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Introduction: This study presents a global research scenario in the broad domain of osteoarthritis (OA) research, using quantitative and qualitative publication and citation indicators. Methods: The study is based on 45,368 global publications, sourced from the Scopus bibliographical database, covering three decades (1994–2023). We studied the performance of the top 12 developed and top 12 developing countries. The key countries, organizations and authors at national and international levels were identified. The broad subject areas and key journals contributing to global OA research were delineated, besides identifying the broad characteristics of highly cited papers in the field. Results: The United States and China were the most productive countries, while the Netherlands and Canada made the largest citation impact. Harvard Medical School and the University of Sydney made the most contribution, while Boston University and Pfizer Inc., USA registered the highest citation impact. Hunter DJ and Guermazi A were the most productive authors, while Lohmander LS, and Hochberg MC registered the highest citation impact. Osteoarthritis and Cartilage (n = 4879) and Annals of the Rheumatic Diseases (n = 786) published the maximum papers, while Arthritis and Rheumatism and Nature Reviews Rheumatology registered the largest citation impact. The highly cited papers with 100 or more citations constituted 6.25% of the total publications. Conclusions: There has been a systematic growth of publications on OA. The research on OA was mainly done in developed countries, with the maximum publications coming from the United States of America, China and Canada. The most impactful publications on OA were from the Netherlands, Canada and the United States of America. [ABSTRACT FROM AUTHOR]

Breast cancer is one of the tumors with the highest prevalence rate among women in the world, and its BRCA1/2 gene is a common mutation site. Talazoparib, as a targeted PARP inhibitor, can effectively control the occurrence and development of breast cancer with BRCA1/2 gene mutation, and play a therapeutic role. Based on the findings from the Phase III EMBRACE trial (NCT01945775 clinical trial), our analysis reveals that the talazoparib group demonstrated a significant extension in progression-free survival, along with improved response markers and patient-reported outcomes when compared to conventional therapies. This study aims to assess the cost-effectiveness of talazoparib for treating advanced breast cancer with germline BRCA1/2 mutations and HER2 negativity, considering the perspectives of health services in China and the United States. The results obtained will serve as a valuable reference for promoting rational drug utilization and enhancing medical resource efficiency. To evaluate the cost-effectiveness of Talazoparib more scientifically and provide clinicians with chemotherapy options, this paper developed a Markov model based on the EMBRACA clinical trial (clinical Trails.gov No., NCT01945775) to simulate the survival events of breast cancer patients in the Talazoparib group and the standard treatment group. The state transition probability and clinical data of breast cancer patients during treatment were extracted from the phase III EMBRACA clinical trial. The cost data generated during the treatment process comes from local hospital pricing, other references, and expert consultation. This article uses US dollars to calculate the treatment cost and incremental cost-effectiveness ratio. Health outcomes are expressed in Quality Adjusted Life Years (QALYs). In addition, Outcomes were measured in quality-adjusted life-years (QALYs), and incremental cost-effectiveness ratio, which robustness was evaluated by deterministic and probabilistic sensitivity analyses. This article establishes a Markov model for single-item sensitivity analysis. The results show that the economic benefits of using Talazoparib as a new treatment strategy in both China and the United States are higher than other drugs, and it is cost-effective. Compared to the control group, the incremental cost incurred by the Talazoparib treatment group in China was $2484.48/QALY, with an incremental QALY of 1.5. However, Talazoparib in the United States holds a dominant position, saving costs of $10,223.43 and increasing QALYs by 1.5. The clinical treatment effect of Talazoparib group in BRCA1/2 mutant advanced breast cancer patients is better than that of the standard treatment group, and the progression free survival period is significantly prolonged. From the perspective of medical and health services in China and the United States, the Talazoparib group is more economical than the standard treatment group in treating patients with BRCA1/2 mutant advanced breast cancer. [ABSTRACT FROM AUTHOR]

Background: Diabetes is a prevalent chronic disease. Although self-care is the crucial element in managing diabetes, older Korean immigrants with diabetes face challenges in performing effective self-care related to vulnerability as minority immigrants. Purpose: This study measures sociodemographics, self-efficacy, social support, diabetes knowledge, and diabetes self-care activities among older Korean immigrants in the United States. This study also aims to demonstrate the direct and indirect effects of the related factors on diabetes self-care activities using a path analysis. Methods: This study uses a cross-sectional design. Convenience sampling targeted Korean immigrants aged 55 or older using paper and online surveys. Four instruments were used to measure variables: self-efficacy was measured by the General Self-Efficacy scale, diabetes knowledge by the Simplified Diabetes Knowledge Test, social support by the Lubben Social Network Scale-6, and diabetes self-care by the Summary of Diabetes Self-Care Activities questionnaire. Using path analysis, the effects of related factors on self-care activities were analyzed. Results: 190 older Korean immigrants participated, 53.2% female, and 46.8% male. The mean age was 67.2 (SD = 9.9; range, 58–93). A path model shows that sociodemographics (sex, age, education, and years in the United States), diabetes knowledge, self-efficacy, and family support predict diabetes self-care. Conclusions: The path model demonstrates the effects of sociodemographics, self-efficacy, diabetes knowledge, and social support on diabetes self-care among older Korean immigrants. The findings can help to understand diabetes self-care among the minority ethnic older group and can be used to develop culturally tailored education, counseling, and healthcare services. [ABSTRACT FROM AUTHOR]

This paper investigates the sensitivity of golfers' performance to meteorological conditions at the men's US Masters tournament over the 40-year period 1980–2019. The mean and standard deviation of round scores are related to local temperature, humidity, wind speed and direction, and concurrent and antecedent precipitation. Mean scores are more dependent on weather conditions than the variability of scores in a given round. The best predictor of mean scores is the wet-bulb temperature in rounds one and two, and the zonal wind speed in rounds three and four. Across both sets of rounds (1 and 2, and 3 and 4), the wet-bulb temperature is a better predictor of mean scores than the air temperature, which implies that atmospheric moisture content affects scores. In general, golfers take fewer shots and so perform better in warmer and calmer conditions. The synergestic effect of several weather variables explains over 44% of the variance in mean scores. Mean meteorological conditions during play are a much better predictor of the players' average performance than the standard deviation of the weather variables. The golfers' performance becomes more variable in cooler conditions with a wider range of scores. Precipitation during play and the dampness of the ground (as quantified by rainfall up to ten days before play) do not have a consistent and statistically significant effect on the competitors' performance. In short, this paper demonstrates that golf scores are dependent on weather conditions. [ABSTRACT FROM AUTHOR]

Introduction: The emergence of artificial intelligence (AI) chat programs has opened two distinct paths, one enhancing interaction and another potentially replacing personal understanding. Ethical and legal concerns arise due to the rapid development of these programs. This paper investigates academic discussions on AI in medicine, analyzing the context, frequency, and reasons behind these conversations. Methods: The study collected data from the Web of Science database on articles containing the keyword "ChatGPT" published from January to September 2023, resulting in 786 medically related journal articles. The inclusion criteria were peer-reviewed articles in English related to medicine. Results: The United States led in publications (38.1%), followed by India (15.5%) and China (7.0%). Keywords such as "patient" (16.7%), "research" (12%), and "performance" (10.6%) were prevalent. The Cureus Journal of Medical Science (11.8%) had the most publications, followed by the Annals of Biomedical Engineering (8.3%). August 2023 had the highest number of publications (29.3%), with significant growth between February to March and April to May. Medical General Internal (21.0%) was the most common category, followed by Surgery (15.4%) and Radiology (7.9%). Discussion: The prominence of India in ChatGPT research, despite lower research funding, indicates the platform's popularity and highlights the importance of monitoring its use for potential medical misinformation. China's interest in ChatGPT research suggests a focus on Natural Language Processing (NLP) AI applications, despite public bans on the platform. Cureus' success in publishing ChatGPT articles can be attributed to its open-access, rapid publication model. The study identifies research trends in plastic surgery, radiology, and obstetric gynecology, emphasizing the need for ethical considerations and reliability assessments in the application of ChatGPT in medical practice. Conclusion: ChatGPT's presence in medical literature is growing rapidly across various specialties, but concerns related to safety, privacy, and accuracy persist. More research is needed to assess its suitability for patient care and implications for non-medical use. Skepticism and thorough review of research are essential, as current studies may face retraction as more information emerges. [ABSTRACT FROM AUTHOR]

This paper investigates the homogeneity of United States aircraft reconnaissance data and the impact of these data on the homogeneity of the tropical cyclone (TC) best track data for the seasons 1949–1987 generated by the China Meteorological Administration (CMA). The evaluation of the reconnaissance data shows that the minimum central sea level pressure (MCP) data are relatively homogeneous, whereas the maximum sustained wind (MSW) data show both overestimations and spurious abrupt changes. Statistical comparisons suggest that both the reconnaissance MCP and MSW were well incorporated into the CMA TC best track dataset. Although no spurious abrupt changes were evident in the reconnaissance-related best track MCP data, two spurious changepoints were identified in the remainder of the best-track MCP data. Furthermore, the influence of the reconnaissance MSWs seems to extend to the best track MSWs unrelated to reconnaissance, which might reflect the optimistic confidence in making higher estimates due to the overestimated extreme wind "observations". In addition, the overestimation of either the reconnaissance MSWs or the best track MSWs was greater during the early decades compared to later decades, which reflects the important influence of reconnaissance data on the CMA TC best track dataset. The wind–pressure relationship (WPR) used in the CMA TC best track dataset is also evaluated and is found to overestimate the MSW, which may lead to inhomogeneity within the dataset between the aircraft reconnaissance era and the satellite era. [ABSTRACT FROM AUTHOR]

In most of the United States, insurance companies may use gender to determine car insurance rates. In addition, several studies have shown that women over the age of 25 generally pay more than men for car insurance. Then, we investigate whether the distributions of claims for women and men differ in location, scale and shape by means of the GAMLSS regression framework, using microdata provided by U.S. and Australian insurance companies, to use this evidence to support policy makers' decisions. We also develop a parametric-bootstrap test to investigate the tail behavior of the distributions. When covariates are not considered, the distribution of claims does not appear to differ by gender. When covariates are included, the regressions provide mixed evidence for the location parameter. However, for female claimants, the spread of the distribution is lower. Our research suggests that, at least for the contexts analyzed, there is no clear statistical reason for charging higher rates to women. While providing evidence to support unisex insurance pricing policies, given the limitations represented by the use of country-specific data, this paper aims to promote further research on this topic with different datasets to corroborate our findings and draw more general conclusions. [ABSTRACT FROM AUTHOR]

This present paper aims to examine various epidemiological aspects of the monkeypox viral infection using a fractional-order mathematical model. Initially, the model is formulated using integer-order nonlinear differential equations. The imperfect vaccination is considered for human population in the model formulation. The proposed model is then reformulated using a fractional order derivative with power law to gain a deeper understanding of disease dynamics. The values of the model parameters are determined from the cumulative reported monkeypox cases in the United States during the period from May 10th to October 10th, 2022. Besides this, some of the demographic parameters are evaluated from the population of the literature. We establish sufficient conditions to ensure the existence and uniqueness of the model's solution in the fractional case. Furthermore, the stability of the endemic equilibrium of the fractional monkeypox model is presented. The Lyapunov function approach is used to demonstrate the global stability of the model equilibria. Moreover, the fractional order model is numerically solved using an efficient numerical technique known as the fractional Adams-Bashforth-Moulton method. The numerical simulations are conducted using estimated parameters, considering various values of the fractional order of the Caputo derivative. The finding of this study reveals the impact of various model parameters and fractional order values on the dynamics and control of monkeypox. [ABSTRACT FROM AUTHOR]

An assessment of the likelihood of use and abuse potential for new tobacco products is an important part of tobacco product regulation in the United States and abroad. This paper reports the results of a randomized, open-label, crossover clinical study that assessed factors related to product adoption and abuse liability (AL), comparing two closed electronic nicotine delivery system (ENDS) products to combustible cigarettes and nicotine gum, high- and low-AL comparator products, respectively. During an 11-day confinement period that included multiple product familiarization sessions, healthy adult smokers participated in AL test sessions to evaluate the abuse liability of each product. During these test sessions, changes in subjective measures; speed and amount of nicotine uptake; and maximum changes in physiological effects before, during, and after use of each assigned product were assessed over 4 h. Positive subjective effects measures scores such as product-liking and overall intent to use again were highest for cigarettes, followed by the Vuse ENDS, with nicotine gum consistently having the lowest scores. The PK results (Cmax and Tmax) of the Vuse ENDS products are between UB cigarettes and nicotine gum, which correlates with the subjective effects. All nicotine uptake measures for the Vuse ENDS products were lower than that of usual brand (UB) cigarettes, including peak nicotine uptake and overall nicotine uptake, and were either similar to or lower than nicotine gum. The time course of nicotine uptake after use of the ENDS was more similar to that of combustible cigarettes than nicotine gum. The results indicate that the AL of each ENDS product is lower than that of UB cigarettes and similar to that of nicotine gum. [ABSTRACT FROM AUTHOR]

In the United States, state-funded preschool is a critical component of both K-12 public education and the early childhood education and care system. In 2021, 44 states and the District of Columbia operated 63 school/center-based preschool programs serving over 1.3 million children. The vast majority of state-funded preschool programs require two adults in each classroom: typically, a lead teacher and an assistant teacher, with an expectation for team teaching. This paper examines the trends and variations in state policies that address the qualifications requirements, compensation, and professional development supports for preschool lead and assistant teachers in state-funded preschool programs that provide a foundation for supporting a culture of collective success and potential quality of the classroom. [ABSTRACT FROM AUTHOR]

This present paper aims to examine various epidemiological aspects of the monkeypox viral infection using a fractional-order mathematical model. Initially, the model is formulated using integer-order nonlinear differential equations. The imperfect vaccination is considered for human population in the model formulation. The proposed model is then reformulated using a fractional order derivative with power law to gain a deeper understanding of disease dynamics. The values of the model parameters are determined from the cumulative reported monkeypox cases in the United States during the period from May 10th to October 10th, 2022. Besides this, some of the demographic parameters are evaluated from the population of the literature. We establish sufficient conditions to ensure the existence and uniqueness of the model's solution in the fractional case. Furthermore, the stability of the endemic equilibrium of the fractional monkeypox model is presented. The Lyapunov function approach is used to demonstrate the global stability of the model equilibria. Moreover, the fractional order model is numerically solved using an efficient numerical technique known as the fractional Adams-Bashforth-Moulton method. The numerical simulations are conducted using estimated parameters, considering various values of the fractional order of the Caputo derivative. The finding of this study reveals the impact of various model parameters and fractional order values on the dynamics and control of monkeypox. [ABSTRACT FROM AUTHOR]

COVID-19 has dramatically changed people's mobility geste patterns and affected the operations of different functional spots. In the environment of the successful reopening of countries around the world since 2022, it's pivotal to understand whether the reopening of different types of locales poses a threat of wide epidemic transmission. In this paper, by establishing an epidemiological model based on mobile network data, combining the data handed by the Safegraph website, and taking into account the crowd inflow characteristics and the changes of susceptible and latent populations, the trends of the number of crowd visits and the number of epidemic infections at different functional points of interest after the perpetration of continuing strategies were simulated. The model was also validated with daily new cases in ten metropolitan areas in the United States from March to May 2020, and the results showed that the model fitted the evolutionary trend of realistic data more accurately. Further, the points of interest were classified into risk levels, and the corresponding reopening minimum standard prevention and control measures were proposed to be implemented according to different risk levels. The results showed that restaurants and gyms became high-risk points of interest after the perpetration of the continuing strategy, especially the general dine-in restaurants were at higher risk levels. Religious exertion centers were the points of interest with the loftiest average infection rates after the perpetration of the continuing strategy. Points of interest such as convenience stores, large shopping malls, and pharmacies were at a lower risk for outbreak impact after the continuing strategy was enforced. Based on this, continuing forestallment and control strategies for different functional points of interest are proposed to provide decision support for the development of precise forestallment and control measures for different spots. [ABSTRACT FROM AUTHOR]

The prolonged COVID-19 pandemic has tied up significant medical resources, and its management poses a challenge for the public health care decision making. Accurate predictions of the hospitalizations are crucial for the decision makers to make informed decision for the medical resource allocation. This paper proposes a method named County Augmented Transformer (CAT). To generate accurate predictions of four-week-ahead COVID-19 related hospitalizations for every states in the United States. Inspired by the modern deep learning techniques, our method is based on a self-attention model (known as the transformer model) that is actively used in Natural Language Processing. Our transformer based model can capture both short-term and long-term dependencies within the time series while enjoying computational efficiency. Our model is a data based approach that utilizes the publicly available information including the COVID-19 related number of confirmed cases, deaths, hospitalizations data, and the household median income data. Our numerical experiments demonstrate the strength and the usability of our model as a potential tool for assisting the medical resources allocation. [ABSTRACT FROM AUTHOR]

Background: Tremendous progress has been made in the field of cartilage repair and regeneration, particularly with tissue-engineering approaches. This study aims to estimate the global status and current trends in the field of cartilage tissue engineering. Methods: Publications from 2011 to 2020 on tissue engineering for cartilage repair and regeneration were retrieved from Web of Science Core Collection database. The source data were statistically evaluated based on the bibliometrics. In terms of visualized analysis, some bibliometric indicators such as bibliographic coupling, co-citation, co-authorship and co-occurrence analysis were performed by VOSviewer software, to investigate the research trends in tissue engineering for cartilage repair and regeneration. Results: In total, 3715 papers were included. Since 2011, the amount of issued papers and relative research interest (RRI) have grown by leaps and bounds globally. The United States was the biggest contributor to the research in this field, due to the greatest citation frequency, the highest H index and the strongest total link strength. Romania had the highest average citation for each. The journal Tissue Engineering Part A published most articles in this field. For institutions, the largest contributors were Shanghai Jiaotong University, University of California System and Sichuan University. Studies could all be grouped into four main clusters: study of biomaterial scaffolds, study of seeding cells and growth factors, experimental animal model and clinical study, and mechanism research. Conclusion: Great efforts should be put into the study of biomaterial scaffolds, seeding cells and growth factors, considered to be the next hot topics in cartilage tissue engineering. This findings provide collaborative insights and research orientation for academic researchers, surgeons and healthcare practitioners to a certain extent. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic is a global health concern that has spread around the globe. Machine Learning is promising in the fight against the COVID-19 pandemic. Machine learning and artificial intelligence have been employed by various healthcare providers, scientists, and clinicians in medical industries in the fight against COVID-19 disease. In this paper, we discuss the impact of the Covid-19 pandemic on alcohol consumption habit changes among healthcare workers in the United States during the first wave of the Covid-19 pandemic. We utilize multiple supervised and unsupervised machine learning methods and models such as decision trees, logistic regression, support vector machines, multilayer perceptron, XGBoost, CatBoost, LightGBM, AdaBoost, Chi-Squared Test, mutual information, KModes clustering and the synthetic minority oversampling technique on a mental health survey data obtained from the University of Michigan Inter-University Consortium for Political and Social Research to investigate the links between COVID-19-related deleterious effects and changes in alcohol consumption habits among healthcare workers. Through the interpretation of the supervised and unsupervised methods, we have concluded that healthcare workers whose children stayed home during the first wave in the US consumed more alcohol. We also found that the work schedule changes due to the Covid-19 pandemic led to a change in alcohol use habits. Changes in food consumption, age, gender, geographical characteristics, changes in sleep habits, the amount of news consumption, and screen time are also important predictors of an increase in alcohol use among healthcare workers in the United States. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic is a global health concern that has spread around the globe. Machine Learning is promising in the fight against the COVID-19 pandemic. Machine learning and artificial intelligence have been employed by various healthcare providers, scientists, and clinicians in medical industries in the fight against COVID-19 disease. In this paper, we discuss the impact of the Covid-19 pandemic on alcohol consumption habit changes among healthcare workers in the United States during the first wave of the Covid-19 pandemic. We utilize multiple supervised and unsupervised machine learning methods and models such as decision trees, logistic regression, support vector machines, multilayer perceptron, XGBoost, CatBoost, LightGBM, AdaBoost, Chi-Squared Test, mutual information, KModes clustering and the synthetic minority oversampling technique on a mental health survey data obtained from the University of Michigan Inter-University Consortium for Political and Social Research to investigate the links between COVID-19-related deleterious effects and changes in alcohol consumption habits among healthcare workers. Through the interpretation of the supervised and unsupervised methods, we have concluded that healthcare workers whose children stayed home during the first wave in the US consumed more alcohol. We also found that the work schedule changes due to the Covid-19 pandemic led to a change in alcohol use habits. Changes in food consumption, age, gender, geographical characteristics, changes in sleep habits, the amount of news consumption, and screen time are also important predictors of an increase in alcohol use among healthcare workers in the United States. [ABSTRACT FROM AUTHOR]

In the present paper we generalise the classical newsvendor problem for critical perishable commodities having more severe costs than its linear alternative. Piece wise polynomial cost functions are introduced to accommodate the excess severity. Stochastic demand is assumed to follow a completely unknown probability distribution. Non parametric estimator of the optimal order quantity has been developed from an estimating equation using a random sample. Strong consistency of the estimator is proved for unique optimal order quantity and the result is extended for multiple solutions. Simulation results indicate that non parametric estimator is efficient in terms of mean square error. Real life application of the proposed non-parametric estimator has been demonstrated with Avocado demand in the United States of America and Covid-19 test kit demand during second wave of SARS-COV2 pandemic across 86 countries. [ABSTRACT FROM AUTHOR]

In recent decades, data-driven approaches have been developed to analyze demographic and economic surveys on a large scale. Despite advances in multivariate techniques and learning methods, in practice the analysis and interpretations are often focused on a small portion of available data and limited to a single perspective. This paper aims to utilize a selected array of multivariate statistical learning methods in the analysis of income and expenditure patterns of households in the United States using the Public-Use Microdata from the Bureau of Labor Statistics Consumer Expenditure Survey (CE). The objective is to propose an effective data pipeline that provides visualizations and comprehensive interpretations for applications in governmental regulations and economic research, using thirty-five original survey variables covering the categories of demographics, income and expenditure. Details on feature extraction not only showcase CE as a unique publicly-shared big data resource with high potential for in-depth analysis, but also assist interested researchers with pre-processing. Challenges from missing values and categorical variables are treated in the exploratory analysis, while statistical learning methods are comprehensively employed to address multiple economic perspectives. Principal component analysis suggests that after-tax income, wage/salary income, and the quarterly expenditure in food, housing and overall as the five most important of the selected variables, while cluster analysis identifies and visualizes the implicit structure between variables. Based on this, canonical correlation analysis reveals high correlation between two selected groups of variables, one of income and the other of expenditure. [ABSTRACT FROM AUTHOR]

Purpose: The SART CORS database is an informative source of IVF clinic-specific linked data that provides cumulative live birth rates from medically assisted reproduction in the United States (US). These data are used to develop best practice guidelines, for research, quality assurance, and post-market surveillance of assisted reproductive technologies. Here, we sought to investigate the key areas of current research focus (higher-order categories), discover gaps or underserved areas of ART research, and examine the potential application and impact of newer ART adjuvants, future data collection, and analysis needs. Methods: We conducted a systematic review (PRISMA guidelines) to quantify unique output metrics of the SART CORS database. Included were SART member reporting clinics: full-length publications from 2004 to 2021 and conference abstracts from 2015 to 2021, the two key timepoints when the SART CORS database underwent transformative shifts in data collection. Results: We found 206 abstracts presented from 2015 to 2021, 189 full-length peer-reviewed publications since 2004, with 654 unique authors listed on these publications. A total of 19 publications have been highly impactful, garnering over 100 citations at the time of writing. Several higher-order categories, such as endometriosis and tubal infertility, have few publications. The conversion of conference abstracts to full-length papers ranged from 15 to 35% from 2015 to 2021. Conclusions: A substantial body of literature has been generated by analyzing the SART CORS database. Full-length publications have increased year over year. Some topic areas, such as endometriosis and tubal infertility, may be underrepresented. Conversion of conference abstracts to full-length publications has been low, indicating that more organizational support may be needed to ensure that research is methodologically sound and researchers supported to reach full publication status. [ABSTRACT FROM AUTHOR]

Background: Amyotrophic lateral sclerosis (ALS) is a progressive and fatal neuromuscular disease with no curative therapies. Edaravone (Radicava®) (Mitsubishi Tanabe Pharma Corporation, Tokyo, Japan), approved in the United States (US) for ALS in adults in 2017, was shown in a clinical trial to slow the rate of physical functional decline in ALS and is administered intravenously. The aim of this paper is to summarize the observed safety profile from real-world patient use during the first 3 years of edaravone availability in the US. Methods: Edaravone usage data were collected, and adverse events (AEs) were identified from a postmarketing safety database from August 8, 2017 through August 7, 2020 (cutoff date). Results: As of October 3, 2020, 5207 ALS patients had been treated with edaravone. As of August 7, 2020, the most commonly reported AEs included death (not specified), drug ineffective, disease progression, therapeutic response unexpected, fall, asthenia, fatigue, muscular weakness, gait disturbance, and dyspnea. The most commonly reported serious AEs (SAEs) included death (not specified), pneumonia, disease progression, ALS, fall, dyspnea, respiratory failure, device-related infection, hospitalization, and injection-site infection. There were 687 deaths, with 494 reported as death without specifying the cause. Deaths were most commonly attributed to ALS, disease progression, respiratory failure, or pneumonia. Review for administration-site reactions revealed 95 AEs, including 34 site infections, with 22 SAEs (all non-fatal). Five non-fatal SAEs of anaphylaxis were reported. Conclusion: In the postmarketing reporting to date, no new safety signals were identified beyond those already known from the edaravone clinical trial program. [ABSTRACT FROM AUTHOR]

As the COVID-19 spread over the globe and new variants of COVID-19 keep occurring, reliable real-time forecasts of COVID-19 hospitalizations are critical for public health decisions on medical resources allocations. This paper aims to forecast future 2 weeks national and state-level COVID-19 new hospital admissions in the United States. Our method is inspired by the strong association between public search behavior and hospitalization admissions and is extended from a previously-proposed influenza tracking model, AutoRegression with GOogle search data (ARGO). Our LASSO-penalized linear regression method efficiently combines Google search information and COVID-19 related time series information with dynamic training and rolling window prediction. Compared to other publicly available models collected from COVID-19 forecast hub, our method achieves substantial error reduction in a retrospective out-of-sample evaluation from Jan 4, 2021, to Dec 27, 2021. Overall, we showed that our method is flexible, self-correcting, robust, accurate, and interpretable, making it a potentially powerful tool to assist healthcare officials and decision making for the current and future infectious disease outbreaks. [ABSTRACT FROM AUTHOR]

Though preventable, children with disabilities have a high risk of victimization, contributing to worsening health conditions. Hence, this study examined the exposure of school-age children with mental, emotional, developmental, or behavioral (MEDB) disorder to bully victimization. This study used the 2018 National Survey of Children's Health (NSCH) data of 23,494 children ages 5–17 to estimate multilevel logistic regression with fixed and random effects. Children's health conditions were treated as level one variables, while family poverty level and neighborhood characteristics such as vandalism and presence/absence of recreational centers were treated as level two variables. The paper presents the prevalence of bullying victimization among children with at least one disorder (MDBB = 39.5%), anxiety (20.6%), depression (10.8%), ADD/ADHD (18.3%), behavioral problems (14.9%), learning disability (11.9%), Tourette syndrome (0.5%), developmental delay (10.1%), Autism spectrum disorder (4.6%), speech disorder (10.7), and intellectual disability (1.6%), respectively. Bullying victimization was positively associated with anxiety (AOR = 1.995, 95% CI = 1.634–2.436), depression (AOR = 2.688, 95% CI = 2.031–3.557), developmental delay (AOR = 1.804, 95% CI = 1.422–2.288), but inversely associated with Autism spectrum disorder (AOR = 0.614, 95% CI = 0.399–0.946). Neighborhood disorganization and poverty were also associated with bullying victimization. The NSCH data suggests that children with disabilities in the US had a higher prevalence rate of victimization. Consequently, effective bullying prevention strategies that can protect and improve children's quality of life with special needs should be prioritized. [ABSTRACT FROM AUTHOR]

Synthetic organic colourants are extremely prominent in heritage collections, particularly throughout textiles. They are often generalized as highly light-sensitive; although, a broad distribution of lightfastness exists. This is evident in various fastness ratings published in late nineteenth century literature, the work of Schultz and Julius, and the Colour Index. In heritage conservation, much of the research related to light-sensitivity of dyed textiles has focused on natural colourants. This is likely due to a general interest in the dyes present on older objects, and the overwhelming selection of modern synthetic materials. To address this gap, a shortlist of targeted synthetic dyes was recently developed using census data for dye production and imports in the United States, and information gathered from the Colour Index. Our present work provides a follow up to the prior literature review, where a subset of samples from the published target list was used to evaluate lightfastness. To begin the study, a collection of dyed textiles from the last quarter of the nineteenth century was gathered from trade publications of the period, and those matching the target criteria were reserved for analysis. The lightfastness of more than 100 early synthetic dyes was then investigated using the historic materials. Test specimens were illuminated with a custom-built fadometer containing an LED light source that is characteristic of modern gallery lighting. Four batches of samples were exposed at 20 klx for approximately 6 months each, and diffuse reflectance was periodically measured with a portable spectrophotometer over white and black backgrounds. Measurements were taken at increasing light dose values to reach at least 85 Mlx·h per batch. Visible reflectance spectra were recorded in a dataset to document the colours as a function of light exposure, and foster future colorimetry research. Colour change was also calculated using the CIEDE2000 formula to assess lightfastness, and results were summarized as a resource to further develop risk analysis tools for exhibit lighting. In this paper, a detailed description of the test apparatus is presented with an overview of the lightfastness results and the resulting dataset. Comparisons are given with nineteenth century lightfastness data, and ISO values available in the Colour Index. It is hoped that the associated dataset will provide a foundation to be expanded over time with interest in further materials and test methods. [ABSTRACT FROM AUTHOR]

Thousands of people are reported lost in the wilderness in the United States every year and locating these missing individuals as rapidly as possible depends on coordinated search and rescue (SAR) operations. As time passes, the search area grows, survival rate decreases, and searchers are faced with an increasingly daunting task of searching large areas in a short amount of time. To optimize the search process, mathematical models of lost person behavior with respect to landscape can be used in conjunction with current SAR practices. In this paper, we introduce an agent-based model of lost person behavior which allows agents to move on known landscapes with behavior defined as independent realizations of a random variable. The behavior random variable selects from a distribution of six known lost person reorientation strategies to simulate the agent's trajectory. We systematically simulate a range of possible behavior distributions and find a best-fit behavioral profile for a hiker with the International Search and Rescue Incident Database. We validate these results with a leave-one-out analysis. This work represents the first time-discrete model of lost person dynamics validated with data from real SAR incidents and has the potential to improve current methods for wilderness SAR. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to determine the impact of rare disaster risks, captured by the El Niño-Southern Oscillation (ENSO) cycle, on the volatility of Treasury securities of the United States (US) involving 1- to 360-month maturities. We use a random coefficient panel-data-based heterogeneous autoregressive-realized variance (HAR-RV) model over the monthly period of 1961:06 to 2019:12, with the monthly RV derived from the sum of squared daily changes in yield within a month. Our results show a positive and statistically significant (at the 1% level) impact of the ENSO cycle on RV, with the results being robust to alternative metrics of the ENSO, consideration of lagged values, and decomposition of the ENSO cycle into El Niño and La Niña phases, with the former having a relatively stronger effect. Based on the panel estimation method using heterogeneous slope coefficients, we find that the impact on the entire term-structure is positive yet stronger at the two-ends and the middle-part of the term-structure. Our findings have important implications for investors in US Treasury securities. [ABSTRACT FROM AUTHOR]

Algorithmic vision, the computational process of making meaning from digital images or visual information, has changed the relationship between the image and the human subject. In this paper, I explicate on the role of algorithmic vision as a technique of algorithmic governance, the organization of a population by algorithmic means. With its roots in the United States post-war cybernetic sciences, the ontological status of the computational image undergoes a shift, giving way to the hegemonic use of automated facial recognition technologies towards predatory policing and profiling practices. By way of example, I argue that algorithmic vision reconfigures the philosophical links between vision, image, and truth, paradigmatically changing the way a human subject is represented through imagistic data. With algorithmic vision, the relationship between subject and representation challenges the humanistic discourse around images, calling for a critical displacement of the human subject from the center of an analysis of how computational images make meaning. I will explore the relationship between the operative image, the image that acts but is not seen by human eyes, and what Louise Amoore calls an "emergent subject," a subject that is made visible through algorithmic techniques (2013). Algorithmic vision reveals subjects to power in a mode that requires a new approach towards analyzing the entanglement and invisiblization of the human in automated decision-making systems. [ABSTRACT FROM AUTHOR]

This paper extends the understanding of the relation between ADHD and reading comprehension, through examining how this relation differs depending on the quantile an individual falls in for each. Samples from three twin projects around the United States were used (Florida Twin Project, Colorado component of International Longitudinal Twin Study of Early Reading Development, & Western Reserve Reading and Math Projects). Phenotypic analysis using quantile regression showed relations between ADHD related behaviors and reading comprehension to be stronger in the lower quantiles of reading comprehension in two of three samples. A new method was developed extending this analysis into the bivariate genetic space. Results of this quantile genetic analysis revealed that overlapping common environmental influences accounted for a larger proportion of variance in the lower quantiles of these variables in two of three samples. Finally, in all three samples the phenotypic relation was strongest when shared environmental influences accounted for a larger proportion of the overall variance. [ABSTRACT FROM AUTHOR]

Millions commute to work every day in cities and interact with colleagues, partners, friends, and strangers. Commuting facilitates the mixing of people from distant and diverse neighborhoods, but whether this has an imprint on social inclusion or instead, connections remain assortative is less explored. In this paper, we aim to better understand income sorting in social networks inside cities and investigate how commuting distance conditions the online social ties of Twitter users in the 50 largest metropolitan areas of the United States. An above-median commuting distance in cities is linked to more diverse individual networks, moreover, we find that longer commutes are associated with a nearly uniform, moderate reduction of overall social tie assortativity across all cities. This suggests a universal relation between long-distance commutes and the integration of social networks. Our results inform policy that facilitating access across distant neighborhoods can advance the social inclusion of low-income groups. [ABSTRACT FROM AUTHOR]

The sustainability of large dams has been questioned on several grounds. One aspect that has been less explored is that the development of dams and reservoirs often enables agricultural expansion and urban growth, which in turn increase water consumption. As such, dam development influences, while being influenced by, the spatial and temporal distribution of both supply and demand of water resources. In this paper, we explore the interplay between large dams, patterns of population growth and agricultural expansion in the United States over the past two centuries. Based on a large-scale analysis of spatial and temporal trends, we identify three distinct phases, in which different processes dominated the interplay. Then, we focus on agricultural water use in the Southwest region (Arizona, California and Nevada) and explore chicken-and-egg dynamics where water supply partly meets and partly fuels water demand. Lastly, we show that the legacy of dams in the United States consists of a lock-in condition characterized by high levels of water consumption, especially in the Southwest, which leads to severe water crises and groundwater overexploitation when droughts occur. [ABSTRACT FROM AUTHOR]

Containing the COVID-19 pandemic while balancing the economy has proven to be quite a challenge for the world. We still have limited understanding of which combination of policies have been most effective in flattening the curve; given the challenges of the dynamic and evolving nature of the pandemic, lack of quality data etc. This paper introduces a novel data mining-based approach to understand the effects of different non-pharmaceutical interventions in containing the COVID-19 infection rate. We used the association rule mining approach to perform descriptive data mining on publicly available data for 50 states in the United States to understand the similarity and differences among various policies and underlying conditions that led to transitions between different infection growth curve phases. We used a multi-peak logistic growth model to label the different phases of infection growth curve. The common trends in the data were analyzed with respect to lockdowns, face mask mandates, mobility, and infection growth. We observed that face mask mandates combined with mobility reduction through moderate stay-at-home orders were most effective in reducing the number of COVID-19 cases across various states. [ABSTRACT FROM AUTHOR]

The COVID-19 global pandemic has significantly impacted every aspect of life all over the world. The United States is reported to have suffered more than 20% of the global casualties from this pandemic. It is imperative to investigate the growth dynamics of the disease in the US based on varying geographical and governmental factors that best manifest itself in each State of the Country. This paper utilizes a hybrid machine learning and continuum deformation-based approach for analyzing the stability and growth rate of the pandemic. To this end, principal stress values of the pandemic continuum body are obtained using Mohr's Circle method and overlapping, moving windows of data are analysed successively. This helps in finding the correlations between the growth rate and Governments' action/Public's reaction. Government actions include "state of emergency", "shelter at place", and "phase declarations". We also consider the vaccination rate milestones, which shows us the coordinated Governments' action/Public's reaction. Finally, a number of recommendations are made to the Governments and people for better management of future pandemics. [ABSTRACT FROM AUTHOR]

In this paper, we proposed a multi-method modeling approach to community-level spreading of COVID-19 disease. Our methodology was composed of interconnected age-stratified system dynamics models in an agent-based modeling framework that allowed for a granular examination of the scale and severity of disease spread, including metrics such as infection cases, deaths, hospitalizations, and ICU usage. Model parameters were calibrated using an optimization technique with an objective function to minimize error associated with the cumulative cases of COVID-19 during a training period between March 15 and October 31, 2020. We outlined several case studies to demonstrate the model's state- and local-level projection capabilities. We further demonstrated how model outcomes could be used to evaluate perceived levels of COVID-19 risk across different localities using a multi-criteria decision analysis framework. The model's two, three, and four week out-of-sample projection errors varied on a state-by-state basis, and generally increased as the out-of-sample projection period was extended. Additionally, the prediction error in the state-level projections was generally due to an underestimation of cases and an overestimation of deaths. The proposed modeling approach can be used as a virtual laboratory to investigate a wide range of what-if scenarios and easily adapted to future high-consequence public health threats. [ABSTRACT FROM AUTHOR]

Background: Cannabis is the most prevalent illegal drug used and the second most common cause of ED drug-related complaints in the USA. Recently, newer more potent strains, concentrated THC products, and consumption methods have become available. Objective: Our first objective was to define cannabis use in the USA and provide a summary background on its current preparations, pharmacokinetics, vital sign and physical exam findings, adverse effects, and laboratory testing. Our second objective, using the aforementioned summary as relevant background information, was to present and summarize the care and treatment of the most commonly reported cannabis-related topics relevant to ED physicians. Methods: We first performed an extensive literature search of peer-reviewed publications using New PubMed and Cochrane Central Register of Controlled Trials to identify the most commonly reported cannabis-related topics in emergency care. Once the six topic areas were identified, we undertook an extensive narrative literature review for each section of this paper using New PubMed and Cochrane Central Register of Controlled Trials from the inception of the databases to September 30, 2020. Results: The six subject areas that were most frequently reported in the medical literature relevant to cannabis-related ED care were acute intoxication/overdose, pediatric exposure, cannabinoid hyperemesis syndrome, cannabis withdrawal, e-cigarette or vaping product use-associated lung injury (EVALI), and synthetic cannabinoids. Conclusion: As cannabis becomes more widely available with the adoption of state medical cannabis laws, ED-related cannabis visits will likely rise. While cannabis has historically been considered a relatively safe drug, increased legal and illegal access to newer formulations of higher potency products and consumption methods have altered the management and approach to ED patient care and forced physicians to become more vigilant about recognizing and treating some new cannabis-related life-threatening conditions. [ABSTRACT FROM AUTHOR]

This perspective describes federal efforts in the United States (U.S.) to integrate care for an especially complex, vulnerable, and costly patient population: adults eligible for both Medicare and Medicaid insurance. The goal of the paper is to demystify for clinical policy leaders and practicing clinicians the origins and evolution of the Dual-Eligible Special Needs Plans (D-SNPs) recently permanently authorized by the U.S. Congress and to explore the potential for these policy changes to help such health plans improve care for the sickest and most vulnerable Americans. [ABSTRACT FROM AUTHOR]

This paper studies the dynamic interactions of the ethanol, crude oil and corn market prices in the United States from January 2005 up to December 2014. The empirical analysis, for the shake of robustness, employs alternative time series methodologies based on single equation and system estimation approaches to cointegration and more specifically, the autoregressive distributed lags and the Johansen ML cointegration methodologies were applied complementary. The findings reveal a statistically significant long-run causal effect running from crude oil and corn prices to ethanol price as well as from ethanol and corn prices to crude oil price. Additionally, a positive relationship between crude oil price and ethanol price was revealed. [ABSTRACT FROM AUTHOR]

The present paper provides a meta-study of security and defence foresight documents by the Atlantic Alliance, European Union and various official bodies linked to the Defence Ministries of Canada, France, Germany, Italy, Spain, the United Kingdom and United States. It focuses on the political and social trends impacting on defence and military issues. In global terms, the following can be discerned: rivalry among major powers, with armed conflict between some not ruled out entirely; armed non-state actors strengthened by new technologies; global competition for natural resources; extensive demographic imbalances and migration flows; global risks associated with fragile states. In terms of European internal policy, the following trends are identified: population ageing and the challenges associated with immigrant integration; empowerment of interest-based and identity networks. The paper begins by familiarising readers with the characteristics of foresight documents and the methodologies used in their preparation. [ABSTRACT FROM AUTHOR]

Electricity generation currently is the main industrial source of air emissions in the United States. Both researchers and practitioners are interested in conducting studies to evaluate the ecological performance of this industry, in order to propose solutions to curb emissions of air pollutants and to improve the efficiency of converting fossil resources into electric energy. In this paper, data envelopment analysis (DEA) is used to assess ecological efficiency where air emissions are used as undesirable outputs. Although conventional DEA does not require a priori information on the input and output weights, weight restrictions can be incorporated to reflect a user's preference over the performance metrics, or to refine the DEA results. Adding weight restrictions voids the fact that DEA scores are independent of the units of measurement. To incorporate weight constraints in ecological efficiency assessment, this paper develops a DEA model that is units-invariant when weight restrictions are imposed. Moreover, the proposed model is equivalent to the standard units-invariant DEA model when weight restrictions are not present. [ABSTRACT FROM AUTHOR]

America faces a series of intersecting problems that relate to health inequities, failing schools, and an inadequate science, technology, engineering, and math (STEM) workforce, particularly in cities and among low-income Black and Latino youth. Here, we propose a solution, namely reforming secondary school education to include mandatory exposure to population thinking instruction to address these overlapping issues. Public health education has expanded in recent decades in undergraduate education, though it has yet to become an integral component of high school curricula. In this paper, we make the case that all youth should gain exposure to the skills of population thinking through public health education initiated in high school. We further provide a rationale for this approach drawn from multiple youth development frameworks and the community schools movement for honing youth critical thinking skills and problem solving relating to individual and community health, policy, and activism. [ABSTRACT FROM AUTHOR]

In this paper, we explore the relationships that individuals have with their communities. This work was prepared as part of the ASA Data Expo '13 sponsored by the Graphics Section and the Computing Section, using data provided by the Knight Foundation Soul of the Community survey. The Knight Foundation in cooperation with Gallup surveyed 43,000 people over 3 years in 26 communities across the United States with the intention of understanding the association between community attributes and the degree of attachment people feel towards their community. These include the different facets of both urban and rural communities, the impact of quality education, and the trend in the perceived economic conditions of a community over time. The goal of our work is to facilitate understanding of why people feel attachment to their communities through the use of an interactive and web-based visualization. We will explain the development and use of web-based interactive graphics, including an overview of the R package Shiny and the JavaScript library D3, focusing on the choices made in producing the visualizations and technical aspects of how they were created. Then we describe the stories about community attachment that unfolded from our analysis. [ABSTRACT FROM AUTHOR]

Purpose: Gestational surrogacy (GS) has been researched in multiple qualitative studies. In contrast, quantitative aspects of the practice are conspicuously understudied. The present article assesses and compares the incidence of GS in the USA and Israel, two industrialized countries that have maintained active commercial surrogacy practice, for over two decades. Method: The article is a secondary analysis of GS figures published by the Israeli Parliament's Centre for Research and Information (2018) and by the USA's Centers for Disease Control (2016) and related professional publications. Each dataset is analyzed in reference to the respective country, so as to devise local incidence scores that are then juxtaposed in inter-country comparison. Results: The incidence of GS rises steeply in both countries. Though US surrogates are contracted by local and international, heterosexual and gay, and partnered and single intended parents, the relative incidence of GS is lower in the USA than in Israel, where only local heterosexual couples could contract a gestational surrogate. An exceptionally high rate of multiple births was observed in both settings, suggesting some overlooking of professional recommendations for elective single-embryo transfer. Conclusion: GS incidence appears to resemble the ratio between the countries' respective fertility rates. The paper underscores two main risks facing gestational surrogates: the risk of not conceiving and not being paid and the risk of carrying a multiple pregnancy, which is extremely prevalent in GS pregnancies, and sustaining the short- and long-term health complications that are more prevalent in such pregnancies. [ABSTRACT FROM AUTHOR]

It is of importance to perform hydrological forecast using a finite hydrological time series. Most time series analysis approaches presume a data series to be ergodic without justifying this assumption. To our knowledge, there are no methods available for test of ergodicity to date. This paper presents a practical approach to analyze the mean ergodic property of hydrological processes by means of augmented Dickey Fuller test, Mann-Kendall trend test, a radial basis function neural network, and the assessment methods derived from the definition of ergodicity. The mean ergodicity of precipitation processes at Newberry, MI, USA, is analyzed using the proposed approach. The results indicate that the precipitations of January, May, and July in Newberry are highly likely to have ergodic property, the precipitations of February, and October through December have tendency toward mean ergodicity, and the precipitations of all the other months are non-ergodic. [ABSTRACT FROM AUTHOR]

In January 2016, Melissa Cook, a California gestational surrogate experiencing a multiple-birth pregnancy following the in vitro fertilization (IVF) transfer of three embryos comprised of donor eggs and sperm provided by the intended father, went to the media when the intended father requested that she undergo a fetal reduction because twins were less expensive to raise than triplets. Much of the legal interest in this case to date has centered on the enforceability of surrogacy contracts. However, the Cook case also raises troubling issues about fertility treatment practices involving gestational surrogates, twin preference, and third-party reproduction medical decision-making. This paper focuses on multiple-embryo transfers in the context of US surrogacy arrangements. Offering an original analysis of data obtained from the US national-assisted reproduction registry, it examines single- and multiple-embryo transfer trends over a 12-year period (2003 to 2014). Findings reveal that recommended guidelines were followed in fewer than 42% of the cases in 2014. The paper argues that ensuring equitable medical treatment for all recipients of IVF requires the adoption of treatment guidelines tailored to, and offering protections for, specific patient groups, and that, once in place, guidelines must be robustly implemented. [ABSTRACT FROM AUTHOR]

In 2006, the Massachusetts legislature passed a landmark health care reform bill (the Reform) that has served as a model for the national health care reform. By aiming to provide 'access to affordable, quality, accountable health care,' the goals of this reform were to reduce the number of uninsured Massachusetts residents while containing the growth of health care costs and improving the quality of health care services. The current paper examines the impact of the Reform on the quality of care in addition to hospital costs simultaneously from a perspective of efficiency analysis. We develop an integer-valued non-radial Russell data envelopment analysis (DEA) model, which is unit variant and calculate the hospitals' efficiency directly. However, the proposed integer-valued model is non-linear. The current paper thus transforms this model into a parametric integer linear programming. We develop a method to derive its optimal solutions. We then use the new DEA model to calculate and compare the efficiency scores of hospitals in Massachusetts and Connecticut pre- and post-Reform. The analysis shows that the Reform has achieved its cost containment and quality improvement goals at the same time. These analyses provide potentially useful information to hospital regulators and government regulators, especially in light of the national interest on health care legislation. [ABSTRACT FROM AUTHOR]

In the midst of an opioid epidemic, mortality related to opioid overdose continues to rise in the US. Medications to treat opioid use disorder, including methadone and buprenorphine, are highly effective in reducing the morbidity and mortality related to illicit opioid use. Despite the efficacy of these life-saving medications, the majority of people with an opioid use disorder lack access to treatment. This paper briefly reviews the evidence to support the use of medications to treat opioid use disorder with a specific focus on methadone. We discuss the current state of methadone therapy for the treatment of opioid use disorder in the US and present logistical barriers that limit its use. Next, we examine three international pharmacy-based models in which methadone dispensing to treat opioid use disorder occurs outside of an opioid treatment facility. We discuss current challenges and opportunities to incorporate similar methods of methadone dispensing for the treatment of opioid use disorder in the US. Finally, we present our vision to integrate pharmacy-based methadone dispensing into routine opioid use disorder treatment through collaboration between clinicians and pharmacies to improve local access to this life-saving medication. [ABSTRACT FROM AUTHOR]

Delivering well-coordinated care is essential for optimizing clinical outcomes, enhancing patient care experiences, minimizing costs, and increasing provider satisfaction. The Veterans Health Administration (VA) has built a strong foundation for internally coordinating care. However, VA faces mounting internal care coordination challenges due to growth in the number of Veterans using VA care, high complexity in Veterans' care needs, the breadth and depth of VA services, and increasing use of virtual care. VA's Health Services Research and Development service with the Office of Research and Development held a conference assessing the state-of-the-art (SOTA) on care coordination. One workgroup within the SOTA focused on coordination between VA providers for high-need Veterans, including (1) Veterans with multiple chronic conditions; (2) Veterans with high-intensity, focused, specialty care needs; (3) Veterans experiencing care transitions; (4) Veterans with severe mental illness; (5) and Veterans with homelessness and/or substance use disorders. We report on this workgroup's recommendations for policy and organizational initiatives and identify questions for further research. Recommendations from a separate workgroup on coordinating VA and non-VA care are contained in a companion paper. Leaders from research, clinical services, and VA policy will need to partner closely as they develop, implement, assess, and spread effective practices if VA is to fully realize its potential for delivering highly coordinated care to every Veteran. [ABSTRACT FROM AUTHOR]

This perspective paper seeks to lay out an efficient approach for health care providers, researchers, and other stakeholders involved in interventions aimed at improving care coordination to partner in locating and using applicable care coordination theory. The objective is to learn from relevant theory-based literature about fit between intervention options and coordination needs, thereby bringing insights from theory to enhance intervention design, implementation, and troubleshooting. To take this idea from an abstract notion to tangible application, our workgroup on models and measures from the Veterans Health Administration (VA) State of the Art (SOTA) conference on care coordination first summarizes our distillation of care coordination theoretical frameworks (models) into three common conceptual domains-context of an intervention, locus in which an intervention is applied, and specific design features of the intervention. Then we apply these three conceptual domains to four cases of care coordination interventions ("use cases") chosen to represent various scopes and stages of interventions to improve care coordination for veterans. Taken together, these examples make theory more accessible and practical by demonstrating how it can be applied to specific cases. Drawing from theory offers one method to anticipate which intervention options match a particular coordination situation. [ABSTRACT FROM AUTHOR]

Background: Improving care coordination is a key priority for many healthcare systems. However, initiatives to improve care coordination are complex to implement and have produced mixed results. A better understanding of how to craft and support implementation of effective care coordination strategies is needed.Objective: To identify and understand the challenges and factors encountered by Patient-Aligned Care Team (PACT) staff in performing care coordination tasks in outpatient clinics in the Veterans Health Administration (VA).Design: Qualitative study using semi-structured formative evaluation interviews.Participants: Fourteen interviews with 18 clinical frontline managers and staff from 12 clinic sites across five VA health systems.Interventions: This paper reports on baseline data collected for the Coordination Toolkit and Coaching (CTAC) project. CTAC aims to improve patients' experience of care coordination within VA primary care and between PACT and other outpatient and community settings.Approach: We conducted pre-implementation telephone interviews with frontline managers and staff, primarily nurse managers.Key Results: PACT staff described challenges in aligning care coordination priorities across different levels of the VA system, including staff, patients, and leadership. Additionally, PACT staff noted challenges coordinating care both within and outside the VA, and identified resource barriers impeding their care coordination efforts. To address these challenges, staff made several recommendations for improvement, including (1) contingency staffing to address staff burnout; (2) additional PACT training for new staff; (3) clarification of care coordination roles and responsibilities; and (4) and care coordination initiatives that align both with centrally initiated care coordination programs and frontline needs.Conclusion: In the VA and similarly complex healthcare systems, our findings suggest the need for care coordination strategies that are buttressed by a system-level vision for care coordination, backed up by clear roles and responsibilities for information exchange between primary care staff and other settings, and multidimensional accountability metrics that encompass patient-, staff-, and system-level goals. [ABSTRACT FROM AUTHOR]

This paper develops a frequentist model averaging approach for threshold model specifications. The resulting estimator is proved to be asymptotically optimal in the sense of achieving the lowest possible squared errors. In particular, when combining estimators from threshold autoregressive models, this approach is also proved to be asymptotically optimal. Simulation results show that for the situation where the existing model averaging approach is not applicable, our proposed model averaging approach has a good performance; for the other situations, our proposed model averaging approach performs marginally better than other commonly used model selection and model averaging methods. An empirical application of our approach on the US unemployment data is given. [ABSTRACT FROM AUTHOR]

The hop has been used for centuries in brewing because of its aroma and bitterness; it is mainly cultivated in Northern Europe and in the United States; to date, few data are available about its performance in the Mediterranean environment. The hop contribution to the final taste of beers is related to the chemical composition of resin and essential oils resulting from both genetic material and environmental conditions. In the last decade, the rising of interest in craft beer has led brewers to use local ingredients to produce beers with a stronger identity. In this paper, the quality parameters of hop Var. Cascade recently introduced in Sardinia have been investigated. The hop fields were sampled for three crop seasons (2015, 2016 and 2017); the yield and the chemical composition of essential oil as well as the alpha- and beta-acids were evaluated by GC–MS and UV-HPLC techniques. The obtained data demonstrate that features of Cascade hop grew in Sardinia are in the same range of those cultivated in USA. [ABSTRACT FROM AUTHOR]

The purpose of the paper is to explore novel means to overcome the controversial ban in the USA against mitochondrial replacement therapy, a form of IVF, with the added step of replacing a woman's diseased mutated mitochondria with a donor's healthy mitochondria to prevent debilitating and often fatal mitochondrial diseases. Long proven effective in non-human species, MRT recently performed in Mexico resulted in the birth of a healthy baby boy. We explore the ethics of the ban, the concerns over hereditability of mitochondrial disease and its mathematical basis, the overlooked role of Mitochondrial Eve, the financial burden of mitochondrial diseases for taxpayers, and a woman's reproductive rights. We examine applicable court cases, particularly protection of autonomy within the reproductive rights assured by Roe v Wade. We examine the consequences of misinterpreting MRT as genetic engineering in the congressional funding prohibitions causing the MRT ban by the FDA. Allowing MRT to take place in the USA would ensure a high standard of reproductive medicine and safety for afflicted women wishing to have genetically related children, concurrently alleviating the significant financial burden of mitochondrial diseases on its taxpayers. Since MRT does not modify any genome, it falls outside the "heritable genetic modification" terminology of concern to Congress and the FDA. Correcting this terminology, the IOM's conclusion that MRT is ethical, the continuing normalcy of the first MRT recipient, and increasing public awareness of the promising benefits might be all that is required to modify the FDA's position on MRT. [ABSTRACT FROM AUTHOR]