Designing and analyzing studies of coronavirus disease 2019 and post-acute sequelae of severe acute respiratory syndrome coronavirus 2 among immunocompromised individuals.

Observational studies of coronavirus disease 2019 (COVID-19) among transplant candidates and recipients remain important as immunocompromised patients formed a very small proportion of patients included in COVID-19 trials and large database analyses. We discuss methods that have been used in such analyses to evaluate the impact of vaccination on the risk of symptomatic COVID-19 in such patients and on the probability of developing post-acute sequelae of severe acute respiratory syndrome coronavirus 2 after the onset of infection. We also propose future directions for research and discuss the methods that will be useful to conduct such investigations. The study design and analytical issues that we consider have the potential to be helpful not only for COVID-19 research but also for other infections as well.

Post-acute sequelae of COVID-19 in solid organ transplant recipients.

BACKGROUND: Post-acute sequelae of coronavirus disease 2019 (COVID-19) (PASC), defined as prolonged symptoms following an episode of COVID-19, is not well-characterized in solid organ transplant recipients (SOTR). In this study, we aimed to assess the prevalence of PASC in SOTR, its descriptive characteristics, and associated risk factors. METHODS: We retrospectively identified SOTRs with acute COVID-19 between June 1, 2020 and April 15, 2022, and abstracted demographic and medical history, characteristics of acute COVID-19 illness, and COVID-19 vaccination status. We defined PASC as ongoing/new symptoms present at 6 weeks or longer following acute COVID-19 diagnosis. RESULTS: Among 208 SOTRs with acute COVID-19, 72 (35%) developed PASC. Common symptoms were respiratory symptoms (67%), headache (40%), and difficulty concentrating (10%). Severe acute COVID-19 disease and presence of respiratory symptoms were associated with higher odds of PASC in multivariable analyses, while receipt of at least one COVID-19 vaccination prior to transplantation was protective. CONCLUSION: We found that PASC occurs in about a third of SOTRs with acute COVID-19 and has similar symptoms as described previously in immunocompetent hosts. Pre-transplant vaccination may be protective. Further prospective multicenter studies are needed.

Modeling homophily in dynamic networks with application to HIV molecular surveillance.

BACKGROUND: Efforts to control the HIV epidemic can benefit from knowledge of the relationships between the characteristics of people who have transmitted HIV and those who became infected by them. Investigation of this relationship is facilitated by the use of HIV genetic linkage analyses, which allows inference about possible transmission events among people with HIV infection. Two persons with HIV (PWH) are considered linked if the genetic distance between their HIV sequences is less than a given threshold, which implies proximity in a transmission network. The tendency of pairs of nodes (in our case PWH) that share (or differ in) certain attributes to be linked is denoted homophily. Below, we describe a novel approach to modeling homophily with application to analyses of HIV viral genetic sequences from clinical series of participants followed in San Diego. Over the 22-year period of follow-up, increases in cluster size results from HIV transmissions to new people from those already in the cluster-either directly or through intermediaries. METHODS: Our analytical approach makes use of a logistic model to describe homophily with regard to demographic, clinical, and behavioral characteristics-that is we investigate whether similarities (or differences) between PWH in these characteristics are associated with their sequences being linked. To investigate the performance of our methods, we conducted on a simulation study for which data sets were generated in a way that reproduced the structure of the observed database. RESULTS: Our results demonstrated strong positive homophily associated with hispanic ethnicity, and strong negative homophily, with birth year difference. The second result implies that the larger the difference between the age of a newly-infected PWH and the average age for an available cluster, the lower the odds of a newly infected person joining that cluster. We did not observe homophily associated with prior diagnosis of sexually transmitted diseases. Our simulation studies demonstrated the validity of our approach for modeling homophily, by showing that the estimates it produced matched the specified values of the statistical network generating model. CONCLUSIONS: Our novel methods provide a simple and flexible statistical network-based approach for modeling the growth of viral (or other microbial) genetic clusters from linkage to new infections based on genetic distance.

Leveraging Contact Network Information in Clustered Randomized Studies of Contagion Processes.

In a randomized study, leveraging covariates related to the outcome (e.g. disease status) may produce less variable estimates of the effect of exposure. For contagion processes operating on a contact network, transmission can only occur through ties that connect affected and unaffected individuals; the outcome of such a process is known to depend intimately on the structure of the network. In this paper, we investigate the use of contact network features as efficiency covariates in exposure effect estimation. Using augmented generalized estimating equations (GEE), we estimate how gains in efficiency depend on the network structure and spread of the contagious agent or behavior. We apply this approach to simulated randomized trials using a stochastic compartmental contagion model on a collection of model-based contact networks and compare the bias, power, and variance of the estimated exposure effects using an assortment of network covariate adjustment strategies. We also demonstrate the use of network-augmented GEEs on a clustered randomized trial evaluating the effects of wastewater monitoring on COVID-19 cases in residential buildings at the the University of California San Diego.

Network methods and design of randomized trials: Application to investigation of COVID-19 vaccination boosters.

Network science methods can be useful in design, monitoring, and analysis of randomized trials for control of spread of infections. Their usefulness arises from the role of statistical network models in molecular epidemiology and in study design. Computational models, such as agent-based models that propagate disease on simulated contact networks, can be used to investigate the properties of different study designs and analysis plans. Particularly valuable is the use of these methods to assess how magnitude and detectability of intervention effects depend on both individual-level and network-level characteristics of the enrolled populations. Such investigation also provides an important approach to assessing consequences of study data being incomplete or measured with error. To address these goals, we consider two statistical network models: exponential random graph models and the more flexible congruence class models. We focus first on an historical use of these methods in design and monitoring of a cluster randomized trial in Botswana to evaluate the effect of combination HIV prevention modalities compared to standard of care on HIV incidence. We then present a framework for the design of a study of booster vaccine effects on infection with, and forward transmission of, SARS-CoV-2 variants. Motivation for the study is driven in part by guidance from the United Kingdom to base approval of booster vaccines with "strain changes" that target variants on results of neutralizing antibody tests and information about safety, but without requiring evidence of clinical efficacy. Using designs informed by our agent-based network models, we show it may be feasible to conduct a trial of novel SARS-CoV-2 vaccines in a single large campus to obtain useful information regarding vaccine efficacy against susceptibility and infectiousness. If needed, the sample size could be increased by extending the study to a small number of campuses. Novel network methods may be useful in developing pragmatic SARS-CoV-2 vaccine trials that can leverage existing infrastructure to reduce costs and hasten the development of results.

Ensemble clustering of longitudinal bivariate HIV biomarker profiles to group patients by patterns of disease progression.

This paper describes an ensemble cluster analysis of bivariate profiles of HIV biomarkers, viral load and CD4 cell counts, which jointly measure disease progression. Data are from a prevalent cohort of HIV positive participants in a clinical trial of vitamin supplementation in Botswana. These individuals were HIV positive upon enrollment, but with unknown times of infection. To categorize groups of participants based on their patterns of progression of HIV infection using both biomarkers, we combine univariate shape-based cluster results for multiple biomarkers through the use of ensemble clustering methods. We first describe univariate clustering for each of the individual biomarker profiles, and make use of shape-respecting distances for clustering the longitudinal profile data. In our data, profiles are subject to either missing or irregular measurements as well as unobserved initiation times of the process of interest. Shape-respecting distances that can handle such data issues, preserve time-ordering, and identify similar profile shapes are useful in identifying patterns of disease progression from longitudinal biomarker data. However, their performance with regard to clustering differs by severity of the data issues mentioned above. We provide an empirical investigation of shape-respecting distances (Fréchet and dynamic time warping (DTW)) on benchmark shape data, and use DTW in cluster analysis of biomarker profile observations. These reveal a primary group of 'typical progressors,' as well as a smaller group that shows relatively rapid progression. We then refine the analysis using ensemble clustering for both markers to obtain a single classification. The information from joint evaluation of the two biomarkers combined with ensemble clustering reveals subgroups of patients not identifiable through univariate analyses; noteworthy subgroups are those that appear to represent recently and chronically infected subsets. Supplementary Information: The online version contains supplementary material available at 10.1007/s41060-022-00323-2.

Deep-sequence phylogenetics to quantify patterns of HIV transmission in the context of a universal testing and treatment trial - BCPP/Ya Tsie trial.

Background: Mathematical models predict that community-wide access to HIV testing-and-treatment can rapidly and substantially reduce new HIV infections. Yet several large universal test-and-treat HIV prevention trials in high-prevalence epidemics demonstrated variable reduction in population-level incidence. Methods: To elucidate patterns of HIV spread in universal test-and-treat trials, we quantified the contribution of geographic-location, gender, age, and randomized-HIV-intervention to HIV transmissions in the 30-community Ya Tsie trial in Botswana. We sequenced HIV viral whole genomes from 5114 trial participants among the 30 trial communities. Results: Deep-sequence phylogenetic analysis revealed that most inferred HIV transmissions within the trial occurred within the same or between neighboring communities, and between similarly aged partners. Transmissions into intervention communities from control communities were more common than the reverse post-baseline (30% [12.2 - 56.7] vs. 3% [0.1 - 27.3]) than at baseline (7% [1.5 - 25.3] vs. 5% [0.9 - 22.9]) compatible with a benefit from treatment-as-prevention. Conclusions: Our findings suggest that population mobility patterns are fundamental to HIV transmission dynamics and to the impact of HIV control strategies. Funding: This study was supported by the National Institute of General Medical Sciences (U54GM088558), the Fogarty International Center (FIC) of the U.S. National Institutes of Health (D43 TW009610), and the President's Emergency Plan for AIDS Relief through the Centers for Disease Control and Prevention (CDC) (Cooperative agreements U01 GH000447 and U2G GH001911).

Effective Human Immunodeficiency Virus Molecular Surveillance Requires Identification of Incident Cases of Infection.

BACKGROUND: Ending the human immunodeficiency virus (HIV) epidemic requires knowledge of key drivers of spread of HIV infection. METHODS: Between 1996 and 2018, 1119 newly and previously diagnosed, therapy-naive persons with HIV (PWH) from San Diego were followed. A genetic distance-based network was inferred using pol sequences, and genetic clusters grew over time through linkage of sequences from newly observed infections. Cox proportional hazards models were used to identify factors associated with the rate of growth. These results were used to predict the impact of a hypothetical intervention targeting PWH with incident infection. Comparison was made to the Centers for Disease Control and Prevention (CDC) Ending the HIV Epidemic (EHE) molecular surveillance strategy, which prioritizes clusters recently linked to all new HIV diagnoses and does not incorporate data on incident infections. RESULTS: Overall, 219 genetic linkages to incident infections were identified over a median follow-up of 8.8 years. Incident cluster growth was strongly associated with proportion of PWH in the cluster who themselves had incident infection (hazard ratio, 44.09 [95% confidence interval, 17.09-113.78]). The CDC EHE molecular surveillance strategy identified 11 linkages to incident infections a genetic distance threshold of 0.5%, and 24 linkages at 1.5%. CONCLUSIONS: Over the past 2 decades, incident infections drove incident HIV cluster growth in San Diego. The current CDC EHE molecular detection and response strategy would not have identified most transmission events arising from those with incident infection in San Diego. Molecular surveillance that includes detection of incident cases will provide a more effective strategy for EHE.

The vitamin D for COVID-19 (VIVID) trial: A pragmatic cluster-randomized design.

OBJECTIVES: To determine the effect of vitamin D supplementation on disease progression and post-exposure prophylaxis for COVID-19 infection. We hypothesize that high-dose vitamin D3 supplementation will reduce risk of hospitalization/death among those with recently diagnosed COVID-19 infection and will reduce risk of COVID-19 infection among their close household contacts. METHODS: We report the rationale and design of a planned pragmatic, cluster randomized, double-blinded trial (N = 2700 in total nationwide), with 1500 newly diagnosed individuals with COVID-19 infection, together with up to one close household contact each (~1200 contacts), randomized to either vitamin D3 (loading dose, then 3200 IU/day) or placebo in a 1:1 ratio and a household cluster design. The study duration is 4 weeks. The primary outcome for newly diagnosed individuals is the occurrence of hospitalization and/or mortality. Key secondary outcomes include symptom severity scores among cases and changes in the infection (seroconversion) status for their close household contacts. Changes in vitamin D 25(OH)D levels will be assessed and their relation to study outcomes will be explored. CONCLUSIONS: The proposed pragmatic trial will allow parallel testing of vitamin D3 supplementation for early treatment and post-exposure prophylaxis of COVID-19. The household cluster design provides a cost-efficient approach to testing an intervention for reducing rates of hospitalization and/or mortality in newly diagnosed cases and preventing infection among their close household contacts.

Bayesian reconstruction of transmission trees from genetic sequences and uncertain infection times.

Genetic sequence data of pathogens are increasingly used to investigate transmission dynamics in both endemic diseases and disease outbreaks. Such research can aid in the development of appropriate interventions and in the design of studies to evaluate them. Several computational methods have been proposed to infer transmission chains from sequence data; however, existing methods do not generally reliably reconstruct transmission trees because genetic sequence data or inferred phylogenetic trees from such data contain insufficient information for accurate estimation of transmission chains. Here, we show by simulation studies that incorporating infection times, even when they are uncertain, can greatly improve the accuracy of reconstruction of transmission trees. To achieve this improvement, we propose a Bayesian inference methods using Markov chain Monte Carlo that directly draws samples from the space of transmission trees under the assumption of complete sampling of the outbreak. The likelihood of each transmission tree is computed by a phylogenetic model by treating its internal nodes as transmission events. By a simulation study, we demonstrate that accuracy of the reconstructed transmission trees depends mainly on the amount of information available on times of infection; we show superiority of the proposed method to two alternative approaches when infection times are known up to specified degrees of certainty. In addition, we illustrate the use of a multiple imputation framework to study features of epidemic dynamics, such as the relationship between characteristics of nodes and average number of outbound edges or inbound edges, signifying possible transmission events from and to nodes. We apply the proposed method to a transmission cluster in San Diego and to a dataset from the 2014 Sierra Leone Ebola virus outbreak and investigate the impact of biological, behavioral, and demographic factors.

Efficient estimation of human immunodeficiency virus incidence rate using a pooled cross-sectional cohort study design.

Development of methods to accurately estimate human immunodeficiency virus (HIV) incidence rate remains a challenge. Ideally, one would follow a random sample of HIV-negative individuals under a longitudinal study design and identify incident cases as they arise. Such designs can be prohibitively resource intensive and therefore alternative designs may be preferable. We propose such a simple, less resource-intensive study design and develop a weighted log likelihood approach which simultaneously accounts for selection bias and outcome misclassification error. The design is based on a cross-sectional survey which queries individuals' time since last HIV-negative test, validates their test results with formal documentation whenever possible, and tests all persons who do not have documentation of being HIV-positive. To gain efficiency, we update the weighted log likelihood function with potentially misclassified self-reports from individuals who could not produce documentation of a prior HIV-negative test and investigate large sample properties of validated sub-sample only versus pooled sample estimators through extensive Monte Carlo simulations. We illustrate our method by estimating incidence rate for individuals who tested HIV-negative within 1.5 and 5 years prior to Botswana Combination Prevention Project enrolment. This article establishes that accurate estimates of HIV incidence rate can be obtained from individuals' history of testing in a cross-sectional cohort study design by appropriately accounting for selection bias and misclassification error. Moreover, this approach is notably less resource-intensive compared to longitudinal and laboratory-based methods.

Bayesian method for inferring the impact of geographical distance on intensity of communication.

Spatially-embedded networks represent a large class of real-world networks of great scientific and societal interest. For example, transportation networks (such as railways), communication networks (such as Internet routers), and biological networks (such as fungal foraging networks) are all spatially embedded. Both the density of interactions (presence of edges) and intensity of interactions (edge weights) are typically found to decrease as a function of spatial separation of nodes in these networks. Communication and mobility of groups of individuals have also been shown to decline with their spatial separation, and the so-called gravity model postulates that this decline takes the form of a power-law holding at all distances. There is however some evidence that the rate of decline might change as the distance increases beyond a certain value, called a change point, but there have been few statistically principled methods for determining the existence and location of change points or assessing the change in intensity of interactions associated with them. We introduce such a method within the Bayesian paradigm and apply it to anonymized mobile call detail records (CDRs). Our results are potentially useful in settings where understanding social and spatial mixing of people is important, such as in the design of cluster randomized trials for studying interventions for infectious diseases, but we also anticipate the method to be useful for investigating more generally how distance may affect tie strengths in general in spatially embedded networks.

Mapping of HIV-1C Transmission Networks Reveals Extensive Spread of Viral Lineages Across Villages in Botswana Treatment-as-Prevention Trial.

BACKGROUND: Phylogenetic mapping of HIV-1 lineages circulating across defined geographical locations is promising for better understanding HIV transmission networks to design optimal prevention interventions. METHODS: We obtained near full-length HIV-1 genome sequences from people living with HIV (PLWH), including participants on antiretroviral treatment in the Botswana Combination Prevention Project, conducted in 30 Botswana communities in 2013-2018. Phylogenetic relationships among viral sequences were estimated by maximum likelihood. RESULTS: We obtained 6078 near full-length HIV-1C genome sequences from 6075 PLWH. We identified 984 phylogenetically distinct HIV-1 lineages (molecular HIV clusters) circulating in Botswana by mid-2018, with 2-27 members per cluster. Of these, dyads accounted for 62%, approximately 32% (n = 316) were found in single communities, and 68% (n = 668) were spread across multiple communities. Men in clusters were approximately 3 years older than women (median age 42 years, vs 39 years; P < .0001). In 65% of clusters, men were older than women, while in 35% of clusters women were older than men. The majority of identified viral lineages were spread across multiple communities. CONCLUSIONS: A large number of circulating phylogenetically distinct HIV-1C lineages (molecular HIV clusters) suggests highly diversified HIV transmission networks across Botswana communities by 2018.

Population uptake of HIV testing, treatment, viral suppression, and male circumcision following a community-based intervention in Botswana (Ya Tsie/BCPP): a cluster-randomised trial.

BACKGROUND: In settings with high HIV prevalence and treatment coverage, such as Botswana, it is unknown whether uptake of HIV prevention and treatment interventions can be increased further. We sought to determine whether a community-based intervention to identify and rapidly treat people living with HIV, and support male circumcision could increase population levels of HIV diagnosis, treatment, viral suppression, and male circumcision in Botswana. METHODS: The Ya Tsie Botswana Combination Prevention Project study was a pair-matched cluster-randomised trial done in 30 communities across Botswana done from Oct 30, 2013, to June 30, 2018. 15 communities were randomly assigned to receive HIV prevention and treatment interventions, including enhanced HIV testing, earlier antiretroviral therapy (ART), and strengthened male circumcision services, and 15 received standard of care. The first primary endpoint of HIV incidence has already been reported. In this Article, we report findings for the second primary endpoint of population uptake of HIV prevention services, as measured by proportion of people known to be HIV-positive or tested HIV-negative in the preceding 12 months; proportion of people living with HIV diagnosed and on ART; proportion of people living with HIV on ART with viral suppression; and proportion of HIV-negative men circumcised. A longitudinal cohort of residents aged 16-64 years from a random, approximately 20% sample of households across the 15 communities was enrolled to assess baseline uptake of study outcomes; we also administered an end-of-study survey to all residents not previously enrolled in the longitudinal cohort to provide study end coverage estimates. Differences in intervention uptake over time by randomisation group were tested via paired Student's t test. The study has been completed and is registered with ClinicalTrials.gov (NCT01965470). FINDINGS: In the six communities participating in the end-of-study survey, 2625 residents (n=1304 from standard-of-care communities, n=1321 from intervention communities) were enrolled into the 20% longitudinal cohort at baseline from Oct 30, 2013, to Nov 24, 2015. In the same communities, 10 791 (86%) of 12 489 eligible enumerated residents not previously enrolled in the longitudinal cohort participated in the end-of-study survey from March 30, 2017, to Feb 25, 2018 (5896 in intervention and 4895 in standard-of-care communities). At study end, in intervention communities, 1228 people living with HIV (91% of 1353) were on ART; 1166 people living with HIV (88% of 1321 with available viral load) were virally suppressed, and 673 HIV-negative men (40% of 1673) were circumcised in intervention communities. After accounting for baseline differences, at study end the proportion of people living with HIV who were diagnosed was significantly higher in intervention communities (absolute increase of 9% to 93%) compared with standard-of-care communities (absolute increase of 2% to 88%; prevalence ratio [PR] 1·08 [95% CI 1·02-1·14], p=0·032). Population levels of ART, viral suppression, and male circumcision increased from baseline in both groups, with greater increases in intervention communities (ART PR 1·12 [95% CI 1·07-1·17], p=0·018; viral suppression 1·13 [1·09-1·17], p=0·017; male circumcision 1·26 [1·17-1·35], p=0·029). INTERPRETATION: It is possible to achieve very high population levels of HIV testing and treatment in a high-prevalence setting. Maintaining these coverage levels over the next decade could substantially reduce HIV transmission and potentially eliminate the epidemic in these areas. FUNDING: US President's Emergency Plan for AIDS Relief through the Centers for Disease Control and Prevention.

A flexible nonlinear mixed effects model for HIV viral load rebound after treatment interruption.

Characterization of HIV viral rebound after the discontinuation of antiretroviral therapy is central to HIV cure research. We propose a parametric nonlinear mixed effects model for the viral rebound trajectory, which often has a rapid rise to a peak value followed by a decrease to a viral load set point. We choose a flexible functional form that captures the shapes of viral rebound trajectories and can also provide biological insights regarding the rebound process. Each parameter can incorporate a random effect to allow for variation in parameters across individuals. Key features of viral rebound trajectories such as viral set points are represented by the parameters in the model, which facilitates assessment of intervention effects and identification of important pretreatment interruption predictors for these features. We employ a stochastic expectation-maximization (StEM) algorithm to incorporate HIV-1 RNA values that are below the lower limit of assay quantification. We evaluate the performance of our model in simulation studies and apply the proposed model to longitudinal HIV-1 viral load data from five AIDS Clinical Trials Group treatment interruption studies.

Bottom-up and top-down neural signatures of disordered multi-talker speech perception in adults with normal hearing.

In social settings, speech waveforms from nearby speakers mix together in our ear canals. Normally, the brain unmixes the attended speech stream from the chorus of background speakers using a combination of fast temporal processing and cognitive active listening mechanisms. Of >100,000 patient records,~10% of adults visited our clinic because of reduced hearing, only to learn that their hearing was clinically normal and should not cause communication difficulties. We found that multi-talker speech intelligibility thresholds varied widely in normal hearing adults, but could be predicted from neural phase-locking to frequency modulation (FM) cues measured with ear canal EEG recordings. Combining neural temporal fine structure processing, pupil-indexed listening effort, and behavioral FM thresholds accounted for 78% of the variability in multi-talker speech intelligibility. The disordered bottom-up and top-down markers of poor multi-talker speech perception identified here could inform the design of next-generation clinical tests for hidden hearing disorders.

Universal Testing, Expanded Treatment, and Incidence of HIV Infection in Botswana.

BACKGROUND: The feasibility of reducing the population-level incidence of human immunodeficiency virus (HIV) infection by increasing community coverage of antiretroviral therapy (ART) and male circumcision is unknown. METHODS: We conducted a pair-matched, community-randomized trial in 30 rural or periurban communities in Botswana from 2013 to 2018. Participants in 15 villages in the intervention group received HIV testing and counseling, linkage to care, ART (started at a higher CD4 count than in standard care), and increased access to male circumcision services. The standard-care group also consisted of 15 villages. Universal ART became available in both groups in mid-2016. We enrolled a random sample of participants from approximately 20% of households in each community and measured the incidence of HIV infection through testing performed approximately once per year. The prespecified primary analysis was a permutation test of HIV incidence ratios. Pair-stratified Cox models were used to calculate 95% confidence intervals. RESULTS: Of 12,610 enrollees (81% of eligible household members), 29% were HIV-positive. Of the 8974 HIV-negative persons (4487 per group), 95% were retested for HIV infection over a median of 29 months. A total of 57 participants in the intervention group and 90 participants in the standard-care group acquired HIV infection (annualized HIV incidence, 0.59% and 0.92%, respectively). The unadjusted HIV incidence ratio in the intervention group as compared with the standard-care group was 0.69 (P = 0.09) by permutation test (95% confidence interval [CI], 0.46 to 0.90 by pair-stratified Cox model). An end-of-trial survey in six communities (three per group) showed a significantly greater increase in the percentage of HIV-positive participants with an HIV-1 RNA level of 400 copies per milliliter or less in the intervention group (18 percentage points, from 70% to 88%) than in the standard-care group (8 percentage points, from 75% to 83%) (relative risk, 1.12; 95% CI, 1.09 to 1.16). The percentage of men who underwent circumcision increased by 10 percentage points in the intervention group and 2 percentage points in the standard-care group (relative risk, 1.26; 95% CI, 1.17 to 1.35). CONCLUSIONS: Expanded HIV testing, linkage to care, and ART coverage were associated with increased population viral suppression. (Funded by the President's Emergency Plan for AIDS Relief and others; Ya Tsie ClinicalTrials.gov number, NCT01965470.).

Short Communication: Viral Genetic Linkage Analysis Among Black Men Who Have Sex With Men (HIV Prevention Trials Network 061).

Analysis of viral genetic linkage can reveal generalized transmission patterns within a population. The HIV Prevention Trials Network 061 study evaluated HIV incidence among black men who have sex with men. HIV genotypes from 169 men who were HIV infected at enrollment and 23 men who seroconverted during the study were analyzed for genetic linkage. This analysis showed some associations of viral linkage with income, study site, and timing of infection.

Designing & Conducting Trials To Reliably Evaluate HIV Prevention Interventions.

While much has been achieved, much remains to be accomplished in the science of preventing the spread of HIV infection. Clinical trials that are properly designed, conducted and analyzed are of integral importance in the pursuit of reliable insights about HIV prevention. As we build on previous scientific breakthroughs, there will be an increasing need for clinical trials to be designed to efficiently achieve insights without compromising their reliability and generalizability. Key design features should continue to include: 1) the use of randomization and evidence-based controls, 2) specifying the use of intention-to-treat analyses to preserve the integrity of randomization and to increase interpretability of results, 3) obtaining direct assessments of effects on clinical endpoints such as the risk of HIV infection, 4) using either superiority designs or non-inferiority designs with rigorous non-inferiority margins, and 5) enhancing generalizability through the choice of a relative risk rather than risk difference metric. When interventions have complementary and potentially synergistic effects, factorial designs should be considered to increase efficiency as well as to obtain clinically important insights about interaction and the contribution of component interventions to the efficacy and safety of combination regimens. Key trial conduct issues include timely enrollment of participants at high HIV risk recruited from populations with high viral burden, obtaining 'best real-world achievable' levels of adherence to the interventions being assessed and ensuring high levels of retention. High quality of trial conduct occurs through active rather than passive monitoring, using pre-specified targeted levels of performance with defined methods to achieve those targets. During trial conduct, active monitoring of the performance standards not only holds the trial leaders accountable but also can assist in the development and implementation of creative alternative approaches to increase the quality of trial conduct. Designing, conducting and analyzing HIV prevention trials with the quality needed to obtain reliable insights is an ethical as well as scientific imperative.

Simulations for designing and interpreting intervention trials in infectious diseases.

BACKGROUND: Interventions in infectious diseases can have both direct effects on individuals who receive the intervention as well as indirect effects in the population. In addition, intervention combinations can have complex interactions at the population level, which are often difficult to adequately assess with standard study designs and analytical methods. DISCUSSION: Herein, we urge the adoption of a new paradigm for the design and interpretation of intervention trials in infectious diseases, particularly with regard to emerging infectious diseases, one that more accurately reflects the dynamics of the transmission process. In an increasingly complex world, simulations can explicitly represent transmission dynamics, which are critical for proper trial design and interpretation. Certain ethical aspects of a trial can also be quantified using simulations. Further, after a trial has been conducted, simulations can be used to explore the possible explanations for the observed effects. CONCLUSION: Much is to be gained through a multidisciplinary approach that builds collaborations among experts in infectious disease dynamics, epidemiology, statistical science, economics, simulation methods, and the conduct of clinical trials.