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The 2019 UK General Election had seismic consequences for British politics. After three years of political turmoil following the 2016 referendum on Britain’s membership of the European Union (EU), the 2019 election marked a victory for the Leave side of the Brexit debate, putting to rest questions of a second referendum and any chance of Parliament blocking the Withdrawal Bill. The United Kingdom left the EU on January 31, 2020. Although there were clear consequences for Britain’s EU membership, there is debate about whether 2019 was a “Brexit election” (Prosser 2020)—even a critical election (Green 2021)—or the continuation of long-term realignments in British politics (Cutts et al. 2020; Jennings and Stoker 2017). By most accounts, Brexit dominated the 2019 election as a political issue, but whether this represents a key moment in a process of realignment of voters in Britain remains to be seen.
The IntCal family of radiocarbon (14C) calibration curves is based on research spanning more than three decades. The IntCal group have collated the 14C and calendar age data (mostly derived from primary publications with other types of data and meta-data) and, since 2010, made them available for other sorts of analysis through an open-access database. This has ensured transparency in terms of the data used in the construction of the ratified calibration curves. As the IntCal database expands, work is underway to facilitate best practice for new data submissions, make more of the associated metadata available in a structured form, and help those wishing to process the data with programming languages such as R, Python, and MATLAB. The data and metadata are complex because of the range of different types of archives. A restructured interface, based on the “IntChron” open-access data model, includes tools which allow the data to be plotted and compared without the need for export. The intention is to include complementary information which can be used alongside the main 14C series to provide new insights into the global carbon cycle, as well as facilitating access to the data for other research applications. Overall, this work aims to streamline the generation of new calibration curves.
We report on frequency doubling of high-energy, high repetition rate ns pulses from a cryogenically gas cooled multi-slab ytterbium-doped yttrium aluminum garnet laser system, Bivoj/DiPOLE, using a type-I phase matched lithium triborate crystal. We achieved conversion to 515 nm with energy of 95 J at repetition rate of 10 Hz and conversion efficiency of 79%. High conversion efficiency was achieved due to successful depolarization compensation of the fundamental input beam.
To evaluate the incidence of a candidate definition of healthcare facility-onset, treated Clostridioides difficile (CD) infection (cHT-CDI) and to identify variables and best model fit of a risk-adjusted cHT-CDI metric using extractable electronic heath data.
We analyzed 9,134,276 admissions from 265 hospitals during 2015–2020. The cHT-CDI events were defined based on the first positive laboratory final identification of CD after day 3 of hospitalization, accompanied by use of a CD drug. The generalized linear model method via negative binomial regression was used to identify predictors. Standardized infection ratios (SIRs) were calculated based on 2 risk-adjusted models: a simple model using descriptive variables and a complex model using descriptive variables and CD testing practices. The performance of each model was compared against cHT-CDI unadjusted rates.
The median rate of cHT-CDI events per 100 admissions was 0.134 (interquartile range, 0.023–0.243). Hospital variables associated with cHT-CDI included the following: higher community-onset CDI (CO-CDI) prevalence; highest-quartile length of stay; bed size; percentage of male patients; teaching hospitals; increased CD testing intensity; and CD testing prevalence. The complex model demonstrated better model performance and identified the most influential predictors: hospital-onset testing intensity and prevalence, CO-CDI rate, and community-onset testing intensity (negative correlation). Moreover, 78% of the hospitals ranked in the highest quartile based on raw rate shifted to lower percentiles when we applied the SIR from the complex model.
Hospital descriptors, aggregate patient characteristics, CO-CDI burden, and clinical testing practices significantly influence incidence of cHT-CDI. Benchmarking a cHT-CDI metric is feasible and should include facility and clinical variables.
In a survey of infection prevention programs, leaders reported frequent clinical and infection prevention practice modifications to avoid coronavirus disease 2019 (COVID-19) exposure that exceeded national guidance. Future pandemic responses should emphasize balanced approaches to precautions, prioritize educational campaigns to manage safety concerns, and generate an evidence-base that can guide appropriate infection prevention practices.
To examine temporal changes in coverage with a complete primary series of coronavirus disease 2019 (COVID-19) vaccination and staffing shortages among healthcare personnel (HCP) working in nursing homes in the United States before, during, and after the implementation of jurisdiction-based COVID-19 vaccination mandates for HCP.
Sample and setting:
HCP in nursing homes from 15 US jurisdictions.
We analyzed weekly COVID-19 vaccination data reported to the Centers for Disease Control and Prevention’s National Healthcare Safety Network from June 7, 2021, through January 2, 2022. We assessed 3 periods (preintervention, intervention, and postintervention) based on the announcement of vaccination mandates for HCP in 15 jurisdictions. We used interrupted time-series models to estimate the weekly percentage change in vaccination with complete primary series and the odds of reporting a staffing shortage for each period.
Complete primary series vaccination among HCP increased from 66.7% at baseline to 94.3% at the end of the study period and increased at the fastest rate during the intervention period for 12 of 15 jurisdictions. The odds of reporting a staffing shortage were lowest after the intervention.
These findings demonstrate that COVID-19 vaccination mandates may be an effective strategy for improving HCP vaccination coverage in nursing homes without exacerbating staffing shortages. These data suggest that mandates can be considered to improve COVID-19 coverage among HCP in nursing homes to protect both HCP and vulnerable nursing home residents.
Community-based, public, not-for-profit teaching hospital in the southeastern United States.
Adult inpatients with a positive urine culture and the absence of urinary tract infection signs and symptoms.
Implementation of a microbiology comment nudge on urine cultures.
In total, 204 patients were included in the study. Antibiotics were less likely to be continued beyond 72 hours in the postimplementation group: 57 (55%) of 104 versus 38 (38%) of 100 (P = .016). They were less likely to have antibiotics continued beyond 48 hours: 60 (58%) of 104 versus 43 (43%) of 100 (P = .036). They were also less likely to have antibiotics prescribed at discharge 35 (34%) of 104 versus 20 (20%) of 100 (P = .028). In addition, they had fewer total antibiotic days of therapy: 4 (IQR, 1–6) versus 1 (IQR, 0–6) (P = .022).
Microbiology comment nudging may contribute to less antibiotic utilization in patients with ASB.
We demonstrate that the desirability bias, the elevation of the estimated likelihood of a preferred event, can be due in part to the desire for consistency between the preference for the favored event and its predicted likelihood. An experiment uses a participant’s favorite team in Major League Baseball games and a recently devised method for priming the consistency goal. When preference is the first response, priming cognitive consistency moves prediction toward greater agreement with that preference, thereby increasing the desirability bias. In contrast, when prediction is the first response, priming cognitive consistency facilitates greater agreement with the factual information for each game. This increases the accuracy of the prediction and reduces the desirability bias.