Past Record Research Articles

Prediction intervals for future Pareto record claims

AbstractStochastic models and methods for quantifying extreme events are of interest in numerous disciplines. Within this paper, statistical prediction of extreme claims or losses in insurance industry is considered based on upper record values, which describe successively largest observations in a sequence of data over time. The problem of predicting a future record value (here in particular, a future record claim) based on a sequence of previously observed record values (here, past record claims) is addressed by means of prediction intervals. For an underlying Pareto distribution, respective exact and approximate intervals from the literature are summarized and modified and new ones are developed. In a simulation study, these prediction intervals are evaluated and compared regarding coverage frequency and length. The impact of the number of observed record values as well as the choice of the Pareto distribution is discussed. In the case of a small number of record values, the use of k-th record values is considered as an option for statistical analyses to predict, e.g., second largest record claims. Selected prediction methods are applied to several real data sets, which turn out to perform well and to be able to capture the magnitude of future record claims, even for fairly small numbers of record observations. For comparison, generalized Pareto distributions are fitted to real data sets and a corresponding point predictor as well as a respective upper prediction interval for the next record to appear are derived and evaluated.

Are Scientists Changing their Research Productivity Classes When They Move Up the Academic Ladder?

AbstractWe approach productivity in science in a longitudinal fashion: We track scientists’ careers over time, up to 40 years. We first allocate scientists to decile-based publishing productivity classes, from the bottom 10% to the top 10%. Then, we seek patterns of mobility between the classes in two career stages: assistant professorship and associate professorship. Our findings confirm that radically changing publishing productivity levels (upward or downward) almost never happens. Scientists with a very weak past track record in publications emerge as having marginal chances of becoming scientists with a very strong future track record across all science, technology, engineering, mathematics, and medicine (STEMM) fields. Hence, our research shows a long-term character of careers in science, with one’s publishing productivity during the apprenticeship period of assistant professorship heavily influencing productivity during the more independent period of associate professorship. We use individual-level microdata on academic careers (from a national registry of scientists) and individual-level metadata on publications (from the Scopus raw dataset). Polish associate professors tend to be stuck in their productivity classes for years: High performers tend to remain high performers, and low performers tend to remain low performers over their careers. Logistic regression analysis powerfully supports our two-dimensional results. We examine all internationally visible Polish associate professors in five fields of science in STEMM fields (N = 4,165 with Nart = 71,841 articles).

Polymorphic CAG and GGN repeats in Cryptorchidism patient risk: A meta-analytical study

Genetic mutations in the androgen receptor (AR) gene have been identified as the cause of androgen insensitivity syndrome. These mutations are linked to inconsistent development of the Wolffian duct and may result in conditions such as micropenis, hypospadias, and cryptorchidism. The androgen receptor has two polymorphic sites located in exon 1, which consists of varying amounts of CAG and GGN repeats. These repetitions lead to the formation of polyglutamine and polyglycine stretches of varied lengths. Increased CAG repeats lead to a decrease in androgen receptor transcriptional activity, but the impact of GGN triplets is less well understood. This research examined the CAG and GGN repeat lengths in males who had a past medical record of cryptorchidism. Prospective and retrospective observational studies from PubMed, Science Direct, and Embase were systematically searched up to 15th November 2020. Primary outcomes were analyzed using a fixed or random effect model regarding its heterogeneity and continued with multilevel modeling of each polymorphism and ethnicity. CAG and GGN repeat polymorphism was found to be significantly different compared to control in contributing to cryptorchidism (CAG: 0.55 [CI 95%=0.19-0.91]; p-value=0.003 and GGN 0.90 [CI95%=0.65-1.15]; p value<0.000). In conclusion, CAG and GGN repeat polymorphism have an essential role in the incidence of cryptorchidism.

Strategi Kampanye Partai Amanat Nasional Pada Pemilu Legislatif 2024: Pendekatan Teori Marketing-Mix

This article examines to find out the successful campaign strategy of the National Mandate Party (Partai Amanat Nasional, PAN) in increasing its vote in the 2024 Legislative Election. Methodologically, this article uses qualitative method by applying case study approach. In data gathering, it employs library research by mining data from online news channels. In the data analysis, it utilizes the modern platform of NVivo 14 mainly word frequency query. To measure the campaign strategy of PAN, this article applies four indicators of marketing-mix theory: product, price, place, promotion (4P). The finding reveals that in terms of product, PAN promotes a platform of “helping people” with its past record as the reformist party and supported by the legislative candidates’ background of entrepreneurs and artists-celebrities. Regarding the price, the PAN’s campaign budget spent at least 223 billion IDR with its successful campaigns which conducted by each legislative candidate. PAN prefers to rely on the popularity of President of Joko Widodo and legislative candidates. In the context of place, the strategy of local network and canvassing are based on the creativity of each legislative candidates in attracting voters’ sympathy. The leader tour is conducted by visiting influential public figures. Concerning promotion, the campaign publication of PAN relies on printed mass media, television, and social media while event publication is carried out in various regions across the country. Among those four indicators, the price has the most significant impact which causes to the successful campaign of PAN in the 2024 Simultaneous Election.

The Global Business Growth, impeding headwinds: Resilience of Indian Economy and transition to circular economy

The global economy is on the threshold of witnessing greater uncertainties and uneven development in the face of Russia-Ukraine war, China’s zero covid policy and a deep hole in the pockets of developing economies due to burgeoning oil bills and the in-built resilient nature of Indian economy to bounce back given its past record and switch over to renewable energy resources by augmenting renewable source of energy by tapping into green hydrogen.

Self-stigma of incarceration and its impact on health and community integration.

Individuals returning to the wider community from incarceration face many re-entry barriers, including stigmatising beliefs regarding past criminal record, that have impact on health and re-entry. Understanding the development and impact of self-stigma on health can inform re-entry and rehabilitation services. The two aims of this study were first, to evaluate a previously established model of self-stigma applied to individuals who have experienced incarceration and, secondly, to study the impact of self-stigma on physical and mental health as well as community integration on re-entry. This is a cross-sectional study of 129 formerly incarcerated adults recruited using an online platform and asked to complete online rating scales about self-stigmatisation, health and sense of community integration. Repeated-measures analysis of variance, correlation analysis, and path analyses were used to evaluate the model. There was support for the four distinct stages of self-stigmatisation apparent in mental health research. There was a relationship between self-stigma harm and sense of community integration, mediated by mental but not physical health status scores. Our findings add to work on self-stigmatisation in the field of mental health by showing that the concept appears relevant and appears in similar staging among formerly incarcerated individuals and that self-stigmatisation is likely to be important for their community reintegration. Our sample was not typical of the wider prison population for race and gender distribution, in particular having fewer than expected those minority groups likely to be especially vulnerable to stigmatisation by others. Our findings nevertheless suggest that further, preferably, longitudinal research on self-stigma to enable better understanding of pathways could substantially help treatment and rehabilitation of individuals after release from a correctional facility.

Facility Preparedness for Recurring Cholera Epidemics: A Case of Matero and Kanyama Sub-Districts, in Lusaka.

Cholera, an enteric disease of immense public health concern, causes an estimated 2.8 million cases with 91,000 deaths globally every year. The first cholera outbreak in Zambia was reported in the 1970s and several other outbreaks have occurred over the years, with the worst outbreak being in 2023/2024 with over 20,577 cases surpassing the past highest record 1991 outbreak that resulted in over 13,000 cases. During the 2009, 2010 and 2016 cholera outbreaks, Lusaka reported the highest number of cholera cases in Zambia. Zambia remains threatened by recurrent outbreaks of cholera, with more than 11 cholera hotspots spread across peri-urban areas and fishing camps. In Lusaka the hotspots include Matero and Kanyama constituencies/sub-districts. Each time Lusaka has cholera outbreaks, the cases are always overwhelming in the initial stages, something which made the researchers question the levels of preparedness especially that cholera has an established routine of starting at the onset of the rain season. The study used mixed method (both qualitative & quantitative) research with exploratory descriptive approach as a research methodology. The study drew 70 participants from 9 health facilities in the sub-district that attended to a questionnaire and 9 facility heads that responded to a questionnaire. Data analysis was achieved through descriptive analysis of quantitative data using SPSS and thematic narrations for the qualitative data. The findings show that 71.4% of the respondents came from facilities that were only partially prepared to handle cases of cholera should an outbreak occur. The study also found that 74.3% of the respondents had a negative attitude towards work regarding the outbreaks of cholera. The study revealed that 78.6% of the respondents indicated that their facilities had poor flow of materials and resources for management and prevention of the cholera outbreak. 85.7% indicated that their health facility administration policies and initiatives were poor when it comes to prevention and management of cholera cases. The study concluded that the main reasons for partially prepared facilities were inadequate infrastructure, materials/equipment, human resource and poor attitudes of healthcare workers and administrative policies. Therefore, the study recommends provision of supplies and behaviour change including continuous capacity building of Healthcare Workers in response to Cholera and other emerging and re-emerging diseases (Epidemics). Further, we recommend for the development and evaluation of training programs for healthcare workers that focus not only on technical skills but also on improving attitudes and administrative practices.

Theoretical provisions on operation and maintenance for determining the reliability of ship electric drives

The results of the performed studies have confirmed that at present the most common type of drive of ship mechanisms is an electric drive. Electric drives are the main recipients of electric energy, including the ship energy. The number of electric motors installed on ships is, as a rule, hundreds of units. It is proposed, based on the classification developed earlier, to conventionally refer the majority of ship electric drives, despite their wide variety, to three main groups, differing in operating modes and operating conditions determined by their functional purpose: close to typical modes S1 and S6 — the first group; S2 — the second group; S3, S4, S5, S7, S8, the third group. The specific conditions and operating modes of ship electric drives, which differ significantly from land-based ones and are determined by higher climatic and mechanical loads, lead to a decrease in their reliability, the need for increased attention to maintenance and repairs of electric drives, their components and elements. The basic principles of the approach to working with statistical data necessary for calculating the reliability indicators of marine electric drives are formulated. To analyze the past record of operation and maintenance of ship electric drives, a method based on the collection and processing of statistical information in operational conditions was used, which is the most effective, allowing to take into account the complex impact on the reliability of electric drives of all operational factors that are difficult to reproduce during testing, and does not require the cost of creating test benches and performing tests. Judging by the number of failures, the regulated ship electric drives of approximate mode S2 (windlasses, capstans, etc.) and modes S3, S4, S5, S7 and S8 (hoist winches and cranes) are less reliable in comparison with others.

From modern-day parasitology to paleoparasitology: the elusive past record and evolution of Cryptosporidium.

Recent efforts have been made to review the state of the art on a variety of questions and targets in paleoparasitology, including protozoan taxa. Meanwhile, these efforts seemed to let aside Cryptosporidium, and we then intended to review its paleoparasitological record to assess its past distribution and favored detection methods, and eventually highlight needed research trajectories. This review shows that contrary to other parasites, most of the positive results came from South-American sites and coprolites rather than sediment samples, highlighting the need to test this kind of material, notably in Europe where many negative results were reported in the published literature from sediment samples. Moreover, aDNA-based detections are nearly absent from the paleoparasitological record of this parasite, though punctually shown successful. With their potential to address the evolutionary history of Cryptosporidium species, notably through their 18S rRNA tree, aDNA-based approaches should be encouraged in the future. In sum, and though the limits of currently used methods and materials remain unclear, this review highlights the potential role of coprolites and aDNA for the study of Cryptosporidium species in the past and how this history shaped their current diversity and distribution, notably among human populations but also farm animals.

Investigating the mental health of university students during the COVID-19 pandemic in a UK university: a machine learning approach using feature permutation importance

Mental wellbeing of university students is a growing concern that has been worsening during the COVID-19 pandemic. Numerous studies have gathered empirical data to explore the mental health impact of the pandemic on university students and investigate factors associated with higher levels of distress. While the online questionnaire survey has been a prevalent means to collect data, regression analysis has been observed a dominating approach to interpret and understand the impact of independent factors on a mental wellbeing state of interest. Drawbacks such as sensitivity to outliers, ineffectiveness in case of multiple predictors highly correlated may limit the use of regression in complex scenarios. These observations motivate the underlying research to propose alternative computational methods to investigate the questionnaire data. Inspired by recent machine learning advances, this research aims to construct a framework through feature permutation importance to empower the application of a variety of machine learning algorithms that originate from different computational frameworks and learning theories, including algorithms that cannot directly provide exact numerical contributions of individual factors. This would enable to explore quantitative impact of predictors in influencing student mental wellbeing from multiple perspectives as a result of using different algorithms, thus complementing the single view due to the dominant use of regression. Applying the proposed approach over an online survey in a UK university, the analysis suggests the past medical record and wellbeing history and the experience of adversity contribute significantly to mental wellbeing states; and the frequent communication with families and friends to keep good relationship as well as regular exercise are generally contributing to improved mental wellbeing.

Air pollution particulate matter (PM2.5) prediction in South African cities using machine learning techniques.

Air pollution contributes to the most severe environmental and health problems due to industrial emissions and atmosphere contamination, produced by climate and traffic factors, fossil fuel combustion, and industrial characteristics. Because this is a global issue, several nations have established control of air pollution stations in various cities to monitor pollutants like Nitrogen Dioxide (NO2), Ozone (O3), Sulfur Dioxide (SO2), Carbon Monoxide (CO), Particulate Matter (PM2.5, PM10), to notify inhabitants when pollution levels surpass the quality threshold. With the rise in air pollution, it is necessary to construct models to capture data on air pollutant concentrations. Compared to other parts of the world, Africa has a scarcity of reliable air quality sensors for monitoring and predicting Particulate Matter (PM2.5). This demonstrates the possibility of extending research in air pollution control. Machine learning techniques were utilized in this study to identify air pollution in terms of time, cost, and efficiency so that different scenarios and systems may select the optimal way for their needs. To assess and forecast the behavior of Particulate Matter (PM2.5), this study presented a Machine Learning approach that includes Cat Boost Regressor, Extreme Gradient Boosting Regressor, Random Forest Classifier, Logistic Regression, Support Vector Machine, K-Nearest Neighbor, and Decision Tree. Cat Boost Regressor and Extreme Gradient Boosting Regressor were implemented to predict the latest PM2.5 concentrations for South African Cities with recording stations using past dated recordings, then the best performing model between the two is used to predict PM2.5 concentrations for South African Cities with no recording stations and also to predict future PM2.5 concentrations for South African Cities. K-Nearest Neighbor, Logistic Regression, Support Vector Machine, Decision Tree, and Random Forest Classifier were implemented to create a system predicting the Air Quality Index (AQI) Status. This study investigated various machine learning techniques for air pollution to analyze and predict air pollution behavior regarding air quality and air pollutants, detecting which areas are most affected in South African cities.

Spatially coherent variability in modern orographic precipitation produces asymmetric paleo-glacier extents in flowline models: Olympic Mountains, USA

Abstract. Glaciers are sensitive to temporal climate variability. Glacier sensitivity to spatial variability in climate has been much less studied. The Olympic Mountains of Washington state, USA, experience a pronounced orographic precipitation gradient, with modern annual precipitation ranging between ∼6500 and ∼500 mm water equivalent. In the Quinault valley, on the wet side of the range, a glacier extended onto the coastal plain, reaching a maximum position during the Early Wisconsin glaciation. On the dry side of the range, in the Elwha valley, there is no evidence of a large paleo-glacier during the Wisconsin glaciation. We hypothesize that asymmetry in the past glacier extent was driven by spatial variability in precipitation. To evaluate this hypothesis, we constrain the past precipitation gradient and model the glacier extent. We explore variability in observed and modeled precipitation gradients over timescales from 6 h to ∼100 yr. Across three datasets, basin-averaged precipitation in the Elwha is 54 % of that in the Quinault. Our analysis overwhelmingly indicates spatially coherent variability in precipitation across the peninsula. We conclude that the past precipitation gradient was likely similar to the modern gradient. We use a one-dimensional glacier flowline model, driven by sea level summer temperature and annual precipitation to approximate the glacier extent in the Quinault and Elwha valleys. We find several equilibrium states for the Quinault glacier at the mapped maximum position within paleoclimate constraints for cooling and drying, relative to present-day conditions. Assuming stable precipitation gradients, we model the Elwha glacier extent for the climates of these equilibria. At the warm end of the paleoclimate constraint (July average sea level temperature of 10.5 ∘C), a small valley glacier occurs in the high headwaters of the Elwha valley. Yet, for the cooler end of the allowable paleoclimate (July average sea level temperature of 7 ∘C), the Elwha glacier advances to a narrow notch in the valley, thickens, and rapidly extends far beyond the likely true maximum extent. Therefore, we suggest that the Early Wisconsin period was more likely to have been relatively warm because our models of the glacial extent are consistent with the past record of glaciation in both the Quinault valley and Elwha valley for warm conditions but inconsistent for cooler conditions. Alternatively, spatially variable drivers of ablation, including differences in cloudiness, could have contributed to past asymmetry in the glacier extent. Future research to constrain past precipitation gradients and evaluate their impact on glacier dynamics is needed to better interpret the climatic significance of past glaciation and to predict the future response of glaciers to climate change.

Deriving and validating an asthma diagnosis prediction model for children and young people in primary care.

Introduction: Accurately diagnosing asthma can be challenging. We aimed to derive and validate a prediction model to support primary care clinicians assess the probability of an asthma diagnosis in children and young people. Methods: The derivation dataset was created from the Avon Longitudinal Study of Parents and Children (ALSPAC) linked to electronic health records. Participants with at least three inhaled corticosteroid prescriptions in 12-months and a coded asthma diagnosis were designated as having asthma. Demographics, symptoms, past medical/family history, exposures, investigations, and prescriptions were considered as candidate predictors. Potential candidate predictors were included if data were available in ≥60% of participants. Multiple imputation was used to handle remaining missing data. The prediction model was derived using logistic regression. Internal validation was completed using bootstrap re-sampling. External validation was conducted using health records from the Optimum Patient Care Research Database (OPCRD). Results: Predictors included in the final model were wheeze, cough, breathlessness, hay-fever, eczema, food allergy, social class, maternal asthma, childhood exposure to cigarette smoke, prescription of a short acting beta agonist and the past recording of lung function/reversibility testing. In the derivation dataset, which comprised 11,972 participants aged <25 years (49% female, 8% asthma), model performance as indicated by the C-statistic and calibration slope was 0.86, 95% confidence interval (CI) 0.85-0.87 and 1.00, 95% CI 0.95-1.05 respectively. In the external validation dataset, which included 2,670 participants aged <25 years (50% female, 10% asthma), the C-statistic was 0.85, 95% CI 0.83-0.88, and calibration slope 1.22, 95% CI 1.09-1.35. Conclusions: We derived and validated a prediction model for clinicians to calculate the probability of asthma diagnosis for a child or young person up to 25 years of age presenting to primary care. Following further evaluation of clinical effectiveness, the prediction model could be implemented as a decision support software.

Building synergy between Cu and Ag for efficient hydrogen production from formaldehyde reforming

Building a sustainable society requires efficient chemical storage of hydrogen and facile on-demand generation. In this work, hydrogen is spontaneous generated from reforming formaldehyde and water on bimetallic Ag/Cu NWs catalyst, which is synthesized by a simple wet etching method together with electrochemical reduction. The results indicate that O2 can effectively promote H2 generation by forming *OOH specie, and act as a medium for H atom transfer. The synergistic effect between Ag and Cu greatly improves the catalytic activity. Specifically, the adsorption of O2 on Cu sites promotes the dehydrogenation of formaldehyde and proton coupling on Ag sites. Consequently, the Ag/Cu catalyst expresses excellent hydrogen production from aqueous formaldehyde solution at a rate of 12.3 mmol/(gcat.∙min), which surpass the past record. This work clearly explains the synergistic effect mechanism on formaldehyde reforming from the atomic level, providing guidance for the design of highly efficient catalysts for aqueous-phase spontaneous hydrogen evolution reactions.

Historical data-independent remaining useful life prediction method based on dual-input deep learning neural network

Accurate and reliable remaining useful life (RUL) prediction is of critical importance in lithium-ion battery management. Current RUL prediction methods rely on historical data to estimate RUL, which are inapplicable when past record is unavailable or working condition is changing. To get rid of the dependence of historical data and predict RUL under a given working condition, a novel historical data-independent RUL prediction method is proposed, which adopts a dual-input deep learning neural network (DNN) and utilizes data in the current cycle only. Compared with single-input models, including single-input DNN and mainstream machine learning models, the proposed dual-input DNN combines heterogenous vector-form features and sequence-form features together to predict RUL, outperforms all of its counterparts and achieves the best performance on test set. Furthermore, the effectiveness of working condition-derived features is also validated, which demonstrates broad applicability of such features. This work provides a brand-new method towards historical data-independent RUL estimation and offers a unique DNN design for RUL prediction and other battery management applications.

The Metaverse and Terrorism: Threats and Challenges

The metaverse is currently the leading hype in the digital world because of its seemingly infinite potential and possibilities. Large corporations are drawn to the metaverse because it appears as the cutting edge of digital and technological developments. The metaverse is presented by communication technology companies as the next Internet, a leap towards a universe of boundless, interconnected virtual communities. However, there are many potential risks and challenges that the metaverse raises, including technical, legal, security, business, tax, privacy, security, and users’ well-being and safety (among many others). Cyber-savvy terrorists have been highly resourceful in adapting and applying online platforms and have taken advantage of every new development, platform, and application. Based on their past record, it is reasonable to assume that the metaverse is a new dimension that terrorists and violent extremists are poised to study, examine, and possibly utilise. This research note explores some potential uses of the metaverse by terrorists and suggests preemptive measures to minimise the risks of them doing so. If the advancement of the metaverse or similar developments is inevitable, we should consider risks and abuses and think more carefully about them when moving forward.

Aetiology of end-stage renal disease at the National Referral Hospital of Bhutan

Introduction: End stage renal disease (ESRD) is a late stage of chronic kidney disease (CKD) requiring chronic dialysis or renal transplant to survive. End stage renal disease is a growing problem worldwide. In Bhutan too, there are anecdotal evidence showing increasing number of ESRD patients every year, which has become a cause of serious concern for the country. Methods: This retrospective descriptive study was conducted at the National Referral Hospital of Bhutan, from 2019 to 2020 to find out the aetiology of ESRD in the country. The demographic information and aetiology of ESRD were collected from the past record of the patients. A total of 202 eligible ESRD patients were enrolled in this study. Results: The study showed that the mean age of patients was 47.3 ±13.20 years. The majority of the patients 139 (68.8%) were in the age group 24-60 years. Of the total patients, 107 (53.0%) were females. The common aetiologies of ESRD were hypertension (HTN), chronic glomerulonephritis (CGN) and Diabetes Mellitus (DM) constituting 20.8%, 12.9%, and 8.9% of the total patients respectively. In 50.0% of the patients, the aetiology of ESRD were unexplained. Conclusions: The most common aetiologies of ESRD were HTN (20.8%), CGN (12.9%) and DM (8.9%); in 50.0% of the patients, the aetiologies remained unexplained. The majority of ESRD patients (68.8%) in this study were from middle age group, with female predominance (53.0%).

The Many Faces of Credibility: Hawks, Doves, and Nuclear Disarmament

The conventional wisdom in international relations holds that an actor’s past record of keeping her word determines her cooperative credibility, and that mutual perceptions of credibility are essential in sustaining cooperation. Yet competing reputation-skeptic and psychological perspectives dispute this conventional wisdom, suggesting that assessments of cooperative credibility result from observers’ judgments about the other’s capabilities and interests or observers’ foreign policy orientations. How do observers assess others’ cooperative credibility? We field a nationally representative survey experiment asking 2,953 Americans to evaluate a hypothetical coercer’s commitment to lift sanctions on a would-be proliferator in exchange for the latter dismantling its nascent nuclear program. We vary the coercer’s previous behavior plus several other contextual factors. We find that respondents’ hawkishness interacts with the coercer’s past actions to shape respondents’ credibility assessments and their support for the proliferator accepting the proposal, with substantial implications for theories of misperception and bargaining.

The US Immigration Courts, Dumping Ground for the Nation’s Systemic Immigration Failures: The Causes, Composition, and Politically Difficult Solutions to the Court Backlog

The US immigration court system seeks to “fairly, expeditiously, and uniformly administer and interpret US immigration laws” (DOJ 2022a). It represents the first exposure of many immigrants to due process and the rule of law in the United States, and occupies an integral role in the larger US immigration system. Yet it labors under a massive backlog of pending cases that undermines its core goals and objectives. The backlog reached 1.87 million cases in the first quarter of FY 2023 (Straut-Eppsteiner 2023, 6). This paper attributes the backlog to systemic failures in the broader immigration system that negatively affect the immigration courts, such as: Visa backlogs, United States Citizen and Immigration Services (USCIS) application processing delays, and other bottlenecks in legal immigration processes. The immense disparity in funding between the court system and the Department of Homeland Security (DHS) agencies that feed cases into the courts. The failure of Congress to pass broad immigration reform legislation that could ease pressure on the enforcement and court systems. The lack of standard judicial authorities vested in Immigration Judges (IJs), limiting their ability to close cases; pressure parties to “settle” cases; and manage their dockets. The absence of a statute of limitations for civil immigration offenses. Past DHS failures to establish and adhere to enforcement priorities and to exercise prosecutorial discretion (PD) throughout the removal adjudication process, including in initial decisions to prosecute. The location of the Executive Office for Immigration Review (EOIR), which oversees US immigration courts, within the nation’s preeminent law enforcement agency, the Department of Justice (DOJ). The misconception of many policymakers that the court system should primarily serve as an adjunct to DHS. A past record of temporary judge reassignments and government shutdowns. The paper supports a well-resourced and independent immigration court system devoted to producing the right decisions under the law. Following a short introduction, a long section on “Causes and Solutions to the Backlog” examines the multi-faceted causes of the backlog, and offers an integrated, wide-ranging set of recommendations to reverse and ultimately eliminate the backlog. The “Conclusion” summarizes the paper’s topline findings and policy proposals.

Deriving and validating an asthma diagnosis prediction model for children and young people in primary care

Introduction: Accurately diagnosing asthma can be challenging. We aimed to derive and validate a prediction model to support primary care clinicians assess the probability of an asthma diagnosis in children and young people. Methods: The derivation dataset was created from the Avon Longitudinal Study of Parents and Children (ALSPAC) linked to electronic health records. Participants with at least three inhaled corticosteroid prescriptions in 12-months and a coded asthma diagnosis were designated as having asthma. Demographics, symptoms, past medical/family history, exposures, investigations, and prescriptions were considered as candidate predictors. Potential candidate predictors were included if data were available in ≥60% of participants. Multiple imputation was used to handle remaining missing data. The prediction model was derived using logistic regression. Internal validation was completed using bootstrap re-sampling. External validation was conducted using health records from the Optimum Patient Care Research Database (OPCRD). Results: Predictors included in the final model were wheeze, cough, breathlessness, hay-fever, eczema, food allergy, social class, maternal asthma, childhood exposure to cigarette smoke, prescription of a short acting beta agonist and the past recording of lung function/reversibility testing. In the derivation dataset, which comprised 11,972 participants aged &lt;25 years (49% female, 8% asthma), model performance as indicated by the C-statistic and calibration slope was 0.86, 95% confidence interval (CI) 0.85–0.87 and 1.00, 95% CI 0.95–1.05 respectively. In the external validation dataset, which included 2,670 participants aged &lt;25 years (50% female, 10% asthma), the C-statistic was 0.85, 95% CI 0.83–0.88, and calibration slope 1.22, 95% CI 1.09–1.35. Conclusions: We derived and validated a prediction model for clinicians to calculate the probability of asthma diagnosis for a child or young person up to 25 years of age presenting to primary care. Following further evaluation of clinical effectiveness, the prediction model could be implemented as a decision support software.