Emergency department ultrasound probe infection control: Challenges and solutions

Point-of-care ultrasound (US) has become a cornerstone in the diagnosis and treatment of patients in the emergency department (ED). Despite the beneficial impact on patient care, concern exists over repeat use of probes and the role as a vector for pathogen transmission. US probes are used for various applications, with the level of infection risk, based on the Spaulding Classification, ranging from noncritical with common practice to semicritical with endocavitary probes. To date, the most closely studied organisms are Staphylococcus aureus and human papilloma virus. Current evidence does confirm probe colonization but has not established a causative role in human infection. Based on current literature, US use during invasive procedures remains an infection control concern, but routine use on intact skin does not appear to cause significant risk to patients. Various barrier methods are available, each with indications based on extent of procedure and likelihood of contact with mucosal surfaces. Additionally, chemical cleansing methods have been shown to be effective in limiting probe contamination after use. New technologies utilizing ultraviolet light are available and effective but not widely used in the ED setting. As our understanding of the critical factors in US probe cleaning and disinfection improves, it is important to assess the challenges found in our current practice and to identify potential solutions to improve practices and procedures in infection control across the spectrum of US probe use in various applications in the ED. This article serves as a summary of the current literature available on infection control topics with the utilization of point-of-care US, and discusses challenges and potential solutions to improve the current practice of probe-related infection control

The Grizzly, February 16, 2017

First Semester of Philadelphia Experience a Success • College Unveils Construction Plans for The Commons • Peer Advocates Prepare for the Vagina Monologues • Q&A with Author and Activist • By the Way, Meet Vera Stark Tackles Race in the Glamorous World of 1930s Hollywood • History Professor Hugh Clark Reflects on Time at Ursinus • Opinions: La La Land Delivers on Promise of Nostalgia; Graduating Early Should Not Translate to Exclusion • Golf Ready to Swing Into Spring • Three Champions Crowned; Wrestling Advances to Regionalshttps://digitalcommons.ursinus.edu/grizzlynews/1660/thumbnail.jp

Inferring school district learning modalities during the COVID-19 pandemic with a hidden Markov model

In this study, learning modalities offered by public schools across the United States were investigated to track changes in the proportion of schools offering fully in-person, hybrid and fully remote learning over time. Learning modalities from 14,688 unique school districts from September 2020 to June 2021 were reported by Burbio, MCH Strategic Data, the American Enterprise Institute's Return to Learn Tracker and individual state dashboards. A model was needed to combine and deconflict these data to provide a more complete description of modalities nationwide. A hidden Markov model (HMM) was used to infer the most likely learning modality for each district on a weekly basis. This method yielded higher spatiotemporal coverage than any individual data source and higher agreement with three of the four data sources than any other single source. The model output revealed that the percentage of districts offering fully in-person learning rose from 40.3% in September 2020 to 54.7% in June of 2021 with increases across 45 states and in both urban and rural districts. This type of probabilistic model can serve as a tool for fusion of incomplete and contradictory data sources in support of public health surveillance and research efforts.Comment: 25 pages, 4 figure

Impact of exposure measurement error in air pollution epidemiology: effect of error type in time-series studies

<p>Abstract</p> <p>Background</p> <p>Two distinctly different types of measurement error are Berkson and classical. Impacts of measurement error in epidemiologic studies of ambient air pollution are expected to depend on error type. We characterize measurement error due to instrument imprecision and spatial variability as multiplicative (i.e. additive on the log scale) and model it over a range of error types to assess impacts on risk ratio estimates both on a per measurement unit basis and on a per interquartile range (IQR) basis in a time-series study in Atlanta.</p> <p>Methods</p> <p>Daily measures of twelve ambient air pollutants were analyzed: NO₂, NO_x, O₃, SO₂, CO, PM₁₀mass, PM_2.5mass, and PM_2.5components sulfate, nitrate, ammonium, elemental carbon and organic carbon. Semivariogram analysis was applied to assess spatial variability. Error due to this spatial variability was added to a reference pollutant time-series on the log scale using Monte Carlo simulations. Each of these time-series was exponentiated and introduced to a Poisson generalized linear model of cardiovascular disease emergency department visits.</p> <p>Results</p> <p>Measurement error resulted in reduced statistical significance for the risk ratio estimates for all amounts (corresponding to different pollutants) and types of error. When modelled as classical-type error, risk ratios were attenuated, particularly for primary air pollutants, with average attenuation in risk ratios on a per unit of measurement basis ranging from 18% to 92% and on an IQR basis ranging from 18% to 86%. When modelled as Berkson-type error, risk ratios per unit of measurement were biased away from the null hypothesis by 2% to 31%, whereas risk ratios per IQR were attenuated (i.e. biased toward the null) by 5% to 34%. For CO modelled error amount, a range of error types were simulated and effects on risk ratio bias and significance were observed.</p> <p>Conclusions</p> <p>For multiplicative error, both the amount and type of measurement error impact health effect estimates in air pollution epidemiology. By modelling instrument imprecision and spatial variability as different error types, we estimate direction and magnitude of the effects of error over a range of error types.</p

Standalone vertex finding in the ATLAS muon spectrometer

A dedicated reconstruction algorithm to find decay vertices in the ATLAS muon spectrometer is presented. The algorithm searches the region just upstream of or inside the muon spectrometer volume for multi-particle vertices that originate from the decay of particles with long decay paths. The performance of the algorithm is evaluated using both a sample of simulated Higgs boson events, in which the Higgs boson decays to long-lived neutral particles that in turn decay to bbar b final states, and pp collision data at √s = 7 TeV collected with the ATLAS detector at the LHC during 2011

Measurements of Higgs boson production and couplings in diboson final states with the ATLAS detector at the LHC

Measurements are presented of production properties and couplings of the recently discovered Higgs boson using the decays into boson pairs, H →γ γ, H → Z Z∗ →4l and H →W W∗ →lνlν. The results are based on the complete pp collision data sample recorded by the ATLAS experiment at the CERN Large Hadron Collider at centre-of-mass energies of √s = 7 TeV and √s = 8 TeV, corresponding to an integrated luminosity of about 25 fb−1. Evidence for Higgs boson production through vector-boson fusion is reported. Results of combined fits probing Higgs boson couplings to fermions and bosons, as well as anomalous contributions to loop-induced production and decay modes, are presented. All measurements are consistent with expectations for the Standard Model Higgs boson