Overall and abdominal obesity and prostate cancer risk in a West African population: An analysis of the Ghana Prostate Study

Obesity has been associated with an increased risk of advanced prostate cancer. However, most studies have been conducted among North American and European populations. Prostate cancer mortality appears elevated in West Africa, yet risk factors for prostate cancer in this region are unknown. We thus examined the relationship between obesity and prostate cancer using a case-control study conducted in Accra, Ghana in 2004 to 2012. Cases and controls were drawn from a population-based sample of 1037 men screened for prostate cancer, yielding 73 cases and 964 controls. An additional 493 incident cases were recruited from the Korle-Bu Teaching Hospital. Anthropometric measurements were taken at enrollment. We used logistic regression to estimate odds ratios (ORs) and 95% confidence intervals (CIs) for associations between body mass index (BMI), waist circumference (WC), waist-hip ratio (WHR) and prostate cancer, adjusting for potential confounders. The mean BMI was 25.1 kg/m2 for cases and 24.3 kg/m2 for controls. After adjustment, men with BMI ≥ 30 kg/m2 had an increased risk of prostate cancer relative to men with BMI < 25 kg/m2 (OR 1.86, 95% CI 1.11-3.13). Elevated WC (OR 1.76, 95% CI 1.24-2.51) and WHR (OR 1.46, 95% CI 0.99-2.16) were also associated with prostate cancer. Associations were not modified by smoking status and were evident for low- and high-grade disease. These findings indicate that overall and abdominal obesity are positively associated with prostate cancer among men in Ghana, implicating obesity as a potentially modifiable risk factor for prostate cancer in this region

Relativistic Hydrodynamic Evolutions with Black Hole Excision

We present a numerical code designed to study astrophysical phenomena involving dynamical spacetimes containing black holes in the presence of relativistic hydrodynamic matter. We present evolutions of the collapse of a fluid star from the onset of collapse to the settling of the resulting black hole to a final stationary state. In order to evolve stably after the black hole forms, we excise a region inside the hole before a singularity is encountered. This excision region is introduced after the appearance of an apparent horizon, but while a significant amount of matter remains outside the hole. We test our code by evolving accurately a vacuum Schwarzschild black hole, a relativistic Bondi accretion flow onto a black hole, Oppenheimer-Snyder dust collapse, and the collapse of nonrotating and rotating stars. These systems are tracked reliably for hundreds of M following excision, where M is the mass of the black hole. We perform these tests both in axisymmetry and in full 3+1 dimensions. We then apply our code to study the effect of the stellar spin parameter J/M^2 on the final outcome of gravitational collapse of rapidly rotating n = 1 polytropes. We find that a black hole forms only if J/M^2<1, in agreement with previous simulations. When J/M^2>1, the collapsing star forms a torus which fragments into nonaxisymmetric clumps, capable of generating appreciable ``splash'' gravitational radiation.Comment: 17 pages, 14 figures, submitted to PR

Impact of H1N1 on Socially Disadvantaged Populations: Systematic Review

The burden of H1N1 among socially disadvantaged populations is unclear. We aimed to synthesize hospitalization, severe illness, and mortality data associated with pandemic A/H1N1/2009 among socially disadvantaged populations.Studies were identified through searching MEDLINE, EMBASE, scanning reference lists, and contacting experts. Studies reporting hospitalization, severe illness, and mortality attributable to laboratory-confirmed 2009 H1N1 pandemic among socially disadvantaged populations (e.g., ethnic minorities, low-income or lower-middle-income economy countries [LIC/LMIC]) were included. Two independent reviewers conducted screening, data abstraction, and quality appraisal (Newcastle Ottawa Scale). Random effects meta-analysis was conducted using SAS and Review Manager.Sixty-two studies including 44,777 patients were included after screening 787 citations and 164 full-text articles. The prevalence of hospitalization for H1N1 ranged from 17-87% in high-income economy countries (HIC) and 11-45% in LIC/LMIC. Of those hospitalized, the prevalence of intensive care unit (ICU) admission and mortality was 6-76% and 1-25% in HIC; and 30% and 8-15%, in LIC/LMIC, respectively. There were significantly more hospitalizations among ethnic minorities versus non-ethnic minorities in two studies conducted in North America (1,313 patients, OR 2.26 [95% CI: 1.53-3.32]). There were no differences in ICU admissions (n = 8 studies, 15,352 patients, OR 0.84 [0.69-1.02]) or deaths (n = 6 studies, 14,757 patients, OR 0.85 [95% CI: 0.73-1.01]) among hospitalized patients in HIC. Sub-group analysis indicated that the meta-analysis results were not likely affected by confounding. Overall, the prevalence of hospitalization, severe illness, and mortality due to H1N1 was high for ethnic minorities in HIC and individuals from LIC/LMIC. However, our results suggest that there were little differences in the proportion of hospitalization, severe illness, and mortality between ethnic minorities and non-ethnic minorities living in HIC

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages

Supernova remnants: the X-ray perspective

Supernova remnants are beautiful astronomical objects that are also of high scientific interest, because they provide insights into supernova explosion mechanisms, and because they are the likely sources of Galactic cosmic rays. X-ray observations are an important means to study these objects.And in particular the advances made in X-ray imaging spectroscopy over the last two decades has greatly increased our knowledge about supernova remnants. It has made it possible to map the products of fresh nucleosynthesis, and resulted in the identification of regions near shock fronts that emit X-ray synchrotron radiation. In this text all the relevant aspects of X-ray emission from supernova remnants are reviewed and put into the context of supernova explosion properties and the physics and evolution of supernova remnants. The first half of this review has a more tutorial style and discusses the basics of supernova remnant physics and thermal and non-thermal X-ray emission. The second half offers a review of the recent advances.The topics addressed there are core collapse and thermonuclear supernova remnants, SN 1987A, mature supernova remnants, mixed-morphology remnants, including a discussion of the recent finding of overionization in some of them, and finally X-ray synchrotron radiation and its consequences for particle acceleration and magnetic fields.Comment: Published in Astronomy and Astrophysics Reviews. This version has 2 column-layout. 78 pages, 42 figures. This replaced version has some minor language edits and several references have been correcte

Can voluntary apps reduce mobile phone use while driving? (RACV Research Report 19/01)

Free to read on publisher's website The high prevalence use of mobile phones in Australia has led to concerns about the rates and impact of driver distraction, and how technology can be harnessed as an effective countermeasure. In recent years, mobile phone apps have been designed to act as voluntary ‘workload managers’, which work to prevent distracted driving due to mobile phone use. This study investigates the potential use of voluntary apps through four empirical studies utilising both qualitative and quantitative methods – a driver acceptance assessment study, an expert-based risk assessment study, an online-based case-scenario analysis, and an in-vehicle study. Results demonstrate a general willingness to use voluntary apps to reduce distraction, especially among high-risk drivers who report higher levels of visual-manual interaction with their mobile phones. Despite the general acceptance of voluntary apps, there was still reluctance among participants to give up certain benefits of mobile phones. During the in-vehicle survey, there were self-reported reductions in phone use and decreased mental demand while participants were using the voluntary app. However, participants’ intentions to use the voluntary app were influenced by the features and performance of the apps, such that they showed less intention to use the app if they had a negative user experience. Overall, there is value in promoting the benefits of voluntary app use to prevent driving while distracted by a mobile phone. However, only voluntary apps that function reliably, and provide a satisfactory level of integration with the vehicle and the functions drivers use, should be promoted. This report also proposes standard guidelines and recommendations to ensure users make the best use of voluntary apps, and to encourage the relevant organisations to consider advocacy or incentive-based approaches to encourage use