OBJECTIVE

To study whether the effects of intensive glycemic control on major vascular outcomes (a composite of major macrovascular and major microvascular events), all-cause mortality, and severe hypoglycemia events differ among participants with different levels of 10-year risk of atherosclerotic cardiovascular disease (ASCVD) and hemoglobin A_1c (HbA_1c) at baseline.

OBJECTIVE

To identify sleep duration trajectories from early to middle adulthood and their associations with incident type 2 diabetes.

OBJECTIVE

The vast number of antihyperglycemic medications and growing amount of evidence make clinical decision making difficult. The aim of this study was to investigate the safety of antihyperglycemic dual and triple therapies for type 2 diabetes management with respect to major adverse cardiovascular events, severe hypoglycemia, and all-cause mortality in a real-life clinical setting.

OBJECTIVE

Although blood pressure variability is increasingly appreciated as a risk factor for cardiovascular disease, its relationship with heart failure (HF) is less clear. We examined the relationship between blood pressure variability and risk of HF in two cohorts of type 2 diabetes participating in trials of glucose and/or other risk factor management.

OBJECTIVE

To assess the association between use of glucagon-like peptide 1 (GLP-1) receptor agonists and risk of serious renal events in routine clinical practice.

OBJECTIVE

Recent studies have highlighted the significance of the microbiome in human health and disease. Changes in the metabolites produced by microbiota have been implicated in several diseases. Our objective was to identify microbiome metabolites that are associated with type 2 diabetes.

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.

OBJECTIVE

This study evaluated the ability of the Building, Relating, Assessing, and Validating Outcomes (BRAVO) risk engine to accurately project cardiovascular outcomes in three major clinical trials—BI 10773 (Empagliflozin) Cardiovascular Outcome Event Trial in Type 2 Diabetes Mellitus Patients (EMPA-REG OUTCOME), Canagliflozin Cardiovascular Assessment Study (CANVAS), and Dapagliflozin Effect on Cardiovascular Events–Thrombolysis in Myocardial Infarction (DECLARE-TIMI 58) trial—on sodium–glucose cotransporter 2 inhibitors (SGLT2is) to treat patients with type 2 diabetes.

OBJECTIVE

We analyzed data from inpatients with diabetes admitted to a large university hospital to predict the risk of hypoglycemia through the use of machine learning algorithms.

OBJECTIVE

The LEADER trial (ClinicalTrials.gov reg. no. NCT01179048) demonstrated a reduced risk of cardiovascular (CV) events for patients with type 2 diabetes who received the glucagon-like peptide 1 receptor agonist liraglutide versus placebo. The mechanisms behind this CV benefit remain unclear. We aimed to identify potential mediators for the CV benefit observed with liraglutide in the LEADER trial.

OBJECTIVE

Despite evidence of a relationship among obstructive sleep apnea (OSA), metabolic dysregulation, and diabetes, it is uncertain whether OSA treatment can improve metabolic parameters. We sought to determine effects of long-term continuous positive airway pressure (CPAP) treatment on glycemic control and diabetes risk in patients with cardiovascular disease (CVD) and OSA.

I was recently asked if I thought the pandemic is a national trauma. The answer is a simple “Yes.” By the standards of the DSM-5 (Diagnostic and Statistical Manual), the...

Some nominally high-risk individuals may have a lower chance of developing dementia than once thought

-- Read more on ScientificAmerican.com

One conversation could keep your kids safe

-- Read more on ScientificAmerican.com

Shortly after the Sept. 11, 2001, attacks, psychologist Jennifer Lerner conducted a national field experiment: She asked a random sampling of Americans how likely it was that they would be the victim of a terrorist attack in the next 12 months.

The housing market is in free fall: Quick -- let's protect homeowners against foreclosure.

Seventeen years ago, a couple of criminologists at the University of Maryland published an interesting paper about the 1976 District ban on handguns -- a ban that was recently overturned by the Supreme Court on the grounds it was inimical to the constitutional right of Americans to bear arms to p...

OBJECTIVE

Plasma protein N-glycan profiling integrates information on enzymatic protein glycosylation, which is a highly controlled ubiquitous posttranslational modification. Here we investigate the ability of the plasma N-glycome to predict incidence of type 2 diabetes and cardiovascular diseases (CVDs; i.e., myocardial infarction and stroke).

OBJECTIVE

This study aimed to reveal the associations between the risk of new-onset type 2 diabetes and the duration of antidepressant use and the antidepressant dose, and between antidepressant use after diabetes onset and clinical outcomes.

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.

OBJECTIVE

To construct and internally validate prediction models to estimate the risk of long-term end-organ complications and mortality in patients with type 2 diabetes and obesity that can be used to inform treatment decisions for patients and practitioners who are considering metabolic surgery.

Somali and Bhutanese refugees are two of the largest groups recently resettled in the United States and Canada. This report examines factors that might promote or undermine the mental health and overall well-being of children of these refugees, with regard to factors such as past exposure to trauma, parental mental health, educational attainment, social support, and discrimination.

Wilfred Fujimoto
Apr 1, 2013; 31:90-94
Diabetes Advocacy

Sexually transmitted infections (STIs) are on the rise, and many people are curious about the reasons why. While the cause is obviously multifactorial, some have suggested that at least part of the rise in STIs may be due to increasing rates of pubic hair grooming in men and women alike. Given that it’s not uncommon for people to experience cuts and skin irritation from genital grooming practices, it at least sounds plausible in theory that pubic hair shaving could potentially increase infection risk. But what does the research actually say?

ABSTRACT SUBMISSION DEADLINE JANUARY 15, 2020 The Foundations of Utility and Risk (FUR) Conference will, for the first time, take place in the Asia Pacific area in 2020. We invite all Economists and other Social Scientists interested in the study of Decision-making to submit papers to the conference. Since 1982, FUR gathers every two years […]

The post Foundations of Utility and Risk (FUR) Conference, Sydney, July 1-4, 2020 appeared first on Decision Science News.

Officials have not done enough to prevent fraud in Head Start programs, the GAO said. The findings prompted Rep. Virginia Foxx, R-N.C., the ranking member of the House education and labor committee, to call for a hearing on the federally funded preschool program for low-income children.

As the national conversation on reopening schools accelerates, experts say the best way to protect vulnerable teachers might be to not have them in school buildings at all.

U.S. Secretary of Education Betsy DeVos is not famous for pressuring states into desired outcomes, but did put at least two states' Title I funding on "high-risk" status last year.

As the national conversation on reopening schools accelerates, experts say the best way to protect vulnerable teachers might be to not have them in school buildings at all.

Lucas Pereira Lopes, Vicente Garibay Cancho, Francisco Louzada.

Source: Brazilian Journal of Probability and Statistics, Volume 33, Number 4, 801--825.

Abstract:
Multivariate options are adequate tools for multi-asset risk management. The pricing models derived from the pioneer Black and Scholes method under the multivariate case consider that the asset-object prices follow a Brownian geometric motion. However, the construction of such methods imposes some unrealistic constraints on the process of fair option calculation, such as constant volatility over the maturity time and linear correlation between the assets. Therefore, this paper aims to price and analyze the fair price behavior of the call-on-max (bivariate) option considering marginal heteroscedastic models with dependence structure modeled via copulas. Concerning inference, we adopt a Bayesian perspective and computationally intensive methods based on Monte Carlo simulations via Markov Chain (MCMC). A simulation study examines the bias, and the root mean squared errors of the posterior means for the parameters. Real stocks prices of Brazilian banks illustrate the approach. For the proposed method is verified the effects of strike and dependence structure on the fair price of the option. The results show that the prices obtained by our heteroscedastic model approach and copulas differ substantially from the prices obtained by the model derived from Black and Scholes. Empirical results are presented to argue the advantages of our strategy.

In recent years, multi-access edge computing (MEC) is a key enabler for handling the massive expansion of Internet of Things (IoT) applications and services. However, energy consumption of a MEC network depends on volatile tasks that induces risk for energy demand estimations. As an energy supplier, a microgrid can facilitate seamless energy supply. However, the risk associated with energy supply is also increased due to unpredictable energy generation from renewable and non-renewable sources. Especially, the risk of energy shortfall is involved with uncertainties in both energy consumption and generation. In this paper, we study a risk-aware energy scheduling problem for a microgrid-powered MEC network. First, we formulate an optimization problem considering the conditional value-at-risk (CVaR) measurement for both energy consumption and generation, where the objective is to minimize the loss of energy shortfall of the MEC networks and we show this problem is an NP-hard problem. Second, we analyze our formulated problem using a multi-agent stochastic game that ensures the joint policy Nash equilibrium, and show the convergence of the proposed model. Third, we derive the solution by applying a multi-agent deep reinforcement learning (MADRL)-based asynchronous advantage actor-critic (A3C) algorithm with shared neural networks. This method mitigates the curse of dimensionality of the state space and chooses the best policy among the agents for the proposed problem. Finally, the experimental results establish a significant performance gain by considering CVaR for high accuracy energy scheduling of the proposed model than both the single and random agent models.

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.

Adityanand Guntuboyina, Donovan Lieu, Sabyasachi Chatterjee, Bodhisattva Sen.

Source: The Annals of Statistics, Volume 48, Number 1, 205--229.

Abstract:
We study trend filtering, a relatively recent method for univariate nonparametric regression. For a given integer $rgeq1$, the $r$th order trend filtering estimator is defined as the minimizer of the sum of squared errors when we constrain (or penalize) the sum of the absolute $r$th order discrete derivatives of the fitted function at the design points. For $r=1$, the estimator reduces to total variation regularization which has received much attention in the statistics and image processing literature. In this paper, we study the performance of the trend filtering estimator for every $rgeq1$, both in the constrained and penalized forms. Our main results show that in the strong sparsity setting when the underlying function is a (discrete) spline with few “knots,” the risk (under the global squared error loss) of the trend filtering estimator (with an appropriate choice of the tuning parameter) achieves the parametric $n^{-1}$-rate, up to a logarithmic (multiplicative) factor. Our results therefore provide support for the use of trend filtering, for every $rgeq1$, in the strong sparsity setting.

Wenjie Wang, Robert Aseltine, Kun Chen, Jun Yan.

Source: The Annals of Applied Statistics, Volume 14, Number 1, 51--73.

Abstract:
The concept of integrating data from disparate sources to accelerate scientific discovery has generated tremendous excitement in many fields. The potential benefits from data integration, however, may be compromised by the uncertainty due to incomplete/imperfect record linkage. Motivated by a suicide risk study, we propose an approach for analyzing survival data with uncertain event times arising from data integration. Specifically, in our problem deaths identified from the hospital discharge records together with reported suicidal deaths determined by the Office of Medical Examiner may still not include all the death events of patients, and the missing deaths can be recovered from a complete database of death records. Since the hospital discharge data can only be linked to the death record data by matching basic patient characteristics, a patient with a censored death time from the first dataset could be linked to multiple potential event records in the second dataset. We develop an integrative Cox proportional hazards regression in which the uncertainty in the matched event times is modeled probabilistically. The estimation procedure combines the ideas of profile likelihood and the expectation conditional maximization algorithm (ECM). Simulation studies demonstrate that under realistic settings of imperfect data linkage the proposed method outperforms several competing approaches including multiple imputation. A marginal screening analysis using the proposed integrative Cox model is performed to identify risk factors associated with death following suicide-related hospitalization in Connecticut. The identified diagnostics codes are consistent with existing literature and provide several new insights on suicide risk, prediction and prevention.

Jennifer E. Starling, Jared S. Murray, Carlos M. Carvalho, Radek K. Bukowski, James G. Scott.

Source: The Annals of Applied Statistics, Volume 14, Number 1, 28--50.

Abstract:
This article introduces BART with Targeted Smoothing, or tsBART, a new Bayesian tree-based model for nonparametric regression. The goal of tsBART is to introduce smoothness over a single target covariate $t$ while not necessarily requiring smoothness over other covariates $x$. tsBART is based on the Bayesian Additive Regression Trees (BART) model, an ensemble of regression trees. tsBART extends BART by parameterizing each tree’s terminal nodes with smooth functions of $t$ rather than independent scalars. Like BART, tsBART captures complex nonlinear relationships and interactions among the predictors. But unlike BART, tsBART guarantees that the response surface will be smooth in the target covariate. This improves interpretability and helps to regularize the estimate. After introducing and benchmarking the tsBART model, we apply it to our motivating example—pregnancy outcomes data from the National Center for Health Statistics. Our aim is to provide patient-specific estimates of stillbirth risk across gestational age $(t)$ and based on maternal and fetal risk factors $(x)$. Obstetricians expect stillbirth risk to vary smoothly over gestational age but not necessarily over other covariates, and tsBART has been designed precisely to reflect this structural knowledge. The results of our analysis show the clear superiority of the tsBART model for quantifying stillbirth risk, thereby providing patients and doctors with better information for managing the risk of fetal mortality. All methods described here are implemented in the R package tsbart .

Abdelaati Daouia, Stéphane Girard, Gilles Stupfler.

Source: Bernoulli, Volume 26, Number 1, 531--556.

Abstract:
Expectiles define a least squares analogue of quantiles. They are determined by tail expectations rather than tail probabilities. For this reason and many other theoretical and practical merits, expectiles have recently received a lot of attention, especially in actuarial and financial risk management. Their estimation, however, typically requires to consider non-explicit asymmetric least squares estimates rather than the traditional order statistics used for quantile estimation. This makes the study of the tail expectile process a lot harder than that of the standard tail quantile process. Under the challenging model of heavy-tailed distributions, we derive joint weighted Gaussian approximations of the tail empirical expectile and quantile processes. We then use this powerful result to introduce and study new estimators of extreme expectiles and the standard quantile-based expected shortfall, as well as a novel expectile-based form of expected shortfall. Our estimators are built on general weighted combinations of both top order statistics and asymmetric least squares estimates. Some numerical simulations and applications to actuarial and financial data are provided.

Adam R. Brentnall, Jack Cuzick.

Source: Statistical Science, Volume 35, Number 1, 14--30.

Abstract:
Strategies to prevent cancer and diagnose it early when it is most treatable are needed to reduce the public health burden from rising disease incidence. Risk assessment is playing an increasingly important role in targeting individuals in need of such interventions. For breast cancer many individual risk factors have been well understood for a long time, but the development of a fully comprehensive risk model has not been straightforward, in part because there have been limited data where joint effects of an extensive set of risk factors may be estimated with precision. In this article we first review the approach taken to develop the IBIS (Tyrer–Cuzick) model, and describe recent updates. We then review and develop methods to assess calibration of models such as this one, where the risk of disease allowing for competing mortality over a long follow-up time or lifetime is estimated. The breast cancer risk model model and calibration assessment methods are demonstrated using a cohort of 132,139 women attending mammography screening in the State of Washington, USA.

During the Nazi occupation of France, many valuable works of art were stolen from the Jeu de Paume museum and relocated to Germany. One brave French woman kept detailed notes of the thefts

A female leopard is risking life and limb by trying to steal food from another male leopard. One wrong move and the male, a third bigger than she is, could make her pay.

BIS Bulletin No 8, April 2020. Information on local labour markets and Google searches can be used to construct a measure of the vulnerability of employment in different regions of the United States to the Covid-19 shock. Regional exposure to Covid-19 varies significantly, ranging from a low of 2% to a high of 98% of total local employment. We test for the usefulness of the Covid-19 exposure measure by showing that areas with higher exposure report more Google search queries related to the pandemic and unemployment benefits.

Press release about the Basel Committee publishing consultation paper on revisions to the credit valuation adjustment risk framework, 28 November 2019.

Basel Committee Press release "Basel Committee meets to review vulnerabilities and emerging risks, advance supervisory initiatives and promote Basel III implementationl", 27 February 2020.

BCBS Press release "Basel Committee issues progress report on banks' implementation of the 'Principles for effective risk data aggregation and reporting'", 29 April 2020

Quebecers, like the rest of the world, are growing accustomed to the uncertainty that's accompanied the pandemic. But they may not appreciate their government adding to that already hefty burden.

Some Prince Edward Islanders are raising concerns about returning to work under the province's plan to ease back COVID-19 restrictions, but if they choose to stay home they could lose financial support from the federal government.

Michigan officials are ordering a Flint hospital to take steps to reduce the risk of exposure to Legionella bacteria and Legionnaires' disease at the facility.