Comparative Diagnostic Utility of SARS-CoV-2 Rapid Antigen and Molecular Testing in a Community Setting.

BACKGROUND: SARS-CoV-2 antigen-detection rapid diagnostic tests (Ag-RDTs) have become widely utilized but longitudinal characterization of their community-based performance remains incompletely understood. METHODS: This prospective longitudinal study at a large public university in Seattle, WA utilized remote enrollment, online surveys, and self-collected nasal swab specimens to evaluate Ag-RDT performance against real-time reverse transcription polymerase chain reaction (rRT-PCR) in the context of SARS-CoV-2 Omicron. Ag-RDT sensitivity and specificity within 1 day of rRT-PCR were evaluated by symptom status throughout the illness episode and Orf1b cycle threshold (Ct). RESULTS: From February to December 2022, 5757 participants reported 17 572 Ag-RDT results and completed 12 674 rRT-PCR tests, of which 995 (7.9%) were rRT-PCR positive. Overall sensitivity and specificity were 53.0% (95% confidence interval [CI], 49.6%-56.4%) and 98.8% (95% CI, 98.5%-99.0%), respectively. Sensitivity was comparatively higher for Ag-RDTs used 1 day after rRT-PCR (69.0%), 4-7 days after symptom onset (70.1%), and Orf1b Ct ≤20 (82.7%). Serial Ag-RDT sensitivity increased with repeat testing ≥2 (68.5%) and ≥4 (75.8%) days after an initial Ag-RDT-negative result. CONCLUSIONS: Ag-RDT performance varied by clinical characteristics and temporal testing patterns. Our findings support recommendations for serial testing following an initial Ag-RDT-negative result, especially among recently symptomatic persons or those at high risk for SARS-CoV-2 infection.

Clinical and Genomic Epidemiology of Coxsackievirus A21 and Enterovirus D68 in Homeless Shelters, King County, Washington, USA, 2019-2021.

Congregate homeless shelters are disproportionately affected by infectious disease outbreaks. We describe enterovirus epidemiology across 23 adult and family shelters in King County, Washington, USA, during October 2019-May 2021, by using repeated cross-sectional respiratory illness and environmental surveillance and viral genome sequencing. Among 3,281 participants >3 months of age, we identified coxsackievirus A21 (CVA21) in 39 adult residents (3.0% [95% CI 1.9%-4.8%] detection) across 7 shelters during October 2019-February 2020. We identified enterovirus D68 (EV-D68) in 5 adult residents in 2 shelters during October-November 2019. Of 812 environmental samples, 1 was EV-D68-positive and 5 were CVA21-positive. Other enteroviruses detected among residents, but not in environmental samples, included coxsackievirus A6/A4 in 3 children. No enteroviruses were detected during April 2020-May 2021. Phylogenetically clustered CVA21 and EV-D68 cases occurred in some shelters. Some shelters also hosted multiple CVA21 lineages.

Can the potential benefit of individualizing treatment be assessed using trial summary statistics alone?

Individualizing treatment assignment can improve outcomes for diseases with patient-to-patient variability in comparative treatment effects. When a clinical trial demonstrates that some patients improve on treatment while others do not, it is tempting to assume that treatment effect heterogeneity exists. However, if outcome variability is mainly driven by factors other than variability in the treatment effect, investigating the extent to which covariate data can predict differential treatment response is a potential waste of resources. Motivated by recent meta-analyses assessing the potential of individualizing treatment for major depressive disorder using only summary statistics, we provide a method that uses summary statistics widely available in published clinical trial results to bound the benefit of optimally assigning treatment to each patient. We also offer alternate bounds for settings in which trial results are stratified by another covariate. Our upper bounds can be especially informative when they are small, as there is then little benefit to collecting additional covariate data. We demonstrate our approach using summary statistics from a depression treatment trial. Our methods are implemented in the rct2otrbounds R package.

A controlled effects approach to assessing immune correlates of protection.

An immune correlate of risk (CoR) is an immunologic biomarker in vaccine recipients associated with an infectious disease clinical endpoint. An immune correlate of protection (CoP) is a CoR that can be used to reliably predict vaccine efficacy (VE) against the clinical endpoint and hence is accepted as a surrogate endpoint that can be used for accelerated approval or guide use of vaccines. In randomized, placebo-controlled trials, CoR analysis is limited by not assessing a causal vaccine effect. To address this limitation, we construct the controlled risk curve of a biomarker, which provides the causal risk of an endpoint if all participants are assigned vaccine and the biomarker is set to different levels. Furthermore, we propose a causal CoP analysis based on controlled effects, where for the important special case that the biomarker is constant in the placebo arm, we study the controlled vaccine efficacy curve that contrasts the controlled risk curve with placebo arm risk. We provide identification conditions and formulae that account for right censoring of the clinical endpoint and two-phase sampling of the biomarker, and consider G-computation estimation and inference under a semiparametric model such as the Cox model. We add modular approaches to sensitivity analysis that quantify robustness of CoP evidence to unmeasured confounding. We provide an application to two phase 3 trials of a dengue vaccine indicating that controlled risk of dengue strongly varies with 50$\%$ neutralizing antibody titer. Our work introduces controlled effects causal mediation analysis to immune CoP evaluation.

A controlled effects approach to assessing immune correlates of protection.

An immune correlate of risk (CoR) is an immunologic biomarker in vaccine recipients associated with an infectious disease clinical endpoint. An immune correlate of protection (CoP) is a CoR that can be used to reliably predict vaccine efficacy (VE) against the clinical endpoint and hence is accepted as a surrogate endpoint that can be used for accelerated approval or guide use of vaccines. In randomized, placebo-controlled trials, CoR analysis is limited by not assessing a causal vaccine effect. To address this limitation, we construct the controlled risk curve of a biomarker, which provides the causal risk of an endpoint if all participants are assigned vaccine and the biomarker is set to different levels. Furthermore, we propose a causal CoP analysis based on controlled effects, where for the important special case that the biomarker is constant in the placebo arm, we study the controlled vaccine efficacy curve that contrasts the controlled risk curve with placebo arm risk. We provide identification conditions and formulae that account for right censoring of the clinical endpoint and two-phase sampling of the biomarker, and consider G-computation estimation and inference under a semiparametric model such as the Cox model. We add modular approaches to sensitivity analysis that quantify robustness of CoP evidence to unmeasured confounding. We provide an application to two phase 3 trials of a dengue vaccine indicating that controlled risk of dengue strongly varies with 50$\%$ neutralizing antibody titer. Our work introduces controlled effects causal mediation analysis to immune CoP evaluation.

Scrutinizing human resources for health availability and distribution in Mozambique between 2016 and 2020: a subnational descriptive longitudinal study.

INTRODUCTION: Overall, resilient health systems build upon sufficient, qualified, well-distributed, and motivated health workers; however, this precious resource is limited in numbers to meet people's demands, particularly in LMICs. Understanding the subnational distribution of health workers from different lens is critical to ensure quality healthcare and improving health outcomes. METHODS: Using data from Health Personnel Information System, facility-level Service Availability and Readiness Assessment, and other sources, we performed a district-level longitudinal analysis to assess health workforce density and the ratio of male to female health workers between January 2016 and June 2020 across all districts in Mozambique. RESULTS: 22 011 health workers were sampled, of whom 10 405 (47.3%) were male. The average age was 35 years (SD: 9.4). Physicians (1025, 4.7%), maternal and child health nurses (4808, 21.8%), and nurses (6402, 29.1%) represented about 55% of the sample. In January 2016, the average district-level workforce density was 75.8 per 100 000 population (95% CI 65.9, 87.1), and was increasing at an annual rate of 8.0% (95% CI 6.00, 9.00) through January 2018. The annual growth rate declined to 3.0% (95% CI 2.00, 4.00) after January 2018. Two provinces, Maputo City and Maputo Province, with 268.3 (95% CI 186.10, 387.00) and 104.6 (95% CI 84.20, 130.00) health workers per 100 000 population, respectively, had the highest workforce density at baseline (2016). There were 3122 community health workers (CHW), of whom 72.8% were male, in January 2016. The average number of CHWs per 10 000 population was 1.33 (95% CI 1.11, 1.59) in 2016 and increased by 18% annually between January 2016 and January 2018. This trend reduced to 11% (95% CI 0.00, 13.00) after January 2018. The sex ratio was twice as high for all provinces in the central and northern regions relative to Maputo Province. Maputo City (OR: 0.34; 95% CI 0.32, 0.34) and Maputo Province (OR: 0.56; 95% CI 0.49, 0.65) reported the lowest sex ratio at the baseline. Encouragingly, important sex ratio improvements were observed after January 2018, particularly in the northern and central regions. CONCLUSION: Mozambique made substantial progress in health workers' availability during the study period; however, with a critical slowdown after 2018. Despite the progress, meaningful shortages and distribution disparities persist.

Burden of long COVID among adults experiencing sheltered homelessness: a longitudinal cohort study in King County, WA between September 2020-April 2022.

BACKGROUND: People experiencing homelessness (PEH) are at increased risk for acquiring SARS-CoV-2, but the burden of long COVID in this population is unknown. METHODS: We conducted a matched prospective cohort study to assess the prevalence, characteristics, and impact of long COVID among sheltered PEH in Seattle, WA between September 2020-April 2022. Adults ≥ 18 years, residing across nine homeless shelters with active respiratory virus surveillance, were eligible to complete in-person baseline surveys and interval follow-up phone surveys. We included a subset of 22 COVID-19-positive cases who tested positive or inconclusive for SARS-CoV-2 and 44 COVID-19-negative controls who tested negative for SARS-CoV-2, frequency matched on age and sex. Among controls, 22 were positive and 22 were negative for one of 27 other respiratory virus pathogens. To assess the impact of COVID-19 on the risk of symptom presence at follow-up (day 30-225 post-enrollment test), we performed log-linear regression with robust standard errors, adjusting for confounding by shelter site and demographic variables determined a priori. RESULTS: Of 53 eligible COVID-19 cases, 22 (42%) completed ≥ 1 follow-up survey. While five (23%) cases reported ≥ 1 symptom at baseline, this increased to 77% (10/13) between day 30-59 and 33% (4/12) day 90 + . The most commonly reported symptoms day 30 + were fatigue (27%) and rhinorrhea (27%), with 8 (36%) reporting symptoms that interfered with or prevented daily activities. Four (33%) symptomatic cases reported receiving medical care outside of a medical provider at an isolation facility. Of 44 controls, 12 (27%) reported any symptoms day 90 + . Risk of any symptoms at follow-up was 5.4 times higher among COVID-19 cases compared to controls (95% CI: 2.7-10.5). CONCLUSIONS: Shelter residents reported a high prevalence of symptoms 30 + days after their SARS-CoV-2 detection, though few accessed medical care for persistent illness. The impact of COVID-19 extends beyond acute illness and may exacerbate existing challenges that marginalized populations face in maintaining their health and wellbeing.

Marginal Bayesian Posterior Inference using Recurrent Neural Networks with Application to Sequential Models.

In Bayesian data analysis, it is often important to evaluate quantiles of the posterior distribution of a parameter of interest (e.g., to form posterior intervals). In multi-dimensional problems, when non-conjugate priors are used, this is often difficult generally requiring either an analytic or sampling-based approximation, such as Markov chain Monte-Carlo (MCMC), Approximate Bayesian computation (ABC) or variational inference. We discuss a general approach that reframes this as a multi-task learning problem and uses recurrent deep neural networks (RNNs) to approximately evaluate posterior quantiles. As RNNs carry information along a sequence, this application is particularly useful in time-series. An advantage of this risk-minimization approach is that we do not need to sample from the posterior or calculate the likelihood. We illustrate the proposed approach in several examples.

Clinical Endpoints for Evaluating Efficacy in COVID-19 Vaccine Trials.

Several vaccine candidates to protect against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection or coronavirus disease 2019 (COVID-19) have entered or will soon enter large-scale, phase 3, placebo-controlled randomized clinical trials. To facilitate harmonized evaluation and comparison of the efficacy of these vaccines, a general set of clinical endpoints is proposed, along with considerations to guide the selection of the primary endpoints on the basis of clinical and statistical reasoning. The plausibility that vaccine protection against symptomatic COVID-19 could be accompanied by a shift toward more SARS-CoV-2 infections that are asymptomatic is highlighted, as well as the potential implications of such a shift.

A Deferred-Vaccination Design to Assess Durability of COVID-19 Vaccine Effect After the Placebo Group Is Vaccinated.

Multiple candidate vaccines to prevent COVID-19 have entered large-scale phase 3 placebo-controlled randomized clinical trials, and several have demonstrated substantial short-term efficacy. At some point after demonstration of substantial efficacy, placebo recipients should be offered the efficacious vaccine from their trial, which will occur before longer-term efficacy and safety are known. The absence of a placebo group could compromise assessment of longer-term vaccine effects. However, by continuing follow-up after vaccination of the placebo group, this study shows that placebo-controlled vaccine efficacy can be mathematically derived by assuming that the benefit of vaccination over time has the same profile for the original vaccine recipients and the original placebo recipients after their vaccination. Although this derivation provides less precise estimates than would be obtained by a standard trial where the placebo group remains unvaccinated, this proposed approach allows estimation of longer-term effect, including durability of vaccine efficacy and whether the vaccine eventually becomes harmful for some. Deferred vaccination, if done open-label, may lead to riskier behavior in the unblinded original vaccine group, confounding estimates of long-term vaccine efficacy. Hence, deferred vaccination via blinded crossover, where the vaccine group receives placebo and vice versa, would be the preferred way to assess vaccine durability and potential delayed harm. Deferred vaccination allows placebo recipients timely access to the vaccine when it would no longer be proper to maintain them on placebo, yet still allows important insights about immunologic and clinical effectiveness over time.

Nonparametric variable importance assessment using machine learning techniques.

In a regression setting, it is often of interest to quantify the importance of various features in predicting the response. Commonly, the variable importance measure used is determined by the regression technique employed. For this reason, practitioners often only resort to one of a few regression techniques for which a variable importance measure is naturally defined. Unfortunately, these regression techniques are often suboptimal for predicting the response. Additionally, because the variable importance measures native to different regression techniques generally have a different interpretation, comparisons across techniques can be difficult. In this work, we study a variable importance measure that can be used with any regression technique, and whose interpretation is agnostic to the technique used. This measure is a property of the true data-generating mechanism. Specifically, we discuss a generalization of the analysis of variance variable importance measure and discuss how it facilitates the use of machine learning techniques to flexibly estimate the variable importance of a single feature or group of features. The importance of each feature or group of features in the data can then be described individually, using this measure. We describe how to construct an efficient estimator of this measure as well as a valid confidence interval. Through simulations, we show that our proposal has good practical operating characteristics, and we illustrate its use with data from a study of risk factors for cardiovascular disease in South Africa.

Combining biomarkers by maximizing the true positive rate for a fixed false positive rate.

Biomarkers abound in many areas of clinical research, and often investigators are interested in combining them for diagnosis, prognosis, or screening. In many applications, the true positive rate (TPR) for a biomarker combination at a prespecified, clinically acceptable false positive rate (FPR) is the most relevant measure of predictive capacity. We propose a distribution-free method for constructing biomarker combinations by maximizing the TPR while constraining the FPR. Theoretical results demonstrate desirable properties of biomarker combinations produced by the new method. In simulations, the biomarker combination provided by our method demonstrated improved operating characteristics in a variety of scenarios when compared with alternative methods for constructing biomarker combinations. Thus, use of our method could lead to the development of better biomarker combinations, increasing the likelihood of clinical adoption.

Ophthalmic conditions associated with dementia risk: The Cardiovascular Health Study.

INTRODUCTION: Ophthalmic conditions and dementia appear to overlap and may share common pathways, but research has not differentiated dementia subtypes. METHODS: Diagnoses of cataracts, age-related macular degeneration (AMD), diabetic retinopathy (DR), and glaucoma were based on medical histories and International Classification of Diseases, Ninth Revision (ICD-9) codes for 3375 participants from the Cardiovascular Health Study. Dementia, including Alzheimer's disease (AD) and vascular dementia (VaD), was classified using standardized research criteria. RESULTS: Cataracts were associated with AD (hazard ratio [HR] = 1.34; 95% confidence interval [CI] = 1.01-1.80) and VaD/mixed dementia (HR = 1.41; 95% CI = 1.02-1.95). AMD was associated with AD only (HR = 1.87; 95% CI = 1.13-3.09), whereas DR was associated with VaD/mixed dementia only (HR = 2.63; 95% CI = 1.10-6.27). DISCUSSION: Differential associations between specific ophthalmic conditions and dementia subtypes may elucidate pathophysiologic pathways. Lack of association between glaucoma and dementia was most surprising from these analyses.

Universal sieve-based strategies for efficient estimation using machine learning tools.

Suppose that we wish to estimate a finite-dimensional summary of one or more function-valued features of an underlying data-generating mechanism under a nonparametric model. One approach to estimation is by plugging in flexible estimates of these features. Unfortunately, in general, such estimators may not be asymptotically efficient, which often makes these estimators difficult to use as a basis for inference. Though there are several existing methods to construct asymptotically efficient plug-in estimators, each such method either can only be derived using knowledge of efficiency theory or is only valid under stringent smoothness assumptions. Among existing methods, sieve estimators stand out as particularly convenient because efficiency theory is not required in their construction, their tuning parameters can be selected data adaptively, and they are universal in the sense that the same fits lead to efficient plug-in estimators for a rich class of estimands. Inspired by these desirable properties, we propose two novel universal approaches for estimating function-valued features that can be analyzed using sieve estimation theory. Compared to traditional sieve estimators, these approaches are valid under more general conditions on the smoothness of the function-valued features by utilizing flexible estimates that can be obtained, for example, using machine learning.

Prediction of VRC01 neutralization sensitivity by HIV-1 gp160 sequence features.

The broadly neutralizing antibody (bnAb) VRC01 is being evaluated for its efficacy to prevent HIV-1 infection in the Antibody Mediated Prevention (AMP) trials. A secondary objective of AMP utilizes sieve analysis to investigate how VRC01 prevention efficacy (PE) varies with HIV-1 envelope (Env) amino acid (AA) sequence features. An exhaustive analysis that tests how PE depends on every AA feature with sufficient variation would have low statistical power. To design an adequately powered primary sieve analysis for AMP, we modeled VRC01 neutralization as a function of Env AA sequence features of 611 HIV-1 gp160 pseudoviruses from the CATNAP database, with objectives: (1) to develop models that best predict the neutralization readouts; and (2) to rank AA features by their predictive importance with classification and regression methods. The dataset was split in half, and machine learning algorithms were applied to each half, each analyzed separately using cross-validation and hold-out validation. We selected Super Learner, a nonparametric ensemble-based cross-validated learning method, for advancement to the primary sieve analysis. This method predicted the dichotomous resistance outcome of whether the IC50 neutralization titer of VRC01 for a given Env pseudovirus is right-censored (indicating resistance) with an average validated AUC of 0.868 across the two hold-out datasets. Quantitative log IC50 was predicted with an average validated R2 of 0.355. Features predicting neutralization sensitivity or resistance included 26 surface-accessible residues in the VRC01 and CD4 binding footprints, the length of gp120, the length of Env, the number of cysteines in gp120, the number of cysteines in Env, and 4 potential N-linked glycosylation sites; the top features will be advanced to the primary sieve analysis. This modeling framework may also inform the study of VRC01 in the treatment of HIV-infected persons.

Distance-based analysis of variance for brain connectivity.

The field of neuroimaging dedicated to mapping connections in the brain is increasingly being recognized as key for understanding neurodevelopment and pathology. Networks of these connections are quantitatively represented using complex structures, including matrices, functions, and graphs, which require specialized statistical techniques for estimation and inference about developmental and disorder-related changes. Unfortunately, classical statistical testing procedures are not well suited to high-dimensional testing problems. In the context of global or regional tests for differences in neuroimaging data, traditional analysis of variance (ANOVA) is not directly applicable without first summarizing the data into univariate or low-dimensional features, a process that might mask the salient features of high-dimensional distributions. In this work, we consider a general framework for two-sample testing of complex structures by studying generalized within-group and between-group variances based on distances between complex and potentially high-dimensional observations. We derive an asymptotic approximation to the null distribution of the ANOVA test statistic, and conduct simulation studies with scalar and graph outcomes to study finite sample properties of the test. Finally, we apply our test to our motivating study of structural connectivity in autism spectrum disorder.

A UNIFIED STUDY OF NONPARAMETRIC INFERENCE FOR MONOTONE FUNCTIONS.

The problem of nonparametric inference on a monotone function has been extensively studied in many particular cases. Estimators considered have often been of so-called Grenander type, being representable as the left derivative of the greatest convex minorant or least concave majorant of an estimator of a primitive function. In this paper, we provide general conditions for consistency and pointwise convergence in distribution of a class of generalized Grenander-type estimators of a monotone function. This broad class allows the minorization or majoratization operation to be performed on a data-dependent transformation of the domain, possibly yielding benefits in practice. Additionally, we provide simpler conditions and more concrete distributional theory in the important case that the primitive estimator and data-dependent transformation function are asymptotically linear. We use our general results in the context of various well-studied problems, and show that we readily recover classical results established separately in each case. More importantly, we show that our results allow us to tackle more challenging problems involving parameters for which the use of flexible learning strategies appears necessary. In particular, we study inference on monotone density and hazard functions using informatively right-censored data, extending the classical work on independent censoring, and on a covariate-marginalized conditional mean function, extending the classical work on monotone regression functions.

Ethical Dilemmas in Radiology: Survey of Opinions and Experiences.

OBJECTIVE. The aim of this study was to assess trainees' and practicing radiologists' perceptions and experiences in handling ethical situations. We sought to identify frequently encountered ethical dilemmas and how they are addressed in daily practice. MATERIALS AND METHODS. A questionnaire on ethics was sent by email invitation to 1569 radiologists and radiology trainees in an institutional database maintained for continuing medical education purposes on three separate occasions between September 17, 2016, and October 31, 2016. The link to the survey was also posted on social media sites via the authors' and institutional accounts on Facebook, Twitter, Instagram, and Aunt Minnie as well as on American College of Radiology and Radiological Society of North America web blogs. RESULTS. A total of 424 radiologists and trainees responded to the survey, for a response rate of 27% (424/1569). Of them, 363 responded to a question asking whether they had witnessed an ethical dilemma; 203 (56%) had. The wording of reports when a miss was discovered was not handled in a consistent fashion. Regarding disclosure, trainees were more likely than practicing radiologists to report theirs and others' errors to the patient. Of the 362 respondents who responded to a question about whether they would report a negligent act by a colleague to the group director, 292 (81%) stated that they would, but trainees were less likely than practicing radiologists to do so. CONCLUSION. This study found many common ethical dilemmas in radiology practices remain without an appropriate, objective, and unified approach to effectively guide the radiologist's actions. These results highlight a need to provide more uniform recommendations to assist radiologists in addressing ethical issues in an appropriate manner.

Global sensitivity analysis for repeated measures studies with informative drop-out: A semi-parametric approach.

In practice, both testable and untestable assumptions are generally required to draw inference about the mean outcome measured at the final scheduled visit in a repeated measures study with drop-out. Scharfstein et al. (2014) proposed a sensitivity analysis methodology to determine the robustness of conclusions within a class of untestable assumptions. In their approach, the untestable and testable assumptions were guaranteed to be compatible; their testable assumptions were based on a fully parametric model for the distribution of the observable data. While convenient, these parametric assumptions have proven especially restrictive in empirical research. Here, we relax their distributional assumptions and provide a more flexible, semi-parametric approach. We illustrate our proposal in the context of a randomized trial for evaluating a treatment of schizoaffective disorder.

Improved estimation of the cumulative incidence of rare outcomes.

Studying the incidence of rare events is both scientifically important and statistically challenging. When few events are observed, standard survival analysis estimators behave erratically, particularly if covariate adjustment is necessary. In these settings, it is possible to improve upon existing estimators by considering estimation in a bounded statistical model. This bounded model incorporates existing scientific knowledge about the incidence of an event in the population. Estimators that are guaranteed to agree with existing scientific knowledge on event incidence may exhibit superior behavior relative to estimators that ignore this knowledge. Focusing on the setting of competing risks, we propose estimators of cumulative incidence that are guaranteed to respect a bounded model and show that when few events are observed, the proposed estimators offer improvements over existing estimators in bias and variance. We illustrate the proposed estimators using data from a recent preventive HIV vaccine efficacy trial. Copyright © 2017 John Wiley & Sons, Ltd.