Appropriateness of Antibiotic Prescribing in United States Children’s Hospitals: A National Point Prevalence Survey

BACKGROUND: Studies estimate that 30-50% of antibiotics prescribed for hospitalized patients are inappropriate, but pediatric data are limited. Characterization of inappropriate prescribing practices for children are needed to guide pediatric antimicrobial stewardship. METHODS: Cross-sectional analysis of antibiotic prescribing at 32 US children's hospitals. Subjects included hospitalized children with ≥1 antibiotic order at 0800 on one day per calendar quarter, over six quarters (Quarter 3 2016 - Quarter 4 2017). Antimicrobial stewardship program (ASP) physicians and/or pharmacists used a standardized survey to collect data on antibiotic orders and evaluate appropriateness. The primary outcome was the percentage of antibiotics prescribed for infectious use that were classified as suboptimal, defined as inappropriate or needing modification. RESULTS: Of 34 927 children hospitalized on survey days, 12 213 (35.0%) had ≥1 active antibiotic order. Among 11 784 patients receiving antibiotics for infectious use, 25.9% were prescribed ≥1 suboptimal antibiotic. Of the 17 110 antibiotic orders prescribed for infectious use, 21.0% were considered suboptimal. Most common reasons for inappropriate use were bug-drug mismatch (27.7%), surgical prophylaxis >24 hours (17.7%), overly broad empiric therapy (11.2%), and unnecessary treatment (11.0%). The majority of recommended modifications were to stop (44.7%) or narrow (19.7%) the drug. ASPs would not have routinely reviewed 46.1% of suboptimal orders. CONCLUSIONS: Across 32 children's hospitals, approximately 1 in 3 hospitalized children are receiving one or more antibiotics at any given time. One quarter of these children are receiving suboptimal therapy, and nearly half of suboptimal use is not captured by current ASP practices

Conceptualising spaced learning in health professions education: A scoping review

Objectives To investigate the definitions and applications of 'spaced learning' and to propose future directions for advancing its study and practice in health professions education. Method The authors searched five online databases for articles published on spaced learning in health professions education prior to February 2018. Two researchers independently screened articles for eligibility with set inclusion criteria. They extracted and analysed key data using both quantitative and qualitative methods. Results Of the 2972 records retrieved, 120 articles were included in the review. More than 90% of these articles were published in the last 10 years. The definition of spaced learning varied widely and was often not theoretically grounded. Spaced learning was applied in distinct contexts, including online learning, simulation training and classroom settings. There was a large variety of spacing formats, ranging from dispersion of information or practice on a single day, to intervals lasting several months. Generally, spaced learning was implemented in practice or testing phases and rarely during teaching. Conclusions Spaced learning is infrequently and poorly defined in the health professions education literature. We propose a comprehensive definition of spaced learning and emphasise that detailed descriptions of spacing formats are needed in future research to facilitate the operationalisation of spaced learning research and practice in health professions education.Cardiolog

Seroepidemiology of coxsackievirus A6, coxsackievirus A16, and Enterovirus 71 infections among children and adolescents in Singapore, 2008-2010

10.1371/journal.pone.0127999PLoS ONE105e012799

Simulating residential demand response: Improving socio-technical assumptions in activity-based models of energy demand

Demand response is receiving increasing interest as a new form of flexibility within lowcarbon power systems. Energy models are an important tool to assess the potential capability of demand side contributions. This paper critically reviews the assumptions in current models and introduces a new conceptual framework to better facilitate such an assessment. We propose three dimensions along which change could occur, namely technology, activities and service expectations. Using this framework, the socio-technical assumptions underpinning ‘bottom-up’ activity-based energy demand models are identified and a number of shortcomings are discussed. First, links between appliance usage and activities are not evidence-based. We propose new data collection approaches to address this gap. Second, aside from thermal comfort, service expectations, which can be an important source of flexibility, are underrepresented and their inclusion into demand models would improve their predicative power in this area. Finally, flexibility can be present over a range of time scales, from immediate responses, to longer term trends. Longitudinal time use data from participants in demand response schemes may be able to illuminate these. The recommendations of this paper seek to enhance the current state-of-the-art in activity-based models and to provide useful tools for the assessment of demand response