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Saadiq M Moledina,* Alexander A Maini,* Alice Gargan, William Harland, Heloise Jenney, Georgina Phillips, Kate Thomas, Devkishan Chauhan, Michael Fertleman From the Cutrale Perioperative & Ageing Group, St Mary’s Hospital, Imperial College London, London, UK*These authors contributed equally to this workCorrespondence: Saadiq M MoledinaSt Mary’s Hospital, Imperial College London, Praed Street, Paddington, London W2 1NY, UKTel +44 02033126666Email smoledina1@doctors.org.ukBackground/Introduction: The coronavirus disease 2019 (COVID-19) pandemic has affected all aspects of inpatient hospital medicine with patients admitted from level 1 (general medical wards) to level 3 (intensive care). Often, there are subtle physiological differences in these cohorts of patients. In particular, in intensive care, patients tend to be younger and have increased disease severity. Data, to date, has combined outcomes from medical and intensive care cohorts, or looked exclusively at intensive care. We looked solely at the level 1 (medical) cohort to identify their clinical characteristics and predictors of outcome.Patients and Methods: This was a retrospective study of adult patients admitted to a central London teaching hospital with a diagnosis of COVID-19 from 23rd March to 7th April 2020 identified from the hospital electronic database. Any patients who required level 2 or 3 care were excluded.Results: A total of 229 patients were included for analysis. Increased age and frailty scores were associated with increased 30-day mortality. Reduced renal function and elevated troponin blood levels are also associated with poor outcome. Baseline observations showed that increased oxygen requirement was predictive for mortality. A trend of increased mortality with lower diastolic blood pressure was noted. Lymphopenia was not shown to be related to mortality.Conclusion: Urea and creatinine are the best predictors of mortality in the level 1 cohort. Unlike previous intensive care data, lymphopenia is not predictive of mortality. We suggest that these factors be considered when prognosticating and for resource allocation for the treatment and escalation of care for patients with COVID-19 infection.Keywords: COVID-19, mortality, frailty, ICU, renal dysfunction, lymphopenia

Christopher Burnsides,1,* Jacqueline Corry,1,* Jacob Alexander,1 Catherine Balint,1 David Cosmar,1 Gary Phillips,2 Jeanette I Webster Marketon1,31Division of Pulmonary, Allergy, Critical Care and Sleep Medicine, Department of Internal Medicine, 2Center for Biostatistics, 3Institute for Behavioral Medicine Research, Wexner Medical Center at The Ohio State University, Columbus, OH, USA*JC and CB have equally contributed to this workPurpose: Glucocorticoids are commonly prescribed to treat a number of diseases including the majority of inflammatory diseases. Despite considerable interpersonal variability in response to glucocorticoids, an insensitivity rate of about 30%, and the risk of adverse side effects of glucocorticoid therapy, currently no assay is performed to determine sensitivity.Patients and methods: Here we propose a whole blood ex vivo stimulation assay to interrogate known glucocorticoid receptor (GR) up- and downregulated genes to indicate glucocorticoid sensitivity. We have chosen to employ real-time PCR in order to provide a relatively fast and inexpensive assay.Results: We show that the GR-regulated genes, GILZ and FKBP51, are upregulated in whole blood by treatment with dexamethasone and that LPS-induction of cytokines (IL-6 and TNFα) are repressed by dexamethasone in a dose responsive manner. There is considerable interpersonal variability in the maximum induction of these genes but little variation in the EC50 and IC50 concentrations. The regulation of the GR-induced genes differs throughout the day whereas the suppression of LPS-induced cytokines is not as sensitive to time of day.Conclusion: In all, this assay would provide a method to determine glucocorticoid receptor responsiveness in whole blood.Keywords: glucocorticoid responsiveness, gene regulation, nuclear receptor, GILZ, FKBP51, cytokines

Louise A Beveridge1, Peter G Davey2, Gabby Phillips3, Marion ET McMurdo11Ageing and Health, Division of Medical Sciences, Ninewells Hospital and Medical School, 2Health Informatics Centre, University of Dundee, 3Medical Microbiology Department, NHS TaysideAbstract: Urinary tract infections (UTI) occur frequently in older people. Unfortunately, UTI is commonly overdiagnosed and overtreated on the basis of nonspecific clinical signs and symptoms. The diagnosis of a UTI in the older patient requires the presence of new urinary symptoms, with or without systemic symptoms. Urinalysis is commonly used to diagnose infection in this population, however, the evidence for its use is limited. There is overwhelming evidence that asymptomatic bacteriuria should not be treated. Catheter associated urinary tract infection accounts for a significant amount of hospital-associated infection. Indwelling urinary catheters should be avoided where possible and alternatives sought. The use of narrow spectrum antimicrobial agents for urinary tract infection is advocated. Local guidelines are now widely used to reflect local resistance patterns and available agents. Guidelines need to be updated to reflect changes in antimicrobial prescribing and a move from broad to narrow spectrum antimicrobials.Keywords: urinary tract infection, elderly, review

Consumers face hard choices when they need cash quickly. Hard choices can lead to emotional or economically unsound decisions. Traditional classroom discussions of raising funds to pay for expenses usually focus on generating income, borrowing, or the sale of real and financial assets, if hardship is discussed at all. However, many families have additional "non-traditional" sources of cash. This article examines the use of life insurance surrenders and policy loans as a source of funds for both routine funding needs as well as a source of emergency financing. The article concludes with a review of the economic considerations of life insurance for Insurance and Financial Planning educators and students.

When a portfolio is not actively managed to maintain a fixed investment percentage in each asset but rather maintains a fixed number of shares for each asset, the portfolio weights will change over time because the market returns of the different assets will not be the same. Consequently, portfolio betas computed as a linear combination of asset betas, which is the usual practice, will be different from betas computed using regression techniques on portfolio returns as is done when evaluating individual assets and mutual funds. The alternative approaches can result in quite different beta statistics and, consequently, inconsistent decisions depending on which method is used.

The Monte Carlo method and related multiple imputation methods are traditionally used in math, physics and science to estimate and analyze data and are now becoming standard tools in analyzing business and financial problems. However, few sources explain the application of the Monte Carlo method for individuals and business professionals who are not immersed in the realm of mathematics or science. This paper introduces these Monte Carlo methods for the non-mathematician and business student, providing examples where the Monte Carlo method is applied when only small samples are available. Statistical analysis and statistically sound extrapolation of sample characteristics to the larger class population can be facilitated by applying Monte Carlo methods and the related concept of multiple imputation, which is also explained. Appendices provide step-by-step instructions for using two popular spreadsheet add-ins to run Monte Carlo based analysis.

While financial planning students are expected to be able to understand client retirement plans, subtle differences in cost-of-living adjustments can have major impact on the success of client retirement plans. This teaching note compares the cost-of-living adjustments in the largest government sponsored retirement systems and a hypothetical traditional privately sponsored plan. Using a Monte Carlo simulation, we estimate the impact on retirement experience from the different COLAs. These differences are large, with differing protection from future inflation and differing risk for running out of money during retirement. This teaching note will help instructors address Certified Financial Planner (CFP) Board Learning Outcome G.52 "Retirement Needs Analysis". The material may also be used in economics, human resources, public administration, and other classes addressing policy aspects of retirement plans.

Financial planning is an interdisciplinary field including finance and business law topics. Consequently, standard pedagogical resources often omit topics that fall between these fields. To address a key gap in educational materials for financial planning students and faculty, this article reviews recent regulatory developments for financial planning students, including strengthened fiduciary standards and FINRA suitability requirements. The discussion introduces FINRA 2090, FINRA 2111, the Uniform Prudent Investor Act, and other widely adopted fiduciary-oriented model laws. The article concludes with a discussion of implications for financial planning professors and students.

This paper presents the Agresti & Coull "Adjusted Wald" method for computing confidence intervals and margins of error for common proportion estimates. The presented method is easily implementable by business students and practitioners and provides more accurate estimates of proportions particularly in extreme samples and small sample situations. The proposed method may have particular applications to focus group analysis, industry benchmarking, and destructive testing sampling. The paper discusses a computational strategy and several comparison examples.

This paper illustrates how a third statistic from asset pricing models, the R-squared statistic, may have information that can help in portfolio construction. Using a traditional CAPM model in comparison to an 18-factor Arbitrage Pricing Style Model, a portfolio separation test is conducted. Portfolio returns and risk metrics are compared using data from the Dow Jones 30 stocks over the period January 2007 through October 2013. Various teaching points are discussed and illustrated.

We use the RIOTS4 sample of SMC field OB stars to determine the origin of massive runaways in this low-metallicity galaxy using Gaia proper motions, together with stellar masses obtained from RIOTS4 data. These data allow us to estimate the relative contributions of stars accelerated by the dynamical ejection vs binary supernova mechanisms, since dynamical ejection favors faster, more massive runaways, while SN ejection favors the opposite trend. In addition, we use the frequencies of classical OBe stars, high-mass X-ray binaries, and non-compact binaries to discriminate between the mechanisms. Our results show that the dynamical mechanism dominates by a factor of 2 – 3. This also implies a significant contribution from two-step acceleration that occurs when dynamically ejected binaries are followed by SN kicks. We update our published quantitative results from Gaia DR2 proper motions with new data from DR3. [ABSTRACT FROM AUTHOR]

An educational example is presented that is an effective teaching illustration to help students understand the difference between traditional CAPM beta and downside (or down-market) beta and why downside beta is a superior measure for use in personal financial planning investment policy statements.

The dual-beta model is a generalization of the CAPM model. In the dual-beta model, separate beta estimates are provided for up-market and down-market days. This paper uses the historical "Anscombe quartet" results which illustrated how very different datasets can produce the same regression coefficients to motivate a discussion of the dual-beta model. Using data from 39 mutual funds, it is shown how very different dual-beta models can lead to the same CAPM beta estimates, much like the Anscombe quartet scenarios.

Statistics, econometrics, investment analysis, and data analysis classes often review the calculation of several types of averages, including the arithmetic mean, geometric mean, harmonic mean, and various weighted averages. This note shows how each of these can be computed using a basic regression framework. By recognizing when a regression model is computing one of these averages, students can properly interpret these types of regressions. Similarly, by seeing how these averages fit into a common framework, students can have a better understanding of the different calculations.