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This paper describes one aspect of our approach in developing an intelligent off-road autonomous vehicle, the Intelligent Off-Road Navigator (ION), as Team Desert Buckeyes from the Ohio State University for the DARPA Grand Challenge 2005. The real-time navigation is one of the critical components in an autonomous ground vehicle system. In this paper, we focus on the navigation module, whose main responsibility is to generate smooth and obstacle-free local paths with appropriate speed setpoints. For the smooth path generation, we introduce a polynomial interpolation method. To generate obstacle-free paths, a steering controller utilizing a fuzzy obstacle avoidance algorithm is presented. A speed fuzzy controller is introduced to generate the speed setpoints. These two fuzzy controllers collaborate with each other to guide our vehicle ION to the goal safely. The obstacle avoidance algorithm proposed in this paper was also tested in simulations and on small robots successfully. Other issues related to the navigation module are discussed in the paper as well, such as the vehicle's system structure and its finite state machine. As a result, ION achieved great performance in the National Qualification Event, covered about 30 miles in the Nevada Desert with complete autonomous operations, and finished 10th in the Grand Challenge 2005. © 2006 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

Background: Parents play an important role in their children's oral health behaviors, provide oral health access, initiate prevention, and coping strategies for health care. Aim: This paper develops a short form (SF) to assist parents to evaluate their children's oral health status using Patient‐Reported Outcome Measurement Information System (PROMIS) framework that conceptualized health as physical, mental, and social components. Design: Surveys of parents were conducted at dental clinics in Los Angeles County, together with an on‐site clinical examination by dentists to determine clinical outcomes, Children's Oral Health Status Index (COHSI), and referral recommendations (RRs). Graded response models in item response theory were used to create the SF. A toolkit including SF, demographic information, and algorithms was developed to predict the COHSI and RRs. Results: The final SF questionnaire consists of eight items. The square root mean squared error for the prediction of COHSI is 7.6. The sensitivity and specificity of using SF to predict immediate treatment needs (binary RRs) are 85% and 31%. Conclusions: The parent SF is an additional component of the oral health evaluation toolkit that can be used for oral health screening, surveillance program, policy planning, and research of school‐aged children and adolescents from guardian perspectives. [ABSTRACT FROM AUTHOR]

This paper proposes an algorithm that uses forecast encompassing tests for combining forecasts when there are a large number of forecasts that might enter the combination. The algorithm excludes a forecast from the combination if it is encompassed by another forecast. To assess the usefulness of this approach, an extensive empirical analysis is undertaken using a US macroeconomic dataset. The results are encouraging; the algorithm forecasts outperform benchmark model forecasts, in a mean square error (MSE) sense, in a majority of cases. The paper also compares the empirical performance of different approaches to forecast combination, and provides a rule-of-thumb cut-off point for the thick-modeling approach. Copyright © 2009 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This paper introduces for the first time the concept of Bayesian model averaging (BMA) with multiple prior structures, for rainfall‐runoff modeling applications. The original BMA model proposed by Raftery et al. (2005, https://doi.org.10.1175/MWR2906.1) assumes that the prior probability density function (pdf) is adequately described by a mixture of Gamma and Gaussian distributions. Here we discuss the advantages of using BMA with fixed and flexible prior distributions. Uniform, Binomial, Binomial‐Beta, Benchmark, and Global Empirical Bayes priors along with Informative Prior Inclusion and Combined Prior Probabilities were applied to calibrate daily streamflow records of a coastal plain watershed in the southeast United States. Various specifications for Zellner's g prior including Hyper, Fixed, and Empirical Bayes Local (EBL) g priors were also employed to account for the sensitivity of BMA and derive the conditional pdf of each constituent ensemble member. These priors were examined using the simulation results of conceptual and semidistributed rainfall‐runoff models. The hydrologic simulations were first coupled with a new sensitivity analysis model and a parameter uncertainty algorithm to assess the sensitivity and uncertainty associated with each model. BMA was then used to subsequently combine the simulations of the posterior pdf of each constituent hydrological model. Analysis suggests that a BMA based on combined fixed and flexible priors provides a coherent mechanism and promising results for calculating a weighted posterior probability compared to individual model calibration. Furthermore, the probability of Uniform and Informative Prior Inclusion priors received significantly lower predictive error, whereas more uncertainty resulted from a fixed g prior (i.e., EBL). Plain Language Summary: This study presents a two‐step procedure that includes model calibration of a range of hydrological models using DREAM (zs) algorithm, followed by ensemble prediction of streamflow using Bayesian model averaging (BMA) with various prior structures. The hydrological modeling simulations were first coupled with a new sensitivity analysis model and a parameter uncertainty algorithm to assess the sensitivity and uncertainty associated with each hydrologic model simulation. BMA was then used to subsequently combine the simulations on the most important parts of the posterior probabilities of each constituent hydrological model. Analysis suggests a BMA with fixed and flexible priors provides a coherent mechanism and promising results for calibrating a weighted posterior probability compared to individual model calibration. The hierarchy of prior distributions used in this study increased the flexibility of BMA fitting for daily streamflow simulation and reduced the dependence of posterior and predictive uncertainty (including model probabilities) on prior assumptions of hydrological modeling simulation. Key Points: Bayesian model averaging with fixed and flexible prior structures were applied to combine the posterior probability distribution of four hydrological modelsCustom prior inclusion and uniform prior induced a much sharper posterior medianPutting a prior on both θ and g makes the analysis naturally adaptive and avoids the information paradox [ABSTRACT FROM AUTHOR]

Mediation analysis allows the examination of effects of a third variable in the pathway between an exposure and an outcome. The general multiple mediation analysis method, proposed by Yu et al, improves traditional methods (eg, estimation of natural and controlled direct effects) to enable consideration of multiple mediators/confounders simultaneously and the use of linear and nonlinear predictive models for estimating mediation/confounding effects. In this paper, we extend the method for time-to-event outcomes and apply the method to explore the racial disparity in breast cancer survivals. Breast cancer is the most common cancer and the second leading cause of cancer death among women of all races. Despite improvement of survival rates of breast cancer in the US, a significant difference between white and black women remains. Previous studies have found that more advanced and aggressive tumors and less than optimal treatment may explain the lower survival rates for black women as compared to white women. Due to limitations of current analytic methods and the lack of comprehensive data sets, researchers have not been able to differentiate the relative effect each factor contributes to the overall racial disparity. We use the CDC-funded Patterns of Care study to examine the determinants of racial disparities in breast cancer survival using a novel multiple mediation analysis. Using the proposed method, we applied the Cox hazard model and multiple additive regression trees as predictive models and found that all racial disparity in survival among Louisiana breast cancer patients were explained by factors included in the study. [ABSTRACT FROM AUTHOR]

Drug-drug interactions (DDIs) are a common cause of adverse drug events (ADEs). The electronic medical record (EMR) database and the FDA's adverse event reporting system (FAERS) database are the major data sources for mining and testing the ADE associated DDI signals. Most DDI data mining methods focus on pair-wise drug interactions, and methods to detect high-dimensional DDIs in medical databases are lacking. In this paper, we propose 2 novel mixture drug-count response models for detecting high-dimensional drug combinations that induce myopathy. The "count" indicates the number of drugs in a combination. One model is called fixed probability mixture drug-count response model with a maximum risk threshold (FMDRM-MRT). The other model is called count-dependent probability mixture drug-count response model with a maximum risk threshold (CMDRM-MRT), in which the mixture probability is count dependent. Compared with the previous mixture drug-count response model (MDRM) developed by our group, these 2 new models show a better likelihood in detecting high-dimensional drug combinatory effects on myopathy. CMDRM-MRT identified and validated (54; 374; 637; 442; 131) 2-way to 6-way drug interactions, respectively, which induce myopathy in both EMR and FAERS databases. We further demonstrate FAERS data capture much higher maximum myopathy risk than EMR data do. The consistency of 2 mixture models' parameters and local false discovery rate estimates are evaluated through statistical simulation studies. [ABSTRACT FROM AUTHOR]

Achieving high performance for distributed I/O on a wide-area network continues to be an elusive holy grail. Despite enhancements in network hardware as well as software stacks, achieving high-performance remains a challenge. In this paper, our worldwide team took a completely new and non-traditional approach to distributed I/O, called ParaMEDIC: Parallel Metadata Environment for Distributed I/O and Computing, by utilizing application-specific transformation of data to orders of magnitude smaller metadata before performing the actual I/O. Specifically, this paper details our experiences in deploying a large-scale system to facilitate the discovery of missing genes and constructing a genome similarity tree by encapsulating the mpiBLAST sequence-search algorithm into ParaMEDIC. The overall project involved nine computational sites spread across the U.S. and generated more than a petabyte of data that was 'teleported' to a large-scale facility in Tokyo for storage. Copyright © 2010 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Technologies that deploy algorithmic calculation are becoming ubiquitous to the homeland securitization of the war on terror. From the surveillance networks of the city subway to the biometric identifiers of new forms of border control, the possibility to identify “association rules” between people, places, objects and events has brought the logic of pre-emption into the most mundane and prosaic spaces. Yet, it is not the case that the turn to algorithmic calculation simply militarizes society, nor even that we are witnessing strictly a commercialization of security. Rather, algorithmic war is one form of Foucault's sense of a “continuation of war by other means”, where the war-like architectures of self/other, here/there, safe/risky, normal/suspicious are played out in the politics of daily life. This paper explores the situated interplay of algorithmic practices across commercial, security, and military spheres, revealing the violent geographies that are concealed in the glossy techno-science of algorithmic calculation. [ABSTRACT FROM AUTHOR]

Some complex geographic events are associated with multiple point locations. Such events include, but are not limited to, those describing linkages between and among places. The term multi-location event is used in the paper to refer to these geographical phenomena. Through formalization of the multi-location event problem, this paper situates the analysis of multi-location events within the broad context of point pattern analysis techniques. Two alternative approaches (vector autocorrelation analysis and cluster correspondence analysis) to the spatial dependence of paired-location events (i.e., two-location events) are explored, with a discussion of their appropriateness to general multi-location event problems. The research proposes a framework of cluster correspondence analysis for the detection of local non-stationarities in the spatial process generating multi-location events. A new algorithm for local analysis of cluster correspondence is proposed. It is implemented on a large-scale dataset of vehicle theft and recovery location pairs in Buffalo, New York. [ABSTRACT FROM AUTHOR]

s The challenges posed by nonstationarity in predicting catchment water balance components motivated this study to test the stationary versus nonstationarity hypothesis and detect changes in the watershed response to land use land cover (LULC) alterations, and climate variability and change. The focus is on a two-step procedure that includes model calibration of Soil and Water Assessment Tool using a sequential Bayesian uncertainty algorithm (i.e. sequential uncertainty fitting), followed by nonstationary assessment of water balance component using extreme value analysis over an Atlantic coastal plain watershed in the southeastern USA. Analysis suggests that the uncertainty of Soil and Water Assessment Tool model is statistically aligned with LULC alterations that increased the sensitivity of Manning's roughness coefficient, transmission loss and the resistance of the soil matrix to water flow. Changes in LULC along with variability in the magnitude, timing and frequency of precipitation diminished surface runoff and groundwater contribution to the river system whereas it increased evapotranspiration with a substantial decline in water storage capacity. Nonstationary assessment of water balance using extreme value analysis model further revealed a functional form of stationary behaviour (no trends) prior to LULC alteration while large amplification was detected during post-changes. The results and findings presented in this paper confirm our hypothesis about a combined effect of climate and LULC changes on hydrological functions and that variation of these fingerprints elucidates the presence of nonstationarity in the watershed system. Copyright © 2017 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Therapy for polymyalgia rheumatica (PMR) varies widely in clinical practice as international recommendations for PMR treatment are not currently available. In this paper, we report the 2015 European League Against Rheumatism (EULAR)/American College of Rheumatology (ACR) recommendations for the management of PMR. We used the Grading of Recommendations, Assessment, Development and Evaluation (GRADE) methodology as a framework for the project. Accordingly, the direction and strength of the recommendations are based on the quality of evidence, the balance between desirable and undesirable effects, patients' and clinicians' values and preferences, and resource use. Eight overarching principles and nine specific recommendations were developed covering several aspects of PMR, including basic and follow-up investigations of patients under treatment, risk factor assessment, medical access for patients and specialist referral, treatment strategies such as initial glucocorticoid (GC) doses and subsequent tapering regimens, use of intramuscular GCs and disease modifying anti-rheumatic drugs (DMARDs), as well as the roles of non-steroidal anti-rheumatic drugs and non-pharmacological interventions. These recommendations will inform primary, secondary and tertiary care physicians about an international consensus on the management of PMR. These recommendations should serve to inform clinicians about best practices in the care of patients with PMR. [ABSTRACT FROM AUTHOR]

Technical analysis, also known as "charting," has been a part of financial practice for many decades, but this discipline has not received the same level of academic scrutiny and acceptance as more traditional approaches such as fundamental analysis. One of the main obstacles is the highly subjective nature of technical analysis--the presence of geometric shapes in historical price charts is often in the eyes of the beholder. In this paper, we propose a systematic and automatic approach to technical pattern recognition using nonparametric kernel regression, and we apply this method to a large number of U.S. stocks from 1962 to 1996 to evaluate the effectiveness of technical analysis. By comparing the unconditional empirical distribution of daily stock returns to the conditional distribution-conditioned on specific technical indicators such as head-and-shoulders or double-bottoms--we find that over the 31-year sample period, several technical indicators do provide incremental information and may have some practical value. [ABSTRACT FROM AUTHOR]

This paper describes the implementation and testing of Alice, the California Institute of Technology's entry in the 2005 DARPA Grand Challenge. Alice utilizes a highly networked control system architecture to provide high performance autonomous driving in unknown environments. Innovations include a vehicle architecture designed for efficient testing in harsh environments, a highly sensory-driven approach to fuse sensor data into speed maps used by real-time trajectory optimization algorithms, health and contingency management algorithms to manage failures at the component and system level, and a software logging and display environment that enables rapid assessment of performance during testing. The system successfully completed several runs in the National Qualifying Event, but encountered a combination of sensing and control issues in the Grand Challenge Event that led to a critical failure after traversing approximately 8 miles. © 2006 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

In this paper, I propose to set out a legit spatial association model for binary spatial events and develop a scan algorithm to search for spatial associations. I extend the traditional legit model with a spatial autocorrelated component so that the model includes not only known risk factors, but also spatially autocorrelated regions as control or explanatory factors. The case study of West Virginia lung cancer shows that the model effectively captures cool and hot spots in lung cancer mortality. [ABSTRACT FROM AUTHOR]

Background: Poor functional status is a key marker of morbidity, yet is not routinely captured in clinical encounters. We developed and evaluated the accuracy of a machine learning algorithm that leveraged electronic health record (EHR) data to provide a scalable process for identification of functional impairment. Methods: We identified a cohort of patients with an electronically captured screening measure of functional status (Older Americans Resources and Services ADL/IADL) between 2018 and 2020 (N = 6484). Patients were classified using unsupervised learning K means and t‐distributed Stochastic Neighbor Embedding into normal function (NF), mild to moderate functional impairment (MFI), and severe functional impairment (SFI) states. Using 11 EHR clinical variable domains (832 variable input features), we trained an Extreme Gradient Boosting supervised machine learning algorithm to distinguish functional status states, and measured prediction accuracies. Data were randomly split into training (80%) and test (20%) sets. The SHapley Additive Explanations (SHAP) feature importance analysis was used to list the EHR features in rank order of their contribution to the outcome. Results: Median age was 75.3 years, 62% female, 60% White. Patients were classified as 53% NF (n = 3453), 30% MFI (n = 1947), and 17% SFI (n = 1084). Summary of model performance for identifying functional status state (NF, MFI, SFI) was AUROC (area under the receiving operating characteristic curve) 0.92, 0.89, and 0.87, respectively. Age, falls, hospitalization, home health use, labs (e.g., albumin), comorbidities (e.g., dementia, heart failure, chronic kidney disease, chronic pain), and social determinants of health (e.g., alcohol use) were highly ranked features in predicting functional status states. Conclusion: A machine learning algorithm run on EHR clinical data has potential utility for differentiating functional status in the clinical setting. Through further validation and refinement, such algorithms can complement traditional screening methods and result in a population‐based strategy for identifying patients with poor functional status who need additional health resources. [ABSTRACT FROM AUTHOR]

The paper describes a general approach to the modelling of nonlinear and nonstationary economic systems from time-series data. This method exploits recursive state space filtering and fixed interval smoothing algorithms to decompose the time-series into long term trend and short term 'small perturbational' components, each of which are then modelled by linear stochastic models which may be characterised by time variable parameters. The approach is illustrated by an example which explores the relationship between the variations in quarterly GNP and Unemployment in the USA over the period 1948 to 1988 and questions previous claims about the changes in the slope of the long term trend in loge(GNP) over this same period. The paper also points out that the recursive approach to estimation facilitates the use of these methods in the development of adaptive forecasting and control systems. [ABSTRACT FROM AUTHOR]

In algorithmic work, algorithms execute operational and management tasks such as work allocation, task tracking and performance evaluation. Humans and algorithms interact with one another to accomplish work so that the algorithm takes on the role of a co‐worker. Human–algorithm interactions are characterised by problematic issues such as absence of mutually co‐constructed dialogue, lack of transparency regarding how algorithmic outputs are generated, and difficulty of over‐riding algorithmic directive – conditions that create lack of clarity for the human worker. This article examines human–algorithm role interactions in algorithmic work. Drawing on the theoretical framing of organisational roles, we theorise on the algorithm as role sender and the human as the role taker. We explain how the algorithm is a multi‐role sender with entangled roles, while the human as role taker experiences algorithm‐driven role conflict and role ambiguity. Further, while the algorithm records all of the human's task actions, it is ignorant of the human's cognitive reactions – it undergoes what we conceptualise as 'broken loop learning'. The empirical context of our study is algorithm‐driven taxi driving (in the United States) exemplified by companies such as Uber. We draw from data that include interviews with 15 Uber drivers, a netnographic study of 1700 discussion threads among Uber drivers from two popular online forums, and analysis of Uber's web pages. Implications for IS scholarship, practice and policy are discussed. [ABSTRACT FROM AUTHOR]

The article presents a study which examined the association between social isolation and incident dementia over nine years in large and nationally representative sample of Medicare beneficiaries in the U.S. Topics include criteria for participants to be classified as having probable dementia, distribution of baseline participant characteristics, and characteristics of baseline participants for the total sample and by social isolation status.

Aim: To develop and validate models based on logistic regression and artificial intelligence for prognostic prediction of molar survival in periodontally affected patients. Materials and Methods: Clinical and radiographic data from four different centres across four continents (two in Europe, one in the United States, and one in China) including 515 patients and 3157 molars were collected and used to train and test different types of machine‐learning algorithms for their prognostic ability of molar loss over 10 years. The following models were trained: logistic regression, support vector machine, K‐nearest neighbours, decision tree, random forest, artificial neural network, gradient boosting, and naive Bayes. In addition, different models were aggregated by means of the ensembled stacking method. The primary outcome of the study was related to the prediction of overall molar loss (MLO) in patients after active periodontal treatment. Results: The general performance in the external validation settings (aggregating three cohorts) revealed that the ensembled model, which combined neural network and logistic regression, showed the best performance among the different models for the prediction of MLO with an area under the curve (AUC) = 0.726. The neural network model showed the best AUC of 0.724 for the prediction of periodontitis‐related molar loss. In addition, the ensembled model showed the best calibration performance. Conclusions: Through a multi‐centre collaboration, both prognostic models for the prediction of molar loss were developed and externally validated. The ensembled model showed the best performance in terms of both discrimination and validation, and it is made freely available to clinicians for widespread use in clinical practice. [ABSTRACT FROM AUTHOR]

Rationale, Aims and Objectives: Critics have charged that evidence‐based medicine (EBM) overemphasises algorithmic rules over unstructured clinical experience and intuition, but the role of structured decision support systems in improving health outcomes remains uncertain. We aim to assess if delivery of anticoagulant prophylaxis in hospitalised patients with COVID‐19 according to an algorithm based on evidence‐based clinical practice guideline (CPG) improved clinical outcomes compared with administration of anticoagulant treatment given at individual practitioners' discretion. Methods: An observational design consisting of the analysis of all acutely ill, consecutive patients (n = 1783) with confirmed COVID‐19 diagnosis admitted between 10 March 2020 to 11 January 2022 to an US academic center. American Society of Haematology CPG for anticoagulant prophylaxis in hospitalised patients with COVID‐19 was converted into a clinical pathway and translated into fast‐and‐frugal decision (FFT) tree ('algorithm'). We compared delivery of anticoagulant prophylaxis in hospitalised patients with COVID‐19 according to the FFT algorithm with administration of anticoagulant treatment given at individual practitioners' discretion. Results: In an adjusted analysis, using combination of Lasso (least absolute shrinkage and selection operator) and propensity score based weighting [augmented inverse‐probability weighting] statistical techniques controlling for cluster data, the algorithm did not reduce death, venous thromboembolism, or major bleeding, but helped avoid longer hospital stay [number of patients needed to be treated (NNT) = 40 (95% CI: 23–143), indicating that for every 40 patients (23–143) managed on FFT algorithm, one avoided staying in hospital longer than 10 days] and averted admission to intensive‐care unit (ICU) [NNT = 19 (95% CI: 13–40)]. All model's selected covariates were well balanced. The results remained robust to sensitivity analyses used to test the stability of the findings. Conclusions: When delivered using a structured FFT algorithm, CPG shortened the hospital stay and help avoided admission to ICU, but it did not affect other relevant outcomes. [ABSTRACT FROM AUTHOR]

Spatial prediction is commonly achieved under the assumption of a Gaussian random field by obtaining maximum likelihood estimates of parameters, and then using the kriging equations to arrive at predicted values. For massive datasets, fixed rank kriging using the expectation–maximization algorithm for estimation has been proposed as an alternative to the usual but computationally prohibitive kriging method. The method reduces computation cost of estimation by redefining the spatial process as a linear combination of basis functions and spatial random effects. A disadvantage of this method is that it imposes constraints on the relationship between the observed locations and the knots. We develop an alternative method that utilizes the spatial mixed effects model, but allows for additional flexibility by estimating the range of the spatial dependence between the observations and the knots via an alternating expectation conditional maximization algorithm. Experiments show that our methodology improves estimation without sacrificing prediction accuracy while also minimizing the additional computational burden of extra parameter estimation. The methodology is applied to a temperature dataset archived by the United States National Climate Data Center, with improved results over previous methodology. [ABSTRACT FROM AUTHOR]

The joinpoint regression model (JRM) is used to describe trend changes in many applications and relies on the detection of joinpoints (changepoints). However, the existing joinpoint detection methods, namely, the grid search (GS)-based methods, are computationally demanding, and hence, the maximum number of computable joinpoints is limited. Herein, we developed a genetic algorithm-based joinpoint (GAJP) model in which an explicitly decoupled computing procedure for optimization and regression is used to embed a binary genetic algorithm into the JRM for optimal joinpoint detection. The combinations of joinpoints were represented as binary chromosomes, and genetic operations were performed to determine the optimum solution by minimizing the fitness function, the Bayesian information criterion (BIC) and BIC3 . The accuracy and computational performance of the GAJP model were evaluated via intensive simulation studies and compared with those of the GS-based methods using BIC, BIC3 , and permutation test. The proposed method showed an outstanding computational efficiency in detecting multiple joinpoints. Finally, the suitability of the GAJP model for the analysis of cancer incidence trends was demonstrated by applying this model to data on the incidence of colorectal cancer in the United States from 1975 to 2016 from the National Cancer Institute's Surveillance, Epidemiology, and End Results program. Thus, the GAJP model was concluded to be practically feasible to detect multiple joinpoints up to the number of grids without requirement to preassign the number of joinpoints and be easily extendable to cancer trend analysis utilizing large datasets. [ABSTRACT FROM AUTHOR]

Missingness in historical climate data networks is a pervasive phenomenon due to the conditions under which these measurements are made. Accurate estimation of these data is a critical issue as projections of future climate depend on a reliable historical climate record. After all, how can the impact of climate change be reliably forecasted when a large proportion of historical climate records are permeated with missing data? We propose an iterative multivariate infilling algorithm and explore its effectiveness on three United States temperature networks of varying densities (number of stations per unit area). Although other multivariate data are not explored here, the application of this infilling procedure is not restricted to climate networks exclusively. We also explore its utility as a function of the type of missing data (i.e., whether or not there is a mechanism or pattern behind the missing observations themselves) and the proportion of missing data within the network. As one may expect, we observe a slightly smaller root mean square error (RMSE) for temperature networks with more stations and less missingness. Somewhat surprisingly, the RMSE tends to be lower for data which is missing at random (there is some mechanism behind the missing data) rather than for data that is missing completely at random (no mechanism behind the missing data). Not surprisingly, the RMSE is largest for data which is missing not at random (missing data are directly related to the values of the observations themselves). Perhaps the most surprising result observed in these networks is that the inclusion of lagged temperature data does not necessarily improve the accuracy of data imputation. The typical RMSE for monthly minimum temperature networks varies from around 1.5 to 2.5°C. This article is categorized under:Data: Types and Structure > Massive DataData: Types and Structure > Image and Spatial DataData: Types and Structure > Time Series, Stochastic Processes, and Functional DataApplications of Computational Statistics > Computational Climate Change and Numerical Weather Forecasting [ABSTRACT FROM AUTHOR]

Objective: To develop a cardiovascular disease (CVD) risk score that can be used to quantify CVD risk in the Medicare Current Beneficiary Survey (MCBS).Data Sources: We used 1999-2013 MCBS data.Study Design: We used a backward stepwise approach and cox proportional hazards regressions to build and validate a new CVD risk score, similar to the Framingham Risk Score (FRS), using only information available in MCBS. To assess its performance, we calculated C statistics and examined calibration plots.Data Collection/extraction Methods: We studied 21 968 community-dwelling Medicare beneficiaries aged 65 years or older without pre-existing CVD. We obtained risk factors from both survey and claims data. We used claims data to derive "CVD event within 3 years" following the FRS definition of CVD.Principal Findings: About five percent of MCBS participants developed a CVD event over a mean follow-up period of 348 days. Our final MCBS-based model added morbidity burden, reported general health status, and functional limitation to the traditional FRS predictors of CVD. This model had relatively fair discrimination (C statistic = 0.69; 95% confidence interval [CI], 0.67-0.71) and performed well on validation (C = 0.68; CI, 0.66-0.70). More importantly, the plot of observed CVD outcomes versus predicted ones showed that this model had a good calibration.Conclusions: Our new CVD risk score can be calculated using MCBS data, thereby extending the survey's ability to quantify CVD risk in the Medicare population and better inform both health policy and health services research. [ABSTRACT FROM AUTHOR]

Donor lung allocation in the United States focuses on decreasing waitlist mortality and improving recipient outcomes. The implementation of allocation policy to match deceased donor lungs to waitlisted patients occurs through a unique partnership between government and private organizations, namely the Organ Procurement and Transplantation Network under the Department of Health and Human Services and the United Network for Organ Sharing. In 2005, the donor lung allocation algorithm shifted toward the prioritization of medical urgency of waitlisted patients instead of time accrued on the waitlist. This led to the Lung Allocation Score, which weighs over a dozen clinical variables to predict a 1‐year estimate of survival benefit, and is used to prioritize waitlisted patients. In 2017, the use of local allocation boundaries was eliminated in favor of a 250 nautical mile radius from the donor hospital as the first unit of distance used in allocation. The next upcoming iteration of donor allocation policy is expected to use a continuous distribution algorithm where all geographic boundaries are eliminated. There are additional opportunities to improve donor lung allocation, such as for patients with high antibody titers with access to a limited number of donors. [ABSTRACT FROM AUTHOR]

The article presents information related to proceedings in the field of Mathematics research. The Third Siam Conference on Applications of Dynamical Systems, will be held from May 21, 1995 to May 24, 1995, at Snowbird, Utah. The First International Conference on Neural, Parallel, Scientific Computations will be held from May 28, 1995 to May 31, 1995, at Atlanta, Georgia. The symposium on Inertial Manifolds, Approximate Inertial Manifolds and Related Numerical Algorithms will be held from June 6, 1995 to June 10, 1995, at Xi'an, China.

Objectives: To compare existing algorithms for classifying screening vs diagnostic colonoscopies and to quantify the increase in screening colonoscopy rates when Medicare began reimbursement in 2001 and when the Affordable Care Act (ACA) eliminated cost-sharing.Data Sources: Twenty percent random sample of fee-for-service (FFS) Medicare claims, 2000-2012.Study Design: Using recent administrative codes as tarnished gold standards, we examined the sensitivity and specificity of five published algorithms for classifying colonoscopies and calculated annual screening colonoscopy rates. We estimated the change in rates after Medicare began reimbursement and used difference-in-differences analysis to estimate the effects of eliminating cost-sharing by comparing states with and without a mandate to cover screening colonoscopy prior to the ACA.Findings: Model-based algorithms have higher sensitivity (0.53-0.99) than expert-based algorithms (0.35-0.39), but lower specificity (0.43-0.65 vs 0.79-0.88). All algorithms detected increases in screening from both Medicare's reimbursement change (range: 24-93/10 000) and the 2011 cost-sharing change (range: 1.1-34/10 000). Difference-in-difference estimates of the ACA's effect varied from 51 to 155 tests per 10 000 depending on the algorithm.Conclusions: Screening colonoscopy rates increased after eliminating cost-sharing in 2011, but the increase's size varied depending on the algorithm used to classify the indication. Improvements are needed in Medicare coding for screening. [ABSTRACT FROM AUTHOR]

Publicly available national survey data are useful for the evidence-based research to advance our understanding of important questions in the health and biomedical sciences. Appropriate variance estimation is a crucial step to evaluate the strength of evidence in the data analysis. In survey data analysis, the conventional linearization method for estimating the variance of a statistic of interest uses the variance estimator of the total based on linearized variables. We warn that this common practice may result in undesirable consequences such as susceptibility to data shift and severely inflated variance estimates, when unequal weights are incorporated into variance estimation. We propose to use the variance estimator of the mean (mean-approach) instead of the variance estimator of the total (total-approach). We show a superiority of the mean-approach through analytical investigations. A real data example (the National Comorbidity Survey Replication) and simulation-based studies strongly support our conclusion. [ABSTRACT FROM AUTHOR]

We examine the origins, implications, and consequences of yield-based N fertilizer management. Yield-based algorithms have dominated N fertilizer management of corn (Zea mays) in the United States for almost 50 yr, and similar algorithms have been used all over the world to make fertilizer recommendations for other crops. Beginning in the mid-1990s, empirical research started to show that yield-based rules-of-thumb in general are not a useful guide to fertilizer management. Yet yield-based methods continue to be widely used, and are part of the principal algorithms of nearly all current "decision tool" software being sold to farmers for N management. We present details of the theoretical and empirical origins of yield-based management algorithms, which were introduced by George Stanford (1966, 1973) as a way to make N fertilizer management less reliant on data. We show that Stanford's derivation of his "1.2 Rule" was based on very little data, questionable data omissions, and negligible and faulty statistical analysis. We argue that, nonetheless, researchers, outreach personnel, and private-sector crop management consultants were obliged to give some kind of N management guidance to farmers. Since data generation is costly, it is understandable that a broad, "ball park" rule-of-thumb was developed, loosely based on agronomic principles. We conclude by suggesting that technology changes now allow for exciting new possibilities in data-intensive fertilizer management research, which may lead to more efficient N management possibilities in the near future. [ABSTRACT FROM AUTHOR]

Objective: Develop and validate a surveillance model to identify outpatient surgical adverse events (AEs) based on previously developed electronic triggers.Data Sources: Veterans Health Administration's Corporate Data Warehouse.Study Design: Six surgical AE triggers, including postoperative emergency room visits and hospitalizations, were applied to FY2012-2014 outpatient surgeries (n = 744,355). We randomly sampled trigger-flagged and unflagged cases for nurse chart review to document AEs and measured positive predictive value (PPV) for triggers. Next, we used chart review data to iteratively estimate multilevel logistic regression models to predict the probability of an AE, starting with the six triggers and adding in patient, procedure, and facility characteristics to improve model fit. We validated the final model by applying the coefficients to FY2015 outpatient surgery data (n = 256,690) and reviewing charts for cases at high and moderate probability of an AE.Principal Findings: Of 1,730 FY2012-2014 reviewed surgeries, 350 had an AE (20 percent). The final surveillance model c-statistic was 0.81. In FY2015 surgeries with >0.8 predicted probability of an AE (n = 405, 0.15 percent), PPV was 85 percent; in surgeries with a 0.4-0.5 predicted probability of an AE, PPV was 38 percent.Conclusions: The surveillance model performed well, accurately identifying outpatient surgeries with a high probability of an AE. [ABSTRACT FROM AUTHOR]

We exhibit the convexity ratio of voting districts in many states of the USA, which have had their plans challenged. The convexity ratio confirms that these states have likely been gerrymandered. We then redistrict the largest of these states, Texas, avoiding gerrymandering by starting with counties and their populations and only adding or deleting municipalities as necessary. The voting districts themselves are made as nicely shaped as possible, using the convexity ratio as a guide. Those districts with immovable boundaries that cause a poorly shaped designation, a topic of much current research, are dealt with appropriately in the context of the convexity ratio. Algorithms for finding the convexity ratio and designing non‐gerrymandered districts are shown. Other more probabilistic convexity measures are discussed. Finally, we comment on how the convexity ratio can be used in other countries, both politically and in other contexts, for example, in territory design for geomarketing and on the electrical or police districting problem. [ABSTRACT FROM AUTHOR]

At sentencing, youth can be considered both a mitigating circumstance because of its association with diminished culpability, and an aggravating circumstance because of its association with crime risk. In theory, judges and parole boards can recognize this double-edged sword phenomenon and balance the mitigating and aggravating effects of youth. But when sentencing authorities rely on algorithmic risk assessments, a practice that is becoming increasingly common, this balancing process may never take place. Algorithmic risk assessments often place heavy weights on age in a manner that is not fully transparent - or, in the case of proprietary "black box" algorithms, not transparent at all. For instance, our analysis of one of the leading black-box tools, the COMPAS Violent Recidivism Risk Score, shows that roughly 60% of the risk score it produces is attributable to age. We argue that this type of fact must be disclosed to sentencing authorities in an easily interpretable manner so that they understand the role an offender's age plays in the risk calculation. Failing to reveal that a stigmatic label such as "high risk of violent crime" is due primarily to a defendant's young age could lead to improper condemnation of a youthful offender, especially given the close association between risk labels and perceptions of character and moral blameworthiness. [ABSTRACT FROM AUTHOR]

Objectives To examine the effect of the Tool to Reduce Inappropriate Medications ( TRIM), a web tool linking an electronic health record ( EHR) to a clinical decision support system, on medication communication and prescribing. Design Randomized clinical trial. Setting Primary care clinics at a Veterans Affairs Medical Center. Participants Veterans aged 65 and older prescribed seven or more medications randomized to receipt of TRIM or usual care (N = 128). Intervention TRIM extracts information on medications and chronic conditions from the EHR and contains data entry screens for information obtained from brief chart review and telephonic patient assessment. These data serve as input for automated algorithms identifying medication reconciliation discrepancies, potentially inappropriate medications ( PIMs), and potentially inappropriate regimens. Clinician feedback reports summarize discrepancies and provide recommendations for deprescribing. Patient feedback reports summarize discrepancies and self-reported medication problems. Measurements Primary: subscales of the Patient Assessment of Care for Chronic Conditions ( PACIC) related to shared decision-making; clinician and patient communication. Secondary: changes in medications. Results 29.7% of TRIM participants and 15.6% of control participants provided the highest PACIC ratings; this difference was not significant. Adjusting for covariates and clustering of patients within clinicians, TRIM was associated with significantly more-active patient communication and facilitative clinician communication and with more medication-related communication among patients and clinicians. TRIM was significantly associated with correction of medication discrepancies but had no effect on number of medications or reduction in PIMs. Conclusion TRIM improved communication about medications and accuracy of documentation. Although there was no association with prescribing, the small sample size provided limited power to examine medication-related outcomes. [ABSTRACT FROM AUTHOR]

Background Alcohol consumption patterns on the U.S.-Mexico border and their relationships with DSM-5 alcohol use disorders ( AUD) have been understudied. Yet, the effects of drinking by Mexican-origin individuals may differ between cities on versus off the border both in the United States and in Mexico. We characterize prior 12-month drinking patterns and examine their relationships with AUD, in border and off-border cities of Texas and adjacent Mexican states. Methods Data come from the U.S.-Mexico Study of Alcohol and Related Conditions involving 2,336 Mexican Americans in Texas and 2,460 Mexicans in bordering states of Nuevo Leon and Tamaulipas in Mexico. Drinking pattern was defined as an interaction between volume and maximum amount, or intensity (never vs. ever 5+/4+ [men/women], 8+, and 12+ drinks in a day). DSM-5 AUD was assessed using an adaptation of the Alcohol Section of the World Health Organization Composite International Diagnostic Interview core. Separately by gender, 5 logistic regressions models controlling for age were estimated predicting symptoms in 2 or more AUD criteria domains from volume, heavy pattern and, successively, effects of country, and (by country) residing on vs. off the border, or in each of 3 cities/country. Results A segmentation analysis for Texas males based on rate of experiencing AUD generated several distinct volume groups, each partitioned by an empirically selected maximum, and helped identify a drinking-pattern typology. In gender-stratified models of AUD rates using this typology, adjusting for age, significant volume and intensity effects were seen, more strongly in the United States. Border versus interior differences implied more AUD for given patterns at the border in the United States and the reverse in Mexico, with some city differences also evident. Conclusions Drinking-pattern analyses confirm that border proximity may affect drinking problems but in opposite directions in the United States and Mexico, possibly related to economic and psychological stresses specific to respective communities. [ABSTRACT FROM AUTHOR]

This article proposes, exemplifies, and validates the use of course-subject co-occurrence ( CSCO) data to generate topic maps of an academic discipline. A CSCO event is when 2 course-subjects are taught in the same academic year by the same teacher. A total of 61,856 CSCO events were extracted from the 2010-11 directory of the American Association of Law Schools and used to visualize the structure of law school education in the United States. Different normalization, ordination (layout), and clustering algorithms were compared and the best performing algorithm of each type was used to generate the final map. Validation studies demonstrate that CSCO produces topic maps that are consistent with expert opinion and 4 other indicators of the topical similarity of law school course-subjects. This research is the first to use CSCO to produce a visualization of a domain. It is also the first to use an expanded, multi-part gold standard to evaluate the validity of domain maps and the intermediate steps in their creation. It is suggested that the framework used herein may be adopted for other studies that compare different inputs of a domain map in order to empirically derive the best maps as measured against extrinsic sources of topical similarity (gold standards). [ABSTRACT FROM AUTHOR]

Objective: Electronic clinical quality measures (eCQMs) rely on computer algorithms to extract data from electronic health records (EHRs). On behalf of the American College of Rheumatology (ACR), we sought to develop and test eCQMs for rheumatoid arthritis (RA).Methods: Drawing from published ACR guidelines, a working group developed candidate RA process measures and subsequently assessed face validity through an interdisciplinary panel of health care stakeholders. A public comment period followed. Measures that passed these levels of review were electronically specified using the quality data model, which provides standard nomenclature for data elements (category, datatype, and value sets) obtained through an EHR. For each eCQM, 3 clinical sites using different EHR systems tested the scientific feasibility and validity of measures. Measures appropriate for accountability were presented for national endorsement.Results: Expert panel validity ratings were high for all measures (median 8-9 of 9). Health system performance on the eCQMs was 53.6% for RA disease activity assessment, 69.1% for functional status assessment, 93.1% for disease-modifying antirheumatic drug (DMARD) use, and 72.8% for tuberculosis screening. Kappa statistics, which evaluated whether the eCQM validly captured data obtained from manual EHR chart review, demonstrated moderate to substantial agreement (0.54 for functional status assessment, 0.73 for tuberculosis screening, 0.84 for disease activity, and 0.85 for DMARD use).Conclusion: Four eCQMs for RA have achieved national endorsement and are recommended for use in federal quality reporting programs. Implementation and further refinement of these measures is ongoing in the ACR's registry, the Rheumatology Informatics System for Effectiveness (RISE). [ABSTRACT FROM AUTHOR]

Objectives To describe the prevalence of discrepancies between medication lists that referring providers and home healthcare ( HH) nurses create. Design The active medication list from the hospital at time of HH initiation was compared with the HH agency's plan of care medication list. An electronic algorithm was developed to compare the two lists for discrepancies. Setting Single large hospital and HH agency in the western United States. Participants Individuals referred for HH from the hospital in 2012 (N = 770, 96.3% male, median age 71). Measurements Prevalence was calculated for discrepancies, including medications missing from one list or the other and differences in dose, frequency, or route for medications contained on both lists. Results Participants had multiple medical problems (median 16 active problems) and were taking a median of 15 medications (range 1-93). Every participant had at least one discrepancy; 90.1% of HH lists were missing at least one medication that the referring provider had prescribed, 92.1% of HH lists contained medications not on the referring provider's list, 89.8% contained medication naming errors. 71.0% contained dosing discrepancies, and 76.3% contained frequency discrepancies. Conclusion Discrepancies between HH and referring provider lists are common. Future work is needed to address possible safety and care coordination implications of discrepancies in this highly complex population. [ABSTRACT FROM AUTHOR]

The increasing abundance of digital textual archives provides an opportunity for understanding human social systems. Yet the literature has not adequately considered the disparate social processes by which texts are produced. Drawing on communication theory, we identify three common processes by which documents might be detectably similar in their textual features-authors sharing subject matter, sharing goals, and sharing sources. We hypothesize that these processes produce distinct, detectable relationships between authors in different kinds of textual overlap. We develop a novel n-gram extraction technique to capture such signatures based on n-grams of different lengths. We test the hypothesis on a corpus where the author attributes are observable: the public statements of the members of the U. S. Congress. This article presents the first empirical finding that shows different social relationships are detectable through the structure of overlapping textual features. Our study has important implications for designing text modeling techniques to make sense of social phenomena from aggregate digital traces. [ABSTRACT FROM AUTHOR]

Background Despite the prevalence of fetal alcohol spectrum disorders (FASD) and the importance of accurate identification of patients, clinical diagnosis may not be consistent across sites due to the heterogeneous nature of FASD and the characteristics of different diagnostic systems used. Here, we compare 5 systems designed to operationalize criteria recommended for the diagnosis of effects of prenatal alcohol exposure ( PAE). We determined the extent of consistency among them as well as factors that may reduce intersystem reliability. Compared are: Emory Clinic, Seattle 4-Digit System (Diagnostic Guidelines for Fetal Alcohol Spectrum Disorders: The 4-Digit Diagnostic Code, Seattle, WA, University Publication Services, 2004), Centers for Disease Control and Prevention (Fetal Alcohol Syndrome: Guidelines for Referral and Diagnosis, Department of Health and Human Services, Centers for Disease Control and Prevention, Atlanta, GA, 2004), Canadian Guidelines (CMAJ, 172, 2005, S1), and the Hoyme Modifications (Pediatrics, 115, 2005, 39). Methods Subjects were 1,581 consecutively registered patients applying for evaluation at a university-based clinic treating alcohol and drug-exposed children. Records of the multidisciplinary evaluation (pediatric, social, psychological) were abstracted. Diagnostic criteria for all 5 systems were applied, and patients were diagnosed according to each of the systems. We compared results using Cohen's Kappa to evaluate the extent of agreement. Results Percent of individuals diagnosed with FASD ranged from 4.74% ( CDC) to 59.58% (Hoyme). Examination using Cohen's Kappa found modest agreement among systems, particularly when individual diagnoses, Fetal Alcohol Syndrome ( FAS), partial FAS ( pFAS), and Alcohol-Related Neurodevelopmental Disorder ( ARND) were used. Examination of diagnostic criteria found almost perfect agreement on growth (weight; height), with limited overlap for physical features (palpebral fissures, hypoplastic philtrum, upper vermillion) and for neurobehavioral outcomes. Child's race and age influenced agreement among systems, with African American and older children more frequently diagnosed. Conclusions Results suggest problems in convergent validity among these systems, as demonstrated by a lack of reliability in diagnosis. Absence of an external standard makes it impossible to determine whether any system is more accurate, but outcomes do suggest areas for future research that may refine diagnosis. [ABSTRACT FROM AUTHOR]

Objectives: To assess the feasibility of using existing claims-based algorithms to identify community-dwelling Medicare beneficiaries with disability based solely on the conditions for which they are being treated, and improving on these algorithms by combining them in predictive models.Data Source: Data on 12,415 community-dwelling fee-for-service Medicare beneficiaries who first responded to the Medicare Current Beneficiary Survey (MCBS) in 2003-2006.Study Design: Logistic regression models in which six claims-based disability indicators are used to predict self-reported disability. Receiver operating characteristic (ROC) curves were used to assess the performance of the predictive models.Principal Findings: The predictive performance of the regression-based models is better than that of the individual claims-based indicators. At a predicted probability threshold chosen to maximize the sum of sensitivity and specificity, sensitivity is 0.72 for beneficiaries age 65 or older and specificity is 0.65. For those under 65, sensitivity is 0.54 and specificity is 0.67. The findings also suggest ways to improve predictive performance for specific disability populations of interest to researchers.Conclusions: Predictive models that incorporate multiple claims-based indicators provide an improved tool for researchers seeking to identify people with disabilities in claims data. [ABSTRACT FROM AUTHOR]

Objective To develop a new evidence-based, pharmacologic treatment guideline for rheumatoid arthritis (RA). Methods We conducted systematic reviews to synthesize the evidence for the benefits and harms of various treatment options. We used the Grading of Recommendations Assessment, Development and Evaluation (GRADE) methodology to rate the quality of evidence. We employed a group consensus process to grade the strength of recommendations (either strong or conditional). A strong recommendation indicates that clinicians are certain that the benefits of an intervention far outweigh the harms (or vice versa). A conditional recommendation denotes uncertainty over the balance of benefits and harms and/or more significant variability in patient values and preferences. Results The guideline covers the use of traditional disease-modifying antirheumatic drugs (DMARDs), biologic agents, tofacitinib, and glucocorticoids in early (<6 months) and established (≥6 months) RA. In addition, it provides recommendations on using a treat-to-target approach, tapering and discontinuing medications, and the use of biologic agents and DMARDs in patients with hepatitis, congestive heart failure, malignancy, and serious infections. The guideline addresses the use of vaccines in patients starting/receiving DMARDs or biologic agents, screening for tuberculosis in patients starting/receiving biologic agents or tofacitinib, and laboratory monitoring for traditional DMARDs. The guideline includes 74 recommendations: 23% are strong and 77% are conditional. Conclusion This RA guideline should serve as a tool for clinicians and patients (our two target audiences) for pharmacologic treatment decisions in commonly encountered clinical situations. These recommendations are not prescriptive, and the treatment decisions should be made by physicians and patients through a shared decision-making process taking into account patients' values, preferences, and comorbidities. These recommendations should not be used to limit or deny access to therapies. [ABSTRACT FROM AUTHOR]

Objectives To compare processes and cost of care of older adults with diabetes mellitus cared for by nurse practitioners ( NPs) with processes and cost of those cared for by primary care physicians ( PCPs). Design Retrospective cohort study. Setting Primary care in communities. Participants Individuals with a diagnosis of diabetes mellitus in 2009 who received all their primary care from NPs or PCPs were selected from a national sample of Medicare beneficiaries (N = 64,354). Measurements Propensity score matching within each state was used to compare these two cohorts with regard to rate of eye examinations, low-density lipoprotein cholesterol ( LDL-C) and glycosylated hemoglobin (HbA1C) testing, nephropathy monitoring, specialist consultation, and Medicare costs. The two groups were also compared regarding medication adherence and use of statins, angiotensin-converting enzyme inhibitors or angiotensin receptor blockers (for individuals with a diagnosis of hypertension), and potentially inappropriate medications ( PIMs). Results Nurse practitioners and PCPs had similar rates of LDL-C testing (odds ratio (OR) = 1.01, 95% confidence interval (CI) = 0.94-1.09) and nephropathy monitoring (OR = 1.05, 95% CI = 0.98-1.03), but NPs had lower rates of eye examinations (OR = 0.89, 95% CI = 0.84-0.93) and HbA1C testing (OR = 0.88, 95% CI = 0.79-0.98). NPs were more likely to have consulted cardiologists (OR = 1.29, 95% CI = 1.21-1.37), endocrinologists (OR = 1.64, 95% CI = 1.48-1.82), and nephrologists (OR = 1.90, 95% CI = 1.67-2.17) and more likely to have prescribed PIMs (OR = 1.07, 95% CI = 1.01-1.12). There was no statistically significant difference in adjusted Medicare spending between the two groups ( P = .56). Conclusion Nurse practitioners were similar to PCPs or slightly lower in their rates of diabetes mellitus guideline-concordant care. NPs used specialist consultations more often but had similar overall costs of care to PCPs. [ABSTRACT FROM AUTHOR]

A letter to the editor is presented which addresses the authors' claim that that the precancerous condition Barrett's esophagus (BE) is commonly diagnosed in elderly men with limited life expectancy (LE), and it mentions a U.S. national, cross-sectional study of LE in male veterans with BE.

The spatial error structure of surface precipitation derived from successive versions of the TRMM Multisatellite Precipitation Analysis (TMPA) algorithms are systematically studied through comparison with the Climate Prediction Center Unified Gauge daily precipitation Analysis (CPCUGA) over the Continental United States (CONUS) for 3 years from June 2008 to May 2011. The TMPA products include the version-6(V6) and version-7(V7) real-time products 3B42RT (3B42RTV6 and 3B42RTV7) and research products 3B42 (3B42V6 and 3B42V7). The evaluation shows that 3B42V7 improves upon 3B42V6 over the CONUS regarding 3 year mean daily precipitation: the correlation coefficient (CC) increases from 0.85 in 3B42V6 to 0.92 in 3B42V7; the relative bias (RB) decreases from −22.95% in 3B42V6 to −2.37% in 3B42V7; and the root mean square error (RMSE) decreases from 0.80 in 3B42V6 to 0.48 mm in 3B42V7. Distinct improvement is notable in the mountainous West especially along the coastal northwest mountainous areas, whereas 3B42V6 (also 3B42RTV6 and 3B42RTV7) largely underestimates: the CC increases from 0.86 in 3B42V6 to 0.89 in 3B42V7, and the RB decreases from −44.17% in 3B42V6 to −25.88% in 3B42V7. Over the CONUS, 3B42RTV7 gained a little improvement over 3B42RTV6 as RB varies from −4.06% in 3B42RTV6 to 0.22% in 3B42RTV7. But there is more overestimation with the RB increasing from 8.18% to 14.92% (0.16-3.22%) over the central US (eastern). [ABSTRACT FROM AUTHOR]

Aims Investigators of the COMBINE ( Combining Medications and Behavioral Interventions for Alcoholism) study examined whether combining medications with a behavioral intervention would improve outcomes over monotherapies. Unexpectedly, the combination did not offer any advantage over either treatment alone. This study aimed to explain the lack of incremental benefit offered by the combination over either monotherapy by assessing the role of cravings as a treatment mediator and moderator. Design Secondary mediation and moderation analyses of COMBINE study data. Setting Eleven United States academic sites. Participants A total of 863 patients randomized to one of four treatment groups: naltrexone (100 mg/day; n = 209), the combined behavioral intervention ( CBI, n = 236), naltrexone and CBI combined ( n = 213) and placebo naltrexone ( n = 205). Measurements Percentage of days abstinent ( PDA) measured between 13 and 16 weeks post-baseline. Cravings, the potential mediator/moderator, were measured at baseline, weeks 4 and 12 using the Obsessive- Compulsive Drinking Scale. Findings Compared with placebo, naltrexone, CBI and the combination all increased PDA by an additional 6-10 percentage points for those with high cravings ( P < 0.05 for all three treatment groups). None had significant effects on PDA for those with low cravings. The effects of all three treatments were mediated at least partially by cravings; craving reduction explained 48-53% of treatment effects ( P < 0.05 for all three treatment groups). Furthermore, naltrexone appeared to reduce cravings at 4 weeks, while CBI did not reduce cravings until 12 weeks. Conclusions The Combining Medications and Behavioral Interventions for Alcoholism ( COMBINE) naltrexone + CBI combination may not be more beneficial than either monotherapy because craving reduction is a common mechanism of both. [ABSTRACT FROM AUTHOR]

Objective Evaluate the accuracy of an algorithm at identifying ethnic minorities from administrative claims for enrollment into a clinical trial. Data Sources/Study Setting Claims data from a health benefits company. Study Design We compared results of a three-step algorithm to self-reported race/ethnicity. Data Collection/Extraction Methods Using the algorithm, we identified subjects with high probability of being minority and ascertained self-reported race/ethnicity. Principal Findings We identified 164 subjects as likely minority based on our algorithm. Of these, 94 completed the survey and 87 identified themselves as black or Hispanic. The positive predictive value of the algorithm was 93 percent (CI: 85-97). Conclusions Claims data can be used to efficiently identify minorities for participation in clinical trials. [ABSTRACT FROM AUTHOR]

The objective of this study was to develop European League Against Rheumatism/American College of Rheumatology classification criteria for polymyalgia rheumatica (PMR). Candidate criteria were evaluated in a 6-month prospective cohort study of 125 patients with new-onset PMR and 169 non-PMR comparison subjects with conditions mimicking PMR. A scoring algorithm was developed based on morning stiffness >45 minutes (2 points), hip pain/limited range of motion (1 point), absence of rheumatoid factor and/or anti-citrullinated protein antibody (2 points), and absence of peripheral joint pain (1 point). A score ≥4 had 68% sensitivity and 78% specificity for discriminating all comparison subjects from PMR. The specificity was higher (88%) for discriminating shoulder conditions from PMR and lower (65%) for discriminating RA from PMR. Adding ultrasound, a score ≥5 had increased sensitivity to 66% and specificity to 81%. According to these provisional classification criteria, patients ≥50 years old presenting with bilateral shoulder pain, not better explained by an alternative pathology, can be classified as having PMR in the presence of morning stiffness >45 minutes, elevated C-reactive protein and/or erythrocyte sedimentation rate, and new hip pain. These criteria are not meant for diagnostic purposes. [ABSTRACT FROM AUTHOR]

Two studies explored the validity of dichotomous classification of organised/disorganised serial killers and the four typologies (visionary, mission, hedonistic, and power/control) adopted by the Federal Bureau of Investigation (FBI). Tables documenting crime scene criteria were devised consisting of 50 typifying different crime scenes (Study 1) and 48 crime scenes (taken from the 50 crime scene criteria) with a further 10 motive-based crime scene criteria (Study 2). Adopting content analysis, crime scenes depicted for 40 (Study 1) and 40 (Study 2) serial killers using secondary sources of data were dichotomously coded for the presence or absence of the crime scene criteria. These data were inputted for agglomerative hierarchical cluster using Ward's method as the clustering algorithm. Differences in the nature of clusters were found for male and female serial killers; however, there was no empirical support for the organised/disorganised classifications or the four typologies. The 'bottom-up' approach resulted in clusters from the different crime scene criteria that helped to understand how these criteria were associated within the serial killer cohort considered. It is concluded that the resulting clusters in both studies support the notion of there being an underlying organised element to most serial murder and that serial murders differ according to the nature of disorganised crime scene criteria present. Copyright © 2011 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Environmental tobacco smoke (ETS) is a major contributor to indoor human exposures to fine particulate matter of 2.5 μm or smaller (PM). The Stochastic Human Exposure and Dose Simulation for Particulate Matter (SHEDS-PM) Model developed by the U.S. Environmental Protection Agency estimates distributions of outdoor and indoor PM exposure for a specified population based on ambient concentrations and indoor emissions sources. A critical assessment was conducted of the methodology and data used in SHEDS-PM for estimation of indoor exposure to ETS. For the residential microenvironment, SHEDS uses a mass-balance approach, which is comparable to best practices. The default inputs in SHEDS-PM were reviewed and more recent and extensive data sources were identified. Sensitivity analysis was used to determine which inputs should be prioritized for updating. Data regarding the proportion of smokers and 'other smokers' and cigarette emission rate were found to be important. SHEDS-PM does not currently account for in-vehicle ETS exposure; however, in-vehicle ETS-related PM levels can exceed those in residential microenvironments by a factor of 10 or more. Therefore, a mass-balance-based methodology for estimating in-vehicle ETS PM concentration is evaluated. Recommendations are made regarding updating of input data and algorithms related to ETS exposure in the SHEDS-PM model. Interindividual variability for ETS exposure was quantified. Geographic variability in ETS exposure was quantified based on the varying prevalence of smokers in five selected locations in the United States. [ABSTRACT FROM AUTHOR]

In this article, we estimate a model of oligopsony behavior under imperfect monitoring of rival actions to analyze weekly marketing margin data for the U.S. beef packing industry. Oligopsonists are hypothesized to follow a discontinuous pricing strategy in equilibrium, and we focus on shocks in the normal throughput of supply as a potential catalyst for regime switching between cooperative and noncooperative phases. We adopt an algorithm developed by that relies on multivariate first-order Markov process to test for the cooperative/noncooperative switching behavior. We find strong evidence that links switching conduct by packers to disruptions in coordinating the derived demands for processed beef with the supply of live cattle. Once switched, cooperative regimes lasted an average of 21 weeks, while noncooperative regimes averaged 33 weeks. The average marketing margin for processed beef was 68% lower in the noncooperative regimes compared to the cooperative regimes. This led to an annual average increase in profits of 408 million dollars to the beef packing industry and about an 8-9% reduction in live cattle prices. [ABSTRACT FROM AUTHOR]