OBJECTIVE

To determine the risk of cardiovascular disease (CVD), microvascular complications, and mortality in patients with type 2 diabetes who subsequently develop obstructive sleep apnea (OSA) compared with patients with type 2 diabetes without a diagnosis of OSA.

This Transatlantic Council on Migration Statement assesses the continuum of policies needed to disrupt illegal migration-related activities and addresses the conditions that make them possible. It examines the role of migration "bad actors"—human traffickers and unscrupulous employers, among them—who operate and profit in this environment, and considers how governments can deploy resources to discourage their actions.

If you were Barack Obama, you would be scratching your head, too.

OBJECTIVE

The Diabetes Control and Complications Trial (DCCT) and its observational follow-up Epidemiology of Diabetes Interventions and Complications (EDIC) demonstrated the dominant role of glycemia, second only to age, as a risk factor for a first cardiovascular event in type 1 diabetes (T1D). We now investigate the association between established risk factors and the total cardiovascular disease (CVD) burden, including subsequent (i.e., recurrent) events.

Humphries, Barry, 1934-

Rockville, Maryland : National Institute on Drug Abuse, 1988.

In many applications, observed data are influenced by some combination of latent causes. For example, suppose sensors are placed inside a building to record responses such as temperature, humidity, power consumption and noise levels. These random, observed responses are typically affected by many unobserved, latent factors (or features) within the building such as the number of individuals, the turning on and off of electrical devices, power surges, etc. These latent factors are usually present for a contiguous period of time before disappearing; further, multiple factors could be present at a time. This paper develops new probabilistic methodology and inference methods for random object generation influenced by latent features exhibiting temporal persistence. Every datum is associated with subsets of a potentially infinite number of hidden, persistent features that account for temporal dynamics in an observation. The ensuing class of dynamic models constructed by adapting the Indian Buffet Process — a probability measure on the space of random, unbounded binary matrices — finds use in a variety of applications arising in operations, signal processing, biomedicine, marketing, image analysis, etc. Illustrations using synthetic and real data are provided.

Factor analysis is a flexible technique for assessment of multivariate dependence and codependence. Besides being an exploratory tool used to reduce the dimensionality of multivariate data, it allows estimation of common factors that often have an interesting theoretical interpretation in real problems. However, standard factor analysis is only applicable when the variables are scaled, which is often inappropriate, for example, in data obtained from questionnaires in the field of psychology,where the variables are often categorical. In this framework, we propose a factor model for the analysis of multivariate ordered and non-ordered polychotomous data. The inference procedure is done under the Bayesian approach via Markov chain Monte Carlo methods. Two Monte-Carlo simulation studies are presented to investigate the performance of this approach in terms of estimation bias, precision and assessment of the number of factors. We also illustrate the proposed method to analyze participants' responses to the Motivational State Questionnaire dataset, developed to study emotions in laboratory and field settings.

We analyze risk factors correlated with the initial transmission growth rate of the COVID-19 pandemic. The number of cases follows an early exponential expansion; we chose as a starting point in each country the first day with 30 cases and used 12 days. We looked for linear correlations of the exponents with other variables, using 126 countries. We find a positive correlation with high C.L. with the following variables, with respective $p$-value: low Temperature ($4cdot10^{-7}$), high ratio of old vs.~working-age people ($3cdot10^{-6}$), life expectancy ($8cdot10^{-6}$), number of international tourists ($1cdot10^{-5}$), earlier epidemic starting date ($2cdot10^{-5}$), high level of contact in greeting habits ($6 cdot 10^{-5}$), lung cancer ($6 cdot 10^{-5}$), obesity in males ($1 cdot 10^{-4}$), urbanization ($2cdot10^{-4}$), cancer prevalence ($3 cdot 10^{-4}$), alcohol consumption ($0.0019$), daily smoking prevalence ($0.0036$), UV index ($0.004$, smaller sample, 73 countries), low Vitamin D levels ($p$-value $0.002-0.006$, smaller sample, $sim 50$ countries). There is highly significant correlation also with blood type: positive correlation with RH- ($2cdot10^{-5}$) and A+ ($2cdot10^{-3}$), negative correlation with B+ ($2cdot10^{-4}$). We also find positive correlation with moderate C.L. ($p$-value of $0.02sim0.03$) with: CO$_2$ emissions, type-1 diabetes, low vaccination coverage for Tuberculosis (BCG). Several such variables are correlated with each other and so they likely have common interpretations. We also analyzed the possible existence of a bias: countries with low GDP-per capita, typically located in warm regions, might have less intense testing and we discuss correlation with the above variables.

As an important type of reinforcement learning algorithms, actor-critic (AC) and natural actor-critic (NAC) algorithms are often executed in two ways for finding optimal policies. In the first nested-loop design, actor's one update of policy is followed by an entire loop of critic's updates of the value function, and the finite-sample analysis of such AC and NAC algorithms have been recently well established. The second two time-scale design, in which actor and critic update simultaneously but with different learning rates, has much fewer tuning parameters than the nested-loop design and is hence substantially easier to implement. Although two time-scale AC and NAC have been shown to converge in the literature, the finite-sample convergence rate has not been established. In this paper, we provide the first such non-asymptotic convergence rate for two time-scale AC and NAC under Markovian sampling and with actor having general policy class approximation. We show that two time-scale AC requires the overall sample complexity at the order of $mathcal{O}(epsilon^{-2.5}log^3(epsilon^{-1}))$ to attain an $epsilon$-accurate stationary point, and two time-scale NAC requires the overall sample complexity at the order of $mathcal{O}(epsilon^{-4}log^2(epsilon^{-1}))$ to attain an $epsilon$-accurate global optimal point. We develop novel techniques for bounding the bias error of the actor due to dynamically changing Markovian sampling and for analyzing the convergence rate of the linear critic with dynamically changing base functions and transition kernel.

Hierarchical abstraction and curiosity-driven exploration are two common paradigms in current reinforcement learning approaches to break down difficult problems into a sequence of simpler ones and to overcome reward sparsity. However, there is a lack of approaches that combine these paradigms, and it is currently unknown whether curiosity also helps to perform the hierarchical abstraction. As a novelty and scientific contribution, we tackle this issue and develop a method that combines hierarchical reinforcement learning with curiosity. Herein, we extend a contemporary hierarchical actor-critic approach with a forward model to develop a hierarchical notion of curiosity. We demonstrate in several continuous-space environments that curiosity approximately doubles the learning performance and success rates for most of the investigated benchmarking problems.

Subhadeep Paul, Yuguo Chen.

Source: The Annals of Statistics, Volume 48, Number 1, 230--250.

Abstract:
We consider the problem of estimating a consensus community structure by combining information from multiple layers of a multi-layer network using methods based on the spectral clustering or a low-rank matrix factorization. As a general theme, these “intermediate fusion” methods involve obtaining a low column rank matrix by optimizing an objective function and then using the columns of the matrix for clustering. However, the theoretical properties of these methods remain largely unexplored. In the absence of statistical guarantees on the objective functions, it is difficult to determine if the algorithms optimizing the objectives will return good community structures. We investigate the consistency properties of the global optimizer of some of these objective functions under the multi-layer stochastic blockmodel. For this purpose, we derive several new asymptotic results showing consistency of the intermediate fusion techniques along with the spectral clustering of mean adjacency matrix under a high dimensional setup, where the number of nodes, the number of layers and the number of communities of the multi-layer graph grow. Our numerical study shows that the intermediate fusion techniques outperform late fusion methods, namely spectral clustering on aggregate spectral kernel and module allegiance matrix in sparse networks, while they outperform the spectral clustering of mean adjacency matrix in multi-layer networks that contain layers with both homophilic and heterophilic communities.

Xinran Li, Peng Ding, Donald B. Rubin.

Source: The Annals of Statistics, Volume 48, Number 1, 43--63.

Abstract:
With many pretreatment covariates and treatment factors, the classical factorial experiment often fails to balance covariates across multiple factorial effects simultaneously. Therefore, it is intuitive to restrict the randomization of the treatment factors to satisfy certain covariate balance criteria, possibly conforming to the tiers of factorial effects and covariates based on their relative importances. This is rerandomization in factorial experiments. We study the asymptotic properties of this experimental design under the randomization inference framework without imposing any distributional or modeling assumptions of the covariates and outcomes. We derive the joint asymptotic sampling distribution of the usual estimators of the factorial effects, and show that it is symmetric, unimodal and more “concentrated” at the true factorial effects under rerandomization than under the classical factorial experiment. We quantify this advantage of rerandomization using the notions of “central convex unimodality” and “peakedness” of the joint asymptotic sampling distribution. We also construct conservative large-sample confidence sets for the factorial effects.

Kyoungjae Lee, Jaeyong Lee, Lizhen Lin.

Source: The Annals of Statistics, Volume 47, Number 6, 3413--3437.

Abstract:
In this paper we study the high-dimensional sparse directed acyclic graph (DAG) models under the empirical sparse Cholesky prior. Among our results, strong model selection consistency or graph selection consistency is obtained under more general conditions than those in the existing literature. Compared to Cao, Khare and Ghosh [ Ann. Statist. (2019) 47 319–348], the required conditions are weakened in terms of the dimensionality, sparsity and lower bound of the nonzero elements in the Cholesky factor. Furthermore, our result does not require the irrepresentable condition, which is necessary for Lasso-type methods. We also derive the posterior convergence rates for precision matrices and Cholesky factors with respect to various matrix norms. The obtained posterior convergence rates are the fastest among those of the existing Bayesian approaches. In particular, we prove that our posterior convergence rates for Cholesky factors are the minimax or at least nearly minimax depending on the relative size of true sparseness for the entire dimension. The simulation study confirms that the proposed method outperforms the competing methods.

Tsuyoshi Kunihama, Zehang Richard Li, Samuel J. Clark, Tyler H. McCormick.

Source: The Annals of Applied Statistics, Volume 14, Number 1, 241--256.

Abstract:
The distribution of deaths by cause provides crucial information for public health planning, response and evaluation. About 60% of deaths globally are not registered or given a cause, limiting our ability to understand disease epidemiology. Verbal autopsy (VA) surveys are increasingly used in such settings to collect information on the signs, symptoms and medical history of people who have recently died. This article develops a novel Bayesian method for estimation of population distributions of deaths by cause using verbal autopsy data. The proposed approach is based on a multivariate probit model where associations among items in questionnaires are flexibly induced by latent factors. Using the Population Health Metrics Research Consortium labeled data that include both VA and medically certified causes of death, we assess performance of the proposed method. Further, we estimate important questionnaire items that are highly associated with causes of death. This framework provides insights that will simplify future data

Elizabeth Lorenzi, Ricardo Henao, Katherine Heller.

Source: The Annals of Applied Statistics, Volume 13, Number 4, 2637--2661.

Abstract:
Nearly a third of all surgeries performed in the United States occur for patients over the age of 65; these older adults experience a higher rate of postoperative morbidity and mortality. To improve the care for these patients, we aim to identify and characterize high risk geriatric patients to send to a specialized perioperative clinic while leveraging the overall surgical population to improve learning. To this end, we develop a hierarchical infinite latent factor model (HIFM) to appropriately account for the covariance structure across subpopulations in data. We propose a novel Hierarchical Dirichlet Process shrinkage prior on the loadings matrix that flexibly captures the underlying structure of our data while sharing information across subpopulations to improve inference and prediction. The stick-breaking construction of the prior assumes an infinite number of factors and allows for each subpopulation to utilize different subsets of the factor space and select the number of factors needed to best explain the variation. We develop the model into a latent factor regression method that excels at prediction and inference of regression coefficients. Simulations validate this strong performance compared to baseline methods. We apply this work to the problem of predicting surgical complications using electronic health record data for geriatric patients and all surgical patients at Duke University Health System (DUHS). The motivating application demonstrates the improved predictive performance when using HIFM in both area under the ROC curve and area under the PR Curve while providing interpretable coefficients that may lead to actionable interventions.

Susheela P. Singh, Ana-Maria Staicu, Robert R. Dunn, Noah Fierer, Brian J. Reich.

Source: The Annals of Applied Statistics, Volume 13, Number 4, 2341--2362.

Abstract:
The advent of high-throughput sequencing technologies has made data from DNA material readily available, leading to a surge of microbiome-related research establishing links between markers of microbiome health and specific outcomes. However, to harness the power of microbial communities we must understand not only how they affect us, but also how they can be influenced to improve outcomes. This area has been dominated by methods that reduce community composition to summary metrics, which can fail to fully exploit the complexity of community data. Recently, methods have been developed to model the abundance of taxa in a community, but they can be computationally intensive and do not account for spatial effects underlying microbial settlement. These spatial effects are particularly relevant in the microbiome setting because we expect communities that are close together to be more similar than those that are far apart. In this paper, we propose a flexible Bayesian spike-and-slab variable selection model for presence-absence indicators that accounts for spatial dependence and cross-dependence between taxa while reducing dimensionality in both directions. We show by simulation that in the presence of spatial dependence, popular distance-based hypothesis testing methods fail to preserve their advertised size, and the proposed method improves variable selection. Finally, we present an application of our method to an indoor fungal community found within homes across the contiguous United States.

Muteb Alharthi, Theodore Kypraios, Philip D. O’Neill.

Source: Bayesian Analysis, Volume 14, Number 3, 927--956.

Abstract:
We consider the problem of model choice for stochastic epidemic models given partial observation of a disease outbreak through time. Our main focus is on the use of Bayes factors. Although Bayes factors have appeared in the epidemic modelling literature before, they can be hard to compute and little attention has been given to fundamental questions concerning their utility. In this paper we derive analytic expressions for Bayes factors given complete observation through time, which suggest practical guidelines for model choice problems. We adapt the power posterior method for computing Bayes factors so as to account for missing data and apply this approach to partially observed epidemics. For comparison, we also explore the use of a deviance information criterion for missing data scenarios. The methods are illustrated via examples involving both simulated and real data.

Quan Zhou, Yongtao Guan.

Source: Bayesian Analysis, Volume 14, Number 2, 573--594.

Abstract:
Bayesian variable selection regression (BVSR) is able to jointly analyze genome-wide genetic datasets, but the slow computation via Markov chain Monte Carlo (MCMC) hampered its wide-spread usage. Here we present a novel iterative method to solve a special class of linear systems, which can increase the speed of the BVSR model-fitting tenfold. The iterative method hinges on the complex factorization of the sum of two matrices and the solution path resides in the complex domain (instead of the real domain). Compared to the Gauss-Seidel method, the complex factorization converges almost instantaneously and its error is several magnitude smaller than that of the Gauss-Seidel method. More importantly, the error is always within the pre-specified precision while the Gauss-Seidel method is not. For large problems with thousands of covariates, the complex factorization is 10–100 times faster than either the Gauss-Seidel method or the direct method via the Cholesky decomposition. In BVSR, one needs to repetitively solve large penalized regression systems whose design matrices only change slightly between adjacent MCMC steps. This slight change in design matrix enables the adaptation of the iterative complex factorization method. The computational innovation will facilitate the wide-spread use of BVSR in reanalyzing genome-wide association datasets.

Joris Mulder, Jean-Paul Fox.

Source: Bayesian Analysis, Volume 14, Number 2, 521--552.

Abstract:
The intraclass correlation plays a central role in modeling hierarchically structured data, such as educational data, panel data, or group-randomized trial data. It represents relevant information concerning the between-group and within-group variation. Methods for Bayesian hypothesis tests concerning the intraclass correlation are proposed to improve decision making in hierarchical data analysis and to assess the grouping effect across different group categories. Estimation and testing methods for the intraclass correlation coefficient are proposed under a marginal modeling framework where the random effects are integrated out. A class of stretched beta priors is proposed on the intraclass correlations, which is equivalent to shifted $F$ priors for the between groups variances. Through a parameter expansion it is shown that this prior is conditionally conjugate under the marginal model yielding efficient posterior computation. A special improper case results in accurate coverage rates of the credible intervals even for minimal sample size and when the true intraclass correlation equals zero. Bayes factor tests are proposed for testing multiple precise and order hypotheses on intraclass correlations. These tests can be used when prior information about the intraclass correlations is available or absent. For the noninformative case, a generalized fractional Bayes approach is developed. The method enables testing the presence and strength of grouped data structures without introducing random effects. The methodology is applied to a large-scale survey study on international mathematics achievement at fourth grade to test the heterogeneity in the clustering of students in schools across countries and assessment cycles.

Steven Poser
Jun 1, 2003; 23:4420-4427
Cellular

The digital revolution has changed the way we work, access information and connect with each other. It offers opportunities to those who can use the new technologies, but also presents new challenges for those who are left behind. Often referred to collectively as Information and Communications Technologies or ICTs, these technologies are any method of electronically sharing or storing data: telephones, [...]

The 18th-century Edinburgh factory once produced a million bottles a week

In a written decision released Thursday, a panel of three appeal court judges said the judge who granted the award to McCaw North Drilling and Blasting Ltd. misinterpreted a clause in the contract for the cleanup.

Central Bank of Chile Working Papers by Andrés Sagner

BACKGROUND AND OBJECTIVES:

Children with autism spectrum disorder (ASD) have a higher prevalence of epilepsy compared with general populations. In this pilot study, we prospectively identified baseline risk factors for the development of seizures in individuals with ASD and also identified characteristics sensitive to seizure onset up to 6 years after enrollment in the Autism Speaks Autism Treatment Network.

Actor and Celebrity MasterChef winner John Partridge shares his culinary journey of recovery and grief with Jenny Stallard.

The recent Chicago Teachers Union strike drew attention from Democratic presidential candidates in Illinois, a state won by Democrats in the last White House contest. For 2020, it's possible we could see a twist on that story: big-city teacher strikes in states with less predictable outcomes.

Hamish Wilson, radio producer and actor

Born: July 9, 1938;

The swirl of attention around Democratic presidential candidate Elizabeth Warren’s story of being forced out of a teaching job when she was pregnant intensifies the spotlight on her background and K-12 credentials.

The recent Chicago Teachers Union strike drew attention from Democratic presidential candidates in Illinois, a state won by Democrats in the last White House contest. For 2020, it's possible we could see a twist on that story: big-city teacher strikes in states with less predictable outcomes.

Cardiovascular risk factors predict the development of premature atherosclerosis. As the number of risk factors increases, so does the extent of these lesions. Assessment of cardiovascular risk factors is an accepted practice in adults but is not used in pediatrics.

In this study, the authors discuss how the presence of ≥2 cardiovascular risk factors is associated with vascular changes in adolescents. The findings were compared with the Patholobiological Determinants of Atherosclerosis in Youth risk score to demonstrate that a simple method of clustering is a reliable tool to use in clinical practice. (Read the full article)

The efficacy of intravenous immunoglobulin and corticosteroid pulse combination therapy for refractory Kawasaki disease has been established. The Egami score can be used to predict which patients are likely to have refractory Kawasaki disease.

As a new strategy for primary treatment, intravenous immunoglobulin and corticosteroid pulse combination therapy is safe and effective for patients predicted to have refractory Kawasaki disease based on the Egami score. (Read the full article)

Gallbladder disease in children is an evolving entity and studies suggest an increasing frequency of symptomatic pediatric gallbladder disease and resultant cholecystectomies.

Hispanic ethnicity and obesity are epidemiologically significant risk factors for symptomatic gallbladder disease in the pediatric population. (Read the full article)

Prone sleep, bed-sharing, maternal smoking during pregnancy, and prematurity increase the risk of sudden infant death syndrome. The sudden infant death syndrome rate initially declined dramatically after the initiation of the US Back-to-Sleep campaign in 1994, but subsequently plateaued.

The risk profile has changed since the Back-to-Sleep campaign; the prevalence of simultaneous risks has remained consistent. Intrinsic and extrinsic risks provide unification into 1 underlying triple-risk model and insights into potential underlying mechanisms. (Read the full article)

Rural Alaska children have high rates of hospitalization with lower respiratory tract infections from a variety of pathogens. Past studies of risk factors for respiratory syncytial virus infection associated medically high-risk status, household crowding, and infant feeding practices with hospitalization.

This study reveals the importance of medically high-risk status and infant feeding practices as important factors in respiratory hospitalization. In addition, we identified woodstove use and the absence of 2 or more sinks in household as risk factors for hospitalization. (Read the full article)

Valid parental permission requires that the decision be both informed and voluntary. Previous research has focused on the informational components of decision-making (eg, disclosure and understanding), with little empirical attention to the voluntariness of decisions.

We address this gap by examining the voluntariness of parents making research or treatment decisions in pediatric oncology. We identify demographic and contextual correlates of voluntariness and highlight the clinical implications of the findings for physicians and investigators. (Read the full article)

Overweight and obese children have a higher prevalence of several cardiovascular disease (CVD) risk factors. There is growing evidence demonstrating that CVD risk factors present during childhood persist into adulthood.

US adolescents had no significant change in prehypertension/hypertension and borderline-high/ high low-density lipoprotein cholesterol prevalence from 1999–2000 to 2007–2008; however, prediabetes/diabetes increased by 14%. (Read the full article)

There is a paucity of controlled studies of recombinant factor VIIa (rFVIIa) use for off-label indications in pediatric patients. Data on the use of off-label rFVIIa, including safety and efficacy, are mostly limited to case reports or small case series.

This is the largest reported case series of off-label rFVIIa in pediatric patients from a well-designed, representative, and rigorously audited registry of rFVIIa use and describes the indications for use, dose administered, adverse events, and outcomes in 388 patients. (Read the full article)

Male circumcision reduces risk of HIV acquisition in men by 60% and is associated with other health benefits. Compared with adult circumcision, infant male circumcision is safer, less expensive, and represents a cost-saving intervention for HIV prevention in many settings.

IMC is little known in East Africa and is not routinely practiced. This is the first study to assess acceptability and uptake of IMC in East Africa among parents who were actually offered the procedure. (Read the full article)

Celiac disease is an immunologic disorder with autoimmune features. Sweden experienced an epidemic of celiac disease in infants (1984–1996). Early vaccinations might influence the risk for autoimmune diseases, and could potentially have contributed to celiac disease risk and the epidemic.

Early vaccinations within the national Swedish program are not risk factors for celiac disease, nor do changes over time contribute to explaining the Swedish epidemic. A protective effect by vaccination against tuberculosis (bacillus Calmette-Guérin) is suggested. (Read the full article)

Because most childhood tuberculosis cases are sputum smear-negative, diagnosis relies largely upon clinical presentation, tuberculin skin testing, and chest radiograph. Diagnostic limitations contribute to treatment delays and high mortality. However, childhood tuberculosis (TB) mortality risk factors are not well documented.

This study demonstrates that false-negative TST is common in children with active TB and is associated with increased risk of death. A negative TST should not delay anti-TB therapy. Improved diagnostic modalities are urgently needed in resource-limited settings. (Read the full article)

Understanding human disease genomics requires large population-based studies. There is lack of standardization, as well as social and ethical concerns surrounding the consent process for pediatric participation in a biorepository.

The study identifies specific barriers to pediatric participation in biorepositories relative to adults, and proposes strategies to improve ethical and responsible participation of pediatric-aged patients in large-scale genomics and biorepository-driven research without significantly increasing research burden for affected families. (Read the full article)

Prenatal cocaine exposure is associated with the trajectories of childhood behavior problems. Exposure effects may also be related to maternal use of other substances during pregnancy, and risk factors other than prenatal exposure may augment the detrimental cocaine effects.

The balance between cumulative risk and protective indexes predicts behavior outcomes, independent of prenatal drug exposure. A high protective index even with a high level of risks can mitigate the detrimental effects of drug exposure on behavior problem trajectory. (Read the full article)

National asthma treatment guidelines include the recommendation that all asthma patients receive a written asthma action plan. No previous study has sought to examine the readability, suitability, and content of asthma action plans within a nationally representative sample.

Although variability was found across written asthma action plans, and improvements in readability, suitability, and content are needed, there were also many common elements that would support a move to a single universal standard action plan. (Read the full article)

A reduced nephron number is associated with glomerular hyperfiltration, resulting in renal injury such as hypertension, proteinuria, and chronic kidney disease. Patients with a solitary functioning kidney have an increased risk of dialysis in early adulthood.

This study demonstrates that a subset of children with a solitary functioning kidney progress toward renal injury during childhood. Risk factors for renal injury are ipsilateral anomalies of the kidney and urinary tract and small renal length. (Read the full article)