The Antibiotic Resistance and Prescribing in European Children Project: A Neonatal and Pediatric Antimicrobial Web-based Point Prevalence Survey in 73 Hospitals Worldwide

BACKGROUND: The neonatal and pediatric antimicrobial point prevalence survey (PPS) of the Antibiotic Resistance and Prescribing in European Children project (http://www.arpecproject.eu/) aims to standardize a method for surveillance of antimicrobial use in children and neonates admitted to the hospital within Europe. This article describes the audit criteria used and reports overall country-specific proportions of antimicrobial use. An analytical review presents methodologies on antimicrobial use.

METHODS: A 1-day PPS on antimicrobial use in hospitalized children was organized in September 2011, using a previously validated and standardized method. The survey included all inpatient pediatric and neonatal beds and identified all children receiving an antimicrobial treatment on the day of survey. Mandatory data were age, gender, (birth) weight, underlying diagnosis, antimicrobial agent, dose and indication for treatment. Data were entered through a web-based system for data-entry and reporting, based on the WebPPS program developed for the European Surveillance of Antimicrobial Consumption project.

RESULTS: There were 2760 and 1565 pediatric versus 1154 and 589 neonatal inpatients reported among 50 European (n = 14 countries) and 23 non-European hospitals (n = 9 countries), respectively. Overall, antibiotic pediatric and neonatal use was significantly higher in non-European (43.8%; 95% confidence interval [CI]: 41.3-46.3% and 39.4%; 95% CI: 35.5-43.4%) compared with that in European hospitals (35.4; 95% CI: 33.6-37.2% and 21.8%; 95% CI: 19.4-24.2%). Proportions of antibiotic use were highest in hematology/oncology wards (61.3%; 95% CI: 56.2-66.4%) and pediatric intensive care units (55.8%; 95% CI: 50.3-61.3%).

CONCLUSIONS: An Antibiotic Resistance and Prescribing in European Children standardized web-based method for a 1-day PPS was successfully developed and conducted in 73 hospitals worldwide. It offers a simple, feasible and sustainable way of data collection that can be used globally.

New Method Based on the UNIFAC–VISCO Model for the Estimation of Ionic Liquids Viscosity Using the Experimental Data Recommended by Mathematical Gnostics

The viscosity of ionic liquids (ILs) has been modeled as a function of temperature and at atmospheric pressure using a new method based on the UNIFAC–VISCO method. This model extends the calculations previously reported by our group (see Zhao et al. J. Chem. Eng. Data 2016, 61, 2160–2169) which used 154 experimental viscosity data points of 25 ionic liquids for regression of a set of binary interaction parameters and ion Vogel–Fulcher–Tammann (VFT) parameters. Discrepancies in the experimental data of the same IL affect the quality of the correlation and thus the development of the predictive method. In this work, mathematical gnostics was used to analyze the experimental data from different sources and recommend one set of reliable data for each IL. These recommended data (totally 819 data points) for 70 ILs were correlated using this model to obtain an extended set of binary interaction parameters and ion VFT parameters, with a regression accuracy of 1.4%. In addition, 966 experimental viscosity data points for 11 binary mixtures of ILs were collected from literature to establish this model. All the binary data consist of 128 training data points used for the optimization of binary interaction parameters and 838 test data points used for the comparison of the pure evaluated values. The relative average absolute deviation (RAAD) for training and test is 2.9% and 3.9%, respectively.

Midwifery-led antenatal care models: mapping a systematic review to an evidence-based quality framework to identify key components and characteristics of care

Background: Implementing effective antenatal care models is a key global policy goal. However, the mechanisms of action of these multi-faceted models that would allow widespread implementation are seldom examined and poorly understood. In existing care model analyses there is little distinction between what is done, how it is done, and who does it. A new evidence-informed quality maternal and newborn care (QMNC) framework identifies key characteristics of quality care. This offers the opportunity to identify systematically the characteristics of care delivery that may be generalizable across contexts, thereby enhancing implementation. Our objective was to map the characteristics of antenatal care models tested in Randomised Controlled Trials (RCTs) to a new evidence-based framework for quality maternal and newborn care; thus facilitating the identification of characteristics of effective care.

Methods: A systematic review of RCTs of midwifery-led antenatal care models. Mapping and evaluation of these models’ characteristics to the QMNC framework using data extraction and scoring forms derived from the five framework components. Paired team members independently extracted data and conducted quality assessment using the QMNC framework and standard RCT criteria.

Results: From 13,050 citations initially retrieved we identified 17 RCTs of midwifery-led antenatal care models from Australia (7), the UK (4), China (2), and Sweden, Ireland, Mexico and Canada (1 each). QMNC framework scores ranged from 9 to 25 (possible range 0–32), with most models reporting fewer than half the characteristics associated with quality maternity care. Description of care model characteristics was lacking in many studies, but was better reported for the intervention arms. Organisation of care was the best-described component. Underlying values and philosophy of care were poorly reported.

Conclusions: The QMNC framework facilitates assessment of the characteristics of antenatal care models. It is vital to understand all the characteristics of multi-faceted interventions such as care models; not only what is done but why it is done, by whom, and how this differed from the standard care package. By applying the QMNC framework we have established a foundation for future reports of intervention studies so that the characteristics of individual models can be evaluated, and the impact of any differences appraised.

A Machine Learning Analysis of Twitter Sentiment to the Sandy Hook Shootings

Gun related violence is a complex issue and accounts for a large proportion of violent incidents. In the research reported in this paper, we set out to investigate the pro-gun and anti-gun sentiments expressed on a social media platform, namely Twitter, in response to the 2012 Sandy Hook Elementary School shooting in Connecticut, USA. Machine learning techniques are applied to classify a data corpus of over 700,000 tweets. The sentiments are captured using a public sentiment score that considers the volume of tweets as well as population. A web-based interactive tool is developed to visualise the sentiments and is available at this http://www.gunsontwitter.com. The key findings from this research are: (i) There are elevated rates of both pro-gun and anti-gun sentiments on the day of the shooting. Surprisingly, the pro-gun sentiment remains high for a number of days following the event but the anti-gun sentiment quickly falls to pre-event levels. (ii) There is a different public response from each state, with the highest pro-gun sentiment not coming from those with highest gun ownership levels but rather from California, Texas and New York.

Shooting for the Moon or Flying Too Near the Sun? Crossing the Value Rubicon in Precision Cancer Care

In his last two State of the Union addresses, President Barack Obama has focused on the need to deliver innovative solutions to improve human health, through the Precision Medicine Initiative in 2015 and the recently announced Cancer Moonshot in 2016. Precision cancer care has delivered clear patient benefit, but even for high-impact medicines such as imatinib mesylate (Glivec) in chronic myeloid leukaemia, the excitement at the success of this practice-changing clinical intervention has been somewhat tempered by the escalating price of this 'poster child' for precision cancer medicine (PCM). Recent studies on the costs of cancer drugs have revealed significant price differentials, which are a major causative factor behind disparities in the access to new generations of immunological and molecularly targeted agents. In this perspective, we will discuss the benefits of PCM to modern cancer control, but also emphasise how increasing costs are rendering the current approaches to integrating the paradigm of PCM unsustainable. Despite the ever increasing pressure on cancer and health care budgets, innovation will and must continue. Value-based frameworks offer one of the most rational approaches for policymakers committed to improving cancer outcomes through a public health approach.

An energy based force prediction method for UD-CFRP orthogonal machining

The machining of carbon fiber reinforced polymer (CFRP) composite presents a significant challenge to the industry, and a better understanding of machining mechanism is the essential fundament to enhance the machining quality. In this study, a new energy based analytical method was developed to predict the cutting forces in orthogonal machining of unidirectional CFRP with fiber orientations ranging from 0° to 75°. The subsurface damage in cutting was also considered. Thus, the total specific energy for cutting has been estimated along with the energy consumed for forming new surfaces, friction, fracture in chip formation and subsurface debonding. Experiments were conducted to verify the validity of the proposed model.