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Introduction: Identifying the optimal approaches to offering HIV prevention to meet the needs of those at risk is a high priority, particularly given the expanding toolkit of biomedical HIV prevention options. An ongoing study in rural East African communities evaluated the uptake of choices in product, testing mode and location of care delivery through a structured patient‐centred HIV prevention delivery model. In this qualitative study, we sought to understand clients' experiences of this "dynamic choice prevention model" (DCP) and highlight pathways of action to inform HIV prevention delivery models. Methods: In‐depth semi‐structured interviews were conducted from November 2021 through March 2022 with a purposively selected sample of n = 56 participants in DCP trials (across outpatient departments, antenatal clinics and community settings), and n = 21 healthcare providers (total n = 77). A seven‐person multi‐regional team translated and inductively coded transcript data. We used a framework analysis approach to identify emergent themes. Results: Individuals taking up HIV pre‐exposure prophylaxis (PrEP) reported feelings of relief, liberation from fears of acquiring HIV and satisfaction with being able to take action despite partners' behaviours. Couples used a range of approaches afforded by the study to persuade partners to get tested and opt for PrEP. Post‐exposure prophylaxis (PEP) use was less common, although women welcomed it in the event of sexual coercion or assault. Participants discussed switching from PEP to PrEP after familiarizing themselves with usage and ascertaining ongoing risk. Participants felt respected by providers, trusted them and appreciated being able to contact them directly for telephone support. Prevention uptake was hindered by stigma, limited experience with and knowledge of prevention methods, gendered and generational power dynamics within intimate partnerships and families, and negative perceptions of methods due to the products themselves. Participants anticipated long‐acting injectable PrEP could solve their challenges regarding pill size, daily pill burden and the likelihood of unwanted disclosure. Conclusions: Diverse preferences and barriers to uptake of prevention require a choice of HIV prevention options, locations and delivery modalities—but in addition, flexible, competent and friendly care provision is crucial to promote uptake. Helping clients feel valued, and addressing their unique needs and challenges, enables their agency to prioritize their health. [ABSTRACT FROM AUTHOR]

Introduction: Unhealthy alcohol use significantly contributes to viral non‐suppression among persons with HIV (PWH). It is unknown whether brief behavioural interventions to reduce alcohol use can improve viral suppression among PWH with unhealthy alcohol use in sub‐Saharan Africa (SSA). Methods: As part of the SEARCH study (NCT04810650), we conducted an individually randomized trial in Kenya and Uganda of a brief, skills‐based alcohol intervention among PWH with self‐reported unhealthy alcohol use (Alcohol Use Disorders Identification Test–Consumption [AUDIT‐C], prior 3 months, ≥3/female; ≥4/male) and at risk of viral non‐suppression, defined as either recent HIV viral non‐suppression (≥400 copies/ml), missed visits, out of care or new diagnosis. The intervention included baseline and 3‐month in‐person counselling sessions with interim booster phone calls every 3 weeks. The primary outcome was HIV viral suppression (<400 copies/ml) at 24 weeks, and the secondary outcome was unhealthy alcohol use, defined by AUDIT‐C or phosphatidylethanol (PEth), an alcohol biomarker, ≥50 ng/ml at 24 weeks. Results: Between April and September 2021, 401 persons (198 intervention, 203 control) were enrolled from HIV clinics in Uganda (58%) and Kenya (27%) and alcohol‐serving venues in Kenya (15%). At baseline, 60% were virally suppressed. Viral suppression did not differ between arms at 24 weeks: suppression was 83% in intervention and 82% in control arms (RR: 1.01, 95% CI: 0.93–1.1). Among PWH with baseline viral non‐suppression, 24‐week suppression was 73% in intervention and 64% in control arms (RR 1.15, 95% CI: 0.93–1.43). Unhealthy alcohol use declined from 98% at baseline to 73% in intervention and 84% in control arms at 24 weeks (RR: 0.86, 95% CI: 0.79–0.94). Effects on unhealthy alcohol use were stronger among women (RR 0.70, 95% CI: 0.56–0.88) than men (RR 0.93, 95% CI: 0.85–1.01) and among participants with a baseline PEth⩽200 ng/ml (RR 0.68, 95% CI: 0.53–0.87) versus >200 ng/ml (RR 0.97, 95% CI: 0.92–1.02). Conclusions: In a randomized trial of 401 PWH with unhealthy alcohol use and risk for viral non‐suppression, a brief alcohol intervention reduced unhealthy alcohol use but did not affect viral suppression at 24 weeks. Brief alcohol interventions have the potential to improve the health of PWH in SSA by reducing alcohol use, a significant driver of HIV‐associated co‐morbidities. [ABSTRACT FROM AUTHOR]

Across research disciplines, cluster randomized trials (CRTs) are commonly implemented to evaluate interventions delivered to groups of participants, such as communities and clinics. Despite advances in the design and analysis of CRTs, several challenges remain. First, there are many possible ways to specify the causal effect of interest (eg, at the individual‐level or at the cluster‐level). Second, the theoretical and practical performance of common methods for CRT analysis remain poorly understood. Here, we present a general framework to formally define an array of causal effects in terms of summary measures of counterfactual outcomes. Next, we provide a comprehensive overview of CRT estimators, including the t‐test, generalized estimating equations (GEE), augmented‐GEE, and targeted maximum likelihood estimation (TMLE). Using finite sample simulations, we illustrate the practical performance of these estimators for different causal effects and when, as commonly occurs, there are limited numbers of clusters of different sizes. Finally, our application to data from the Preterm Birth Initiative (PTBi) study demonstrates the real‐world impact of varying cluster sizes and targeting effects at the cluster‐level or at the individual‐level. Specifically, the relative effect of the PTBi intervention was 0.81 at the cluster‐level, corresponding to a 19% reduction in outcome incidence, and was 0.66 at the individual‐level, corresponding to a 34% reduction in outcome risk. Given its flexibility to estimate a variety of user‐specified effects and ability to adaptively adjust for covariates for precision gains while maintaining Type‐I error control, we conclude TMLE is a promising tool for CRT analysis. [ABSTRACT FROM AUTHOR]

Introduction: Person‐centred HIV prevention delivery models that offer structured choices in product, testing and visit location may increase coverage. However, data are lacking on the actual uptake of choices among persons at risk of HIV in southern Africa. In an ongoing randomized study (SEARCH; NCT04810650) in rural East Africa, we evaluated the uptake of choices made when offered in a person‐centred, dynamic choice model for HIV prevention. Methods: Using the PRECEDE framework, we developed a persont‐centred, Dynamic Choice HIV Prevention (DCP) intervention for persons at risk of HIV in three settings in rural Kenya and Uganda: antenatal clinic (ANC), outpatient department (OPD) and in the community. Components include: provider training on product choice (predisposing); flexibility and responsiveness to client desires and choices (pre‐exposure prophylaxis [PrEP]/post‐exposure prophylaxis [PEP], clinic vs. off‐site visits and self‐ or clinician‐based HIV testing) (enabling); and client and staff feedback (reinforcing). All clients received a structured assessment of barriers with personalized plans to address them, mobile phone access to clinicians (24 hours/7 days/week) and integrated reproductive health services. In this interim analysis, we describe the uptake of choices of product, location and testing during the first 24 weeks of follow‐up (April 2021−March 2022). Results: A total of 612 (203 ANC, 197 OPD and 212 community) participants were randomized to the person‐centred DCP intervention. We delivered the DCP intervention in all three settings with diverse populations: ANC: 39% pregnant; median age: 24 years; OPD: 39% male, median age 27 years; and community: 42% male, median age: 29 years. Baseline choice of PrEP was highest in ANC (98%) vs. OPD (84%) and community (40%); whereas the proportion of adults selecting PEP was higher in the community (46%) vs. OPD (8%) and ANC (1%). Personal preference for off‐site visits increased over time (65% at week 24 vs. 35% at baseline). Interest in alternative HIV testing modalities grew over time (38% baseline self‐testing vs. 58% at week 24). Conclusions: A person‐centred model incorporating structured choice in biomedical prevention and care delivery options in settings with demographically diverse groups, in rural Kenya and Uganda, was responsive to varying personal preferences over time in HIV prevention programmes. [ABSTRACT FROM AUTHOR]

Gaussian graphical models (GGMs) are a popular form of network model in which nodes represent features in multivariate normal data and edges reflect conditional dependencies between these features. GGM estimation is an active area of research. Currently available tools for GGM estimation require investigators to make several choices regarding algorithms, scoring criteria, and tuning parameters. An estimated GGM may be highly sensitive to these choices, and the accuracy of each method can vary based on structural characteristics of the network such as topology, degree distribution, and density. Because these characteristics are a priori unknown, it is not straightforward to establish universal guidelines for choosing a GGM estimation method. We address this problem by introducing SpiderLearner, an ensemble method that constructs a consensus network from multiple estimated GGMs. Given a set of candidate methods, SpiderLearner estimates the optimal convex combination of results from each method using a likelihood‐based loss function. K$$ K $$‐fold cross‐validation is applied in this process, reducing the risk of overfitting. In simulations, SpiderLearner performs better than or comparably to the best candidate methods according to a variety of metrics, including relative Frobenius norm and out‐of‐sample likelihood. We apply SpiderLearner to publicly available ovarian cancer gene expression data including 2013 participants from 13 diverse studies, demonstrating our tool's potential to identify biomarkers of complex disease. SpiderLearner is implemented as flexible, extensible, open‐source code in the R package ensembleGGM at https://github.com/katehoffshutta/ensembleGGM. [ABSTRACT FROM AUTHOR]

The protective effects of vaccines may vary depending on individual characteristics, such as age. Traditionally, such effect modification has been examined with subgroup analyses or inclusion of cross‐product terms in regression frameworks. However, in many vaccine settings, effect modification may also depend on the infecting pathogen's characteristics, which are measured postrandomization. Sieve analysis examines whether such effects are present by combining pathogen genetic sequence information with individual‐level data and can generate new hypotheses on the pathways whereby vaccines provide protection. In this article, we develop a causal framework for evaluating effect modification in the context of sieve analysis. Our approach can be used to assess the magnitude of sieve effects and, in particular, whether these effects are modified by individual‐level characteristics. Our method accounts for difficulties occurring in real‐world data analysis, such as competing risks, nonrandomized treatments, and differential dropout. Our approach also integrates modern machine learning techniques. We demonstrate the validity and efficiency of our approach in simulation studies and apply the methodology to a malaria vaccine study. [ABSTRACT FROM AUTHOR]

Introduction: Antiretroviral‐based HIV prevention, including pre‐exposure prophylaxis (PrEP), is expanding in generalized epidemic settings, but additional prevention options are needed for individuals with periodic, high‐risk sexual exposures. Non‐occupational post‐exposure prophylaxis (PEP) is recommended in global guidelines. However, in Africa, awareness of and access to PEP for sexual exposures are limited. We assessed feasibility, acceptability, uptake and adherence in a pilot study of a patient‐centred PEP programme with options for facility‐ or community‐based service delivery. Methods: After population‐level HIV testing with universal access to PrEP for persons at elevated HIV risk (SEARCH Trial:NCT01864603), we conducted a pilot PEP study in five rural communities in Kenya and Uganda between December 2018 and May 2019. We assessed barriers to PEP in the population and implemented an intervention to address these barriers, building on existing in‐country PEP protocols. We used community leaders for sensitization. Test kits and medications were acquired through the Ministry of Health supply chain and healthcare providers based at the Ministry of Health clinics were trained on PEP delivery. Additional intervention components were (a)PEP availability seven days/week, (b)PEP hotline staffed by providers and (c)option for out‐of‐facility medication delivery. We assessed implementation using the Proctor framework and measured seroconversions via repeat HIV testing. Successful "PEP completion" was defined as self‐reported adherence over four weeks of therapy with post‐PEP HIV testing. Results: Community leaders were able to sensitize and mobilize for PEP. The Ministry of Health supplied test kits and PEP medications; after training, healthcare providers delivered the 28‐day regimen with high completion rates. Among 124 persons who sought PEP, 66% were female, 24% were ≤25 years and 42% were fisherfolk. Of these, 20% reported exposure with a serodifferent partner, 72% with a new or existing relationship and 7% from transactional sex. 12% of all visits were conducted at out‐of‐facility community‐based sites; 35% of participants had ≥1 out‐of‐facility visit. No serious adverse events were reported. Overall, 85% met the definition of PEP completion. There were no HIV seroconversions. Conclusions: Among individuals with elevated‐risk exposures in rural East African communities, patient‐centred PEP was feasible, acceptable and provides a promising addition to the current prevention toolkit. [ABSTRACT FROM AUTHOR]

In randomized trials, adjustment for measured covariates during the analysis can reduce variance and increase power. To avoid misleading inference, the analysis plan must be pre-specified. However, it is often unclear a priori which baseline covariates (if any) should be adjusted for in the analysis. Consider, for example, the Sustainable East Africa Research in Community Health (SEARCH) trial for HIV prevention and treatment. There are 16 matched pairs of communities and many potential adjustment variables, including region, HIV prevalence, male circumcision coverage, and measures of community-level viral load. In this paper, we propose a rigorous procedure to data-adaptively select the adjustment set, which maximizes the efficiency of the analysis. Specifically, we use cross-validation to select from a pre-specified library the candidate targeted maximum likelihood estimator (TMLE) that minimizes the estimated variance. For further gains in precision, we also propose a collaborative procedure for estimating the known exposure mechanism. Our small sample simulations demonstrate the promise of the methodology to maximize study power, while maintaining nominal confidence interval coverage. We show how our procedure can be tailored to the scientific question (intervention effect for the study sample vs. for the target population) and study design (pair-matched or not). Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

In cluster randomized trials, the study units usually are not a simple random sample from some clearly defined target population. Instead, the target population tends to be hypothetical or ill-defined, and the selection of study units tends to be systematic, driven by logistical and practical considerations. As a result, the population average treatment effect (PATE) may be neither well defined nor easily interpretable. In contrast, the sample average treatment effect (SATE) is the mean difference in the counterfactual outcomes for the study units. The sample parameter is easily interpretable and arguably the most relevant when the study units are not sampled from some specific super-population of interest. Furthermore, in most settings, the sample parameter will be estimated more efficiently than the population parameter. To the best of our knowledge, this is the first paper to propose using targeted maximum likelihood estimation (TMLE) for estimation and inference of the sample effect in trials with and without pair-matching. We study the asymptotic and finite sample properties of the TMLE for the sample effect and provide a conservative variance estimator. Finite sample simulations illustrate the potential gains in precision and power from selecting the sample effect as the target of inference. This work is motivated by the Sustainable East Africa Research in Community Health (SEARCH) study, a pair-matched, community randomized trial to estimate the effect of population-based HIV testing and streamlined ART on the 5-year cumulative HIV incidence (NCT01864603). The proposed methodology will be used in the primary analysis for the SEARCH trial. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

In randomized trials, pair-matching is an intuitive design strategy to protect study validity and to potentially increase study power. In a common design, candidate units are identified, and their baseline characteristics used to create the best n/2 matched pairs. Within the resulting pairs, the intervention is randomized, and the outcomes measured at the end of follow-up. We consider this design to be adaptive, because the construction of the matched pairs depends on the baseline covariates of all candidate units. As a consequence, the observed data cannot be considered as n/2 independent, identically distributed pairs of units, as common practice assumes. Instead, the observed data consist of n dependent units. This paper explores the consequences of adaptive pair-matching in randomized trials for estimation of the average treatment effect, conditional the baseline covariates of the n study units. By avoiding estimation of the covariate distribution, estimators of this conditional effect will often be more precise than estimators of the marginal effect. We contrast the unadjusted estimator with targeted minimum loss based estimation and show substantial efficiency gains from matching and further gains with adjustment. This work is motivated by the Sustainable East Africa Research in Community Health study, an ongoing community randomized trial to evaluate the impact of immediate and streamlined antiretroviral therapy on HIV incidence in rural East Africa. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]