIV-1 sero-negative 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 one infection. RESULTS: Among 209,012 participants 248 had recent HIV-1 infection, representing 118 million women and 402,000 (95% CI: 309,000-495,000) new annual infections. Two variables were retained in the model: living in a subnational area with high HIV-1 viremia and having a sexual partner living outside the home. Full-population AUC was 0.80 (95% CI: 0.76-0.84); cross-validated AUC was 0.79 (95% CI: 0.75-0.84). At a sensitivity of 33%, up to 130,000 cases could be averted if 7.9 million women were perfectly adherent to PrEP; NNT would be 61. At a sensitivity of 67%, up to 260,000 cases could be averted if 25.1 million women were perfectly adherent to PrEP; the NNT would be 96. CONCLUSIONS: This risk assessment tool was generalizable, predictive, and parsimonious with tradeoffs between reach and efficiency.

Bridged Treatment Comparisons: an Illustrative Application in HIV Treatment.

Comparisons of treatments, interventions, or exposures are of central interest in epidemiology, but direct comparisons are not always possible due to practical or ethical reasons. Here, we detail a fusion approach to compare treatments across studies. The motivating example entails comparing the risk of the composite outcome of death, AIDS, or greater than a 50% CD4 cell count decline in people with HIV when assigned triple versus mono antiretroviral therapy, using data from the AIDS Clinical Trial Group (ACTG) 175 (mono versus dual therapy) and ACTG 320 (dual versus triple therapy). We review a set of identification assumptions and estimate the risk difference using an inverse probability weighting estimator that leverages the shared trial arms (dual therapy). A fusion diagnostic based on comparing the shared arms is proposed that may indicate violation of the identification assumptions. Application of the data fusion estimator and diagnostic to the ACTG trials indicates triple therapy results in a reduction in risk compared to monotherapy in individuals with baseline CD4 counts between 50 and 300 cells/mm3. Bridged treatment comparisons address questions that none of the constituent data sources could address alone, but valid fusion-based inference requires careful consideration of the underlying assumptions.

Transportability Without Positivity: A Synthesis of Statistical and Simulation Modeling.

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.

Fusing trial data for treatment comparisons: Single vs multi-span bridging.

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.

Higher-order evidence.

Higher-order evidence is evidence about evidence. Epidemiologic examples of higher-order evidence include the settings where the study data constitute first-order evidence and estimates of misclassification comprise the second-order evidence (e.g., sensitivity, specificity) of a binary exposure or outcome collected in the main study. While sampling variability in higher-order evidence is typically acknowledged, higher-order evidence is often assumed to be free of measurement error (e.g., gold standard measures). Here we provide two examples, each with multiple scenarios where second-order evidence is imperfectly measured, and this measurement error can either amplify or attenuate standard corrections to first-order evidence. We propose a way to account for such imperfections that requires third-order evidence. Further illustrations and exploration of how higher-order evidence impacts results of epidemiologic studies is warranted.

Illustration of 2 Fusion Designs and Estimators.

"Fusion" study designs combine data from different sources to answer questions that could not be answered (as well) by subsets of the data. Studies that augment main study data with validation data, as in measurement-error correction studies or generalizability studies, are examples of fusion designs. Fusion estimators, here solutions to stacked estimating functions, produce consistent answers to identified research questions using data from fusion designs. In this paper, we describe a pair of examples of fusion designs and estimators, one where we generalize a proportion to a target population and one where we correct measurement error in a proportion. For each case, we present an example motivated by human immunodeficiency virus research and summarize results from simulation studies. Simulations demonstrate that the fusion estimators provide approximately unbiased results with appropriate 95% confidence interval coverage. Fusion estimators can be used to appropriately combine data in answering important questions that benefit from multiple sources of information.

Missing Outcome Data in Epidemiologic Studies.

Missing data are pandemic and a central problem for epidemiology. Missing data reduce precision and can cause notable bias. There remain too few simple published examples detailing types of missing data and illustrating their possible impact on results. Here we take an example randomized trial that was not subject to missing data and induce missing data to illustrate 4 scenarios in which outcomes are 1) missing completely at random, 2) missing at random with positivity, 3) missing at random without positivity, and 4) missing not at random. We demonstrate that accounting for missing data is generally a better strategy than ignoring missing data, which unfortunately remains a standard approach in epidemiology.

When Does Differential Outcome Misclassification Matter for Estimating Prevalence?

BACKGROUND: When accounting for misclassification, investigators make assumptions about whether misclassification is "differential" or "nondifferential." Most guidance on differential misclassification considers settings where outcome misclassification varies across levels of exposure, or vice versa. Here, we examine when covariate-differential misclassification must be considered when estimating overall outcome prevalence. METHODS: We generated datasets with outcome misclassification under five data generating mechanisms. In each, we estimated prevalence using estimators that (a) ignored misclassification, (b) assumed misclassification was nondifferential, and (c) allowed misclassification to vary across levels of a covariate. We compared bias and precision in estimated prevalence in the study sample and an external target population using different sources of validation data to account for misclassification. We illustrated use of each approach to estimate HIV prevalence using self-reported HIV status among people in East Africa cross-border areas. RESULTS: The estimator that allowed misclassification to vary across levels of the covariate produced results with little bias for both populations in all scenarios but had higher variability when the validation study contained sparse strata. Estimators that assumed nondifferential misclassification produced results with little bias when the covariate distribution in the validation data matched the covariate distribution in the target population; otherwise estimates assuming nondifferential misclassification were biased. CONCLUSIONS: If validation data are a simple random sample from the target population, assuming nondifferential outcome misclassification will yield prevalence estimates with little bias regardless of whether misclassification varies across covariates. Otherwise, obtaining valid prevalence estimates requires incorporating covariates into the estimators used to account for misclassification.

Sensitivity Analyses for Means or Proportions with Missing Outcome Data.

We describe an approach to sensitivity analysis introduced by Robins et al (1999), for the setting where the outcome is missing for some observations. This flexible approach focuses on the relationship between the outcomes and missingness, where data can be missing completely at random, missing at random given observed data, or missing not at random. We provide examples from HIV that include the sensitivity of the estimation of a mean and proportion under different missingness mechanisms. The approach illustrated provides a method for examining how the results of epidemiologic studies might shift as a function of bias due to missing data.

Estimation of SARS-CoV-2 Seroprevalence in Central North Carolina: Accounting for Outcome Misclassification in Complex Sample Designs.

BACKGROUND: Population-based seroprevalence studies are crucial to understand community transmission of COVID-19 and guide responses to the pandemic. Seroprevalence is typically measured from diagnostic tests with imperfect sensitivity and specificity. Failing to account for measurement error can lead to biased estimates of seroprevalence. Methods to adjust seroprevalence estimates for the sensitivity and specificity of the diagnostic test have largely focused on estimation in the context of convenience sampling. Many existing methods are inappropriate when data are collected using a complex sample design. METHODS: We present methods for seroprevalence point estimation and confidence interval construction that account for imperfect test performance for use with complex sample data. We apply these methods to data from the Chatham County COVID-19 Cohort (C4), a longitudinal seroprevalence study conducted in central North Carolina. Using simulations, we evaluate bias and confidence interval coverage for the proposed estimator compared with a standard estimator under a stratified, three-stage cluster sample design. RESULTS: We obtained estimates of seroprevalence and corresponding confidence intervals for the C4 study. SARS-CoV-2 seroprevalence increased rapidly from 10.4% in January to 95.6% in July 2021 in Chatham County, North Carolina. In simulation, the proposed estimator demonstrates desirable confidence interval coverage and minimal bias under a wide range of scenarios. CONCLUSION: We propose a straightforward method for producing valid estimates and confidence intervals when data are based on a complex sample design. The method can be applied to estimate the prevalence of other infections when estimates of test sensitivity and specificity are available.

Defining stable reference genes in HIV latency reversal experiments.

Quantification of cell associated HIV RNA (ca-RNA) is one of the most important and commonly used methods to evaluate the performance of latency-reversing agents (LRAs). Copies of HIV RNA measured by qPCR, are often normalized to the input RNA or cell number. However, these could be affected by biological variability and/or technical errors, which can be avoided by using an internal reference gene. To obtain reliable data, it is essential to select stable reference genes (RGs) of which the expression is not influenced by biological variability, the type of cells, or the LRAs used. However, to date, no study has carefully evaluated RG stability following LRA exposure. We analyzed the stability of six widely used RGs (GAPDH, TBP, YWHAZ, UBE2D2, HPRT1 and RPL27A) in human PBMC and CD4+ T cells. LRA exposure significantly influenced the stability of these RGs. Overall, TBP, UBE2D2, and RPL27A were the most stable RGs in all tested conditions. TBP was generally the most stable RG whereas GAPDH varied the most. Finally, we evaluated the impact of applying different RG normalizers to host genes and HIV ca-RNA data. Altered results were observed both in host and HIV gene expression when unstable RGs were used. Our data underline the importance of testing the stability of RGs utilized to evaluate LRA-induced HIV ca-RNA expression. To our knowledge, this is the first careful evaluation of the stability of RGs after LRA exposure and will significantly contribute to the quality of data analysis in regard to gene expression.IMPORTANCELatency-reversing agents (LRAs) are ubiquitously used in the "shock-and-kill" HIV cure strategy and their performance is often evaluated by ex-vivo quantification of cell associated HIV RNA. HIV RNA, measured by qPCR, is often normalized to internal reference genes, but the expression of these genes should not be influenced by the experimental settings. We found that treatment of human PBMC and CD4+ T cells with LRAs significantly altered the expression of several commonly used reference genes, such as GAPDH. Finally, we evaluate the impact of different reference genes on normalization of host genes and HIV cell associated RNA expression and demonstrated that using unstable reference genes dramatically altered experimental outcome. Our data highlight the importance of using reference genes that are unaffected by LRAs under study to correctly evaluate host gene and cell associated HIV RNA expression induced by latency-reversing agents.

On the Use of Covariate Supersets for Identification Conditions.

The union of distinct covariate sets, or the superset, is often used in proofs for the identification or the statistical consistency of an estimator when multiple sources of bias are present. However, the use of a superset can obscure important nuances. Here, we provide two illustrative examples: one in the context of missing data on outcomes, and one in which the average causal effect is transported to another target population. As these examples demonstrate, the use of supersets may indicate a parameter is not identifiable when the parameter is indeed identified. Furthermore, a series of exchangeability conditions may lead to successively weaker conditions. Future work on approaches to address multiple biases can avoid these pitfalls by considering the more general case of nonoverlapping covariate sets.

Power and sample size for observational studies of point exposure effects.

Inverse probability of treatment weights (IPTWs) are commonly used to control for confounding when estimating causal effects of point exposures from observational data. When planning a study that will be analyzed with IPTWs, determining the required sample size for a given level of statistical power is challenging because of the effect of weighting on the variance of the estimated causal means. This paper considers the utility of the design effect to quantify the effect of weighting on the precision of causal estimates. The design effect is defined as the ratio of the variance of the causal mean estimator divided by the variance of a naïve estimator if, counter to fact, no confounding had been present and weights were not needed. A simple, closed-form approximation of the design effect is derived that is outcome invariant and can be estimated during the study design phase. Once the design effect is approximated for each treatment group, sample size calculations are conducted as for a randomized trial, but with variances inflated by the design effects to account for weighting. Simulations demonstrate the accuracy of the design effect approximation, and practical considerations are discussed.

Permethrin-treated baby wraps for the prevention of malaria: results of a randomized controlled pilot study in rural Uganda.

BACKGROUND: Progress against malaria has stalled and may even be slipping backwards in high-burden countries. This is due to a range of factors including insecticide resistance and mosquito feeding behaviours that limit contact with widely-employed interventions including long-lasting insecticidal nets and indoor-residual spraying. Thus, further innovations in malaria control are urgently needed. METHODS: The pilot was a randomized, placebo-controlled pilot study of permethrin-treated baby wraps-known locally as lesus-in children 6-18 months of age at a single site in rural western Uganda. Fifty mother-infant pairs were assigned to permethrin-treated or untreated lesus in a 1:1 allocation. Participants and clinical staff were blinded to group assignments through use of sham treatment and re-treatment of lesus. Participants attended scheduled clinic visits every 2 weeks for a total 12 weeks. The primary outcome of interest was the safety of the intervention, assessed as changes in the frequency of use, rates of discontinuation, and incidence of adverse events, such as skin rash. Secondary outcomes included acceptability and feasibility of the intervention as measured through participant satisfaction and completion of study activities, respectively. RESULTS: Overall, rates of retention and participation were relatively high with 86.0% (43 of 50) of participants completing all scheduled visits, including 18 (75.0%) and 25 (96.2%) in the intervention and control arms respectively. By the conclusion of the 12-week follow-up period, one adverse event (0.35 events per 100 person-weeks, one-sided 95% CI 0.0-1.65) was reported. Satisfaction with the lesu was high in both groups. In each study arm, there were five incident RDT positive results, but the only PCR-positive results were observed in the control group (n = 2). CONCLUSIONS: Permethrin-treated baby wraps were well-tolerated and broadly acceptable. Adverse events were infrequent and mild. These findings support future trials seeking to determine the efficacy of treated wraps to prevent P. falciparum malaria infection in young children as a complementary tool to existing household-based interventions. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT04102592, Registered 25 September 2019. Available at: https://clinicaltrials.gov/ct2/show/NCT04102592.

Malaria prevalence and long-lasting insecticidal net use in rural western Uganda: results of a cross-sectional survey conducted in an area of highly variable malaria transmission intensity.

BACKGROUND: Long-lasting insecticidal nets (LLINs) remain a cornerstone of malaria control, but strategies to sustain universal coverage and high rates of use are not well-defined. A more complete understanding of context-specific factors, including transmission intensity and access to health facilities, may inform sub-district distribution approaches and tailored messaging campaigns. METHODS: A cross-sectional survey of 2190 households was conducted in a single sub-county of western Uganda that experiences highly variable malaria transmission intensity. The survey was carried out approximately 3 years after the most recent mass distribution campaign. At each household, study staff documented reported LLIN use and source among children 2 to 10 years of age and performed a malaria rapid diagnostic test. Elevation and distance to the nearest health facility was estimated for each household. Associations between parasite prevalence and LLIN use were estimated from log binomial regression models with elevation and distance to clinic being the primary variables of interest. RESULTS: Overall, 6.8% (148 of 2170) of children age 2-10 years of age had a positive RDT result, yielding a weighted estimate of 5.8% (95% confidence interval [CI] 5.4-6.2%). There was substantial variability in the positivity rates among villages, with the highest elevation villages having lower prevalence than lowest-elevation villages (p < .001). Only 64.7% (95% CI 64.0-65.5%) of children were reported to have slept under a LLIN the previous night. Compared to those living < 1 km from a health centre, households at ≥ 2 km were less likely to report the child sleeping under a LLIN (RR 0.86, 95% CI 0.83-0.89, p < .001). Households located farther from a health centre received a higher proportion of LLINs from government distributions compared to households living closer to health centres. CONCLUSIONS: LLIN use and sourcing was correlated with household elevation and estimated distance to the nearest health facility. The findings suggest that current facility-based distribution strategies are limited in their reach. More frequent mass distribution campaigns and complementary approaches are likely required to maintain universal LLIN coverage and high rates of use among children in rural Uganda.

Estimation Without Representation: Early Severe Acute Respiratory Syndrome Coronavirus 2 Seroprevalence Studies and the Path Forward.

The recent development and regulatory approval of a variety of serological assays indicating the presence of antibodies against severe acute respiratory syndrome coronavirus 2 has led to rapid and widespread implementation of seroprevalence studies. Accurate estimates of seroprevalence are needed to model transmission dynamics and estimate mortality rates. Furthermore, seroprevalence levels in a population help guide policy surrounding reopening efforts. The literature to date has focused heavily on issues surrounding the quality of seroprevalence tests and less on the sampling methods that ultimately drive the representativeness of resulting estimates. Seroprevalence studies based on convenience samples are being reported widely and extrapolated to larger populations for the estimation of total coronavirus disease 2019 (COVID-19) infections, comparisons of prevalence across geographic regions, and estimation of mortality rates. In this viewpoint, we discuss the pitfalls that can arise with the use of convenience samples and offer guidance for moving towards more representative and timely population estimates of COVID-19 seroprevalence.

Impact of Biological Sex on Immune Activation and Frequency of the Latent HIV Reservoir During Suppressive Antiretroviral Therapy.

BACKGROUND: Persistent 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. METHODS: ART-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. RESULTS: We 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. CONCLUSIONS: Differences in HIV reservoir frequency and immune activation appear to be small across sexes during long-term suppressive therapy.

Incarceration and Number of Sexual Partners After Incarceration Among Vulnerable US Women, 2007-2017.

Objectives. To examine whether women's incarceration increases numbers of total and new sexual partners.Methods. US women with or at risk for HIV in a multicenter cohort study answered incarceration and sexual partner questions semiannually between 2007 and 2017. We used marginal structural models to compare total and new partners at visits not following incarceration with all visits following incarceration and visits immediately following incarceration. Covariates included demographics, HIV status, sex exchange, drug or alcohol use, and housing instability.Results. Of the 3180 participants, 155 were incarcerated. Women reported 2 partners, 3 or more partners, and new partners at 5.2%, 5.2%, and 9.3% of visits, respectively. Relative to visits not occurring after incarceration, odds ratios were 2.41 (95% confidence interval [CI] = 1.20, 4.85) for 2 partners, 2.03 (95% CI = 0.97, 4.26) for 3 or more partners, and 3.24 (95% CI = 1.69, 6.22) for new partners at visits immediately after incarceration. Odds ratios were similar for all visits following incarceration.Conclusions. Women had more total partners and new partners immediately and at all visits following incarceration after confounders and loss to follow-up had been taken into account.

Perceived Importance of Health Concerns Among Lesbian, Gay, Bisexual, and Transgender Adults in a National, Probability-Based Phone Survey, 2017.

Perceptions of the importance of health problems can drive advocacy, policy change, resource distribution, and individual behaviors. However, little is known about how lesbian, gay, bisexual, and transgender (LGBT), that is, sexual and gender minority (SGM) adults view the health problems facing SGM populations. In a 2017 national, probability-based survey of U.S. SGM adults (N = 453), we asked respondents to identify the most serious health problem facing SGM people today. Participants also rated the seriousness of five specific health problems (HIV/AIDS, suicide, hate crimes, harmful alcohol use, tobacco use). Analyses accounted for the complex sampling design and were stratified by gender identity. One quarter of U.S. SGM adults identified the most serious health problem facing SGM people to be HIV/AIDS (95% confidence interval [20.3, 31.2]). More respondents stated there were no serious LGBT health differences compared with straight/cisgender adults (4.2%, confidence interval [2.6, 5.9]) than identified tobacco use, hate crimes, chronic diseases, cancer, or suicide as the most serious. Importance ratings differed by gender and tobacco/alcohol use were perceived as less serious compared with HIV/AIDS, suicide, and hate crimes. Attention paid to HIV/AIDS by the SGM public, while important, may hinder efforts to address chronic diseases and other health issues affecting SGM people.