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Study Objectives: To examine the cost-effectiveness and potential net monetary benefit (NMB) of a fully automated digital cognitive behavioral therapy (CBT) intervention for insomnia compared with no insomnia treatment in the United States (US). Similar relative comparisons were made for pharmacotherapy and clinician-delivered CBT (individual and group)., Methods: We simulated a Markov model of 100,000 individuals using parameters calibrated from the literature including direct (treatment) and indirect costs (e.g. insomnia-related healthcare expenditure and lost workplace productivity). Health utility estimates were converted into quality-adjusted life years (QALYs) and one QALY was worth $50,000. Simulated individuals were randomized equally to one of five arms (digital CBT, pharmacotherapy, individual CBT, group CBT, or no insomnia treatment). Sensitivity was assessed by bootstrapping the calibrated parameters. Cost estimates were expressed in 2019 US dollars., Results: Digital CBT was cost beneficial when compared with no insomnia treatment and had a positive NMB of $681.06 (per individual over 6 months). Bootstrap sensitivity analysis demonstrated that the NMB was positive in 94.7% of simulations. Relative to other insomnia treatments, digital CBT was the most cost-effective treatment because it generated the smallest incremental cost-effectiveness ratio (-$3,124.73)., Conclusions: Digital CBT was the most cost-effective insomnia treatment followed by group CBT, pharmacotherapy, and individual CBT. It is financially prudent and beneficial from a societal perspective to utilize automated digital CBT to treat insomnia at a population scale., (© Sleep Research Society 2020. Published by Oxford University Press on behalf of the Sleep Research Society. All rights reserved. For permissions, please e-mail journals.permissions@oup.com.)

Escherichia coli is the most common cause of human and canine urinary tract infection (UTI). Clonal groups, often with high levels of antimicrobial resistance, are a major component of the E. coli population that causes human UTI. While little is known about the population structure of E. coli that causes UTI in dogs, there is evidence that dogs and humans can share fecal strains of E. coli and that human-associated strains can cause disease in dogs. In order to better characterize the E. coli strains that cause canine UTI, we analyzed 295 E. coli isolates obtained from canine urine samples from five veterinary diagnostic laboratories and analyzed their multilocus sequence types, phenotypic and genotypic antimicrobial resistance profiles, and virulence-associated gene repertoires. Sequence type 372 (ST372), an infrequent human pathogen, was the predominant sequence type in dogs at all locations. Extended-spectrum β-lactamase-producing isolates with bla CTX-M genes were uncommon in canine isolates but when present were often associated with sequence types that have been described in human infections. This provides support for occasional cross-host-species sharing of strains that cause extraintestinal disease and highlights the importance of understanding the role of companion animals in the overall transmission patterns of extraintestinal pathogenic E. coli ., (Copyright © 2018 American Society for Microbiology.)

Objective: To measure unhealthy aerosol materials in an Emergency Department (ED) and identify their sources for mitigation efforts., Background: Based on pilot findings of elevated ED particulate matter (PM) levels, investigators hypothesized that unhealthy aerosol materials derive from exogenous (vehicular) sources at ambulance receiving entrances., Methods: The Aerosol Environmental Toxicity in Healthcare-related Exposure and Risk program was conducted as an observational study. Calibrated sensors monitored PM and toxic gases at Ambulance Triage Exterior (ATE), Ambulance Triage Desk (ATD), and control Public Triage Desk (PTD) on a 3/3/3-day cycle. Cassette sampling characterized PM; meteorological and ambulance traffic data were logged. Descriptive and multiple linear regression analyses assessed for interactions between aerosol material levels, location, temporal variables, ambulance activity, and meteorological factors., Results: Sensors acquired 93,682 PM0.3, 90,250 PM2.5, and 93,768 PM5 measurements over 366 days to generate a data set representing at least 85.6% of planned measurements. PM0.3, PM2.5, and PM5 mean counts were lowest in PTD; 56%, 224%, and 223% higher in ATD; and 996%, 200%, and 63% higher in ATE, respectively (all p < .001). Qualitative analyses showed similar PM compositions in ATD and ATE. On multiple linear regression analysis, PM0.3 counts correlated primarily with location; PM2.5 and PM5 counts correlated most strongly with location and ambulance presence. PM < 2.5 and toxic gas concentrations at ATD and PTD patient care areas did not exceed hazard levels; PM0.3 counts did not have formal safety thresholds for comparison., Conclusions: Higher levels of PM were linked with ED ambulance areas, although their health impact is unclear., (© The Author(s) 2015.)