172. Impact of COVID-19 Pandemic on Healthcare-associated Infections (HAIs) in a Large Network of Hospitals

Background The COVID-19 pandemic had a considerable impact on US healthcare systems, straining hospital resources, staff, and operations. Our objective was to evaluate the impact of COVID-19 pandemic on incidence and trends of healthcare-associated infections (HAIs) in a network of hospitals. Methods This was a retrospective review of central-line-associated bloodstream infections (CLABSIs), catheter-associated urinary tract infections (CAUTIs), C. difficile infections (CDI), and ventilator-associated events (VAE) in 51 hospitals from 2018 to 2021. Descriptive statistics were reported as mean hospital-level monthly incidence rates (IR) and compared using Poisson regression GEE models with period as the only covariate. Segmented regression (SR) analysis was performed to estimate changes in monthly IR of CAUTIs, CLABSIs and CDI in the baseline period (01/2018 – 02/2020) and the Pandemic period (03/2020 – 03/2021). SR model was not appropriate for VAE based on the plot. All models were constructed using SAS v.9.4 (SAS Institute, Cary NC). Results Compared to the baseline period, CLABSIs increased significantly by 50% from 0.6 to 0.9/ 1000 catheter days (P< 0. 001). In contrast, no significant changes were identified for CAUTI (P=0.87). Similar trends were seen in SR models for CLABSI and CAUTI (Figures 1, 2 and Table 1). While overall CDIs decreased significantly from 3.5 to 2.5/10,000 patient days in the pandemic period (P< 0.001), SR model showed increasing pandemic trend change (Figure 3). VAEs increased > 700% from 6.9 to 59.7/1000 ventilator days (P=0.15), but displayed considerable variation during the pandemic period (Figure 4). Compared to baseline period, there was a significant increase in central line days (647 vs 677, P=0.02), ventilator days (156 vs 215, P< 0.001), but no change in urinary catheter days (675 vs 686, P=0.32) during the pandemic period. Figure 1: Segmented Regression model showing baseline and pandemic period trends of CLABSI Figure 2: Segmented Regression model showing baseline and pandemic period trends of CAUTI Figure 3: Segmented Regression model showing baseline and pandemic period trends of C. difficile (HO-CDI) infections Conclusion The COVID-19 pandemic was associated with substantial increases in CLABSIs and VAEs, no change in CAUTIs, and an increasing trend in CDI incidence. These variations in trends of different HAIs are likely due, in part, to unique characteristics of the underlying infection, resource shortages, staffing concerns, increased device use, changes in testing practices, and the limitations of surveillance definitions. Figure 4: Trend of Ventilator-Associated Events (VAE) in the baseline and pandemic period (Segmented Regression model not appropriate) Disclosures Sonali D. Advani, MBBS, MPH, Nothing to disclose David J. Weber, MD, MPH, Merck (Individual(s) Involved: Self): Consultant; PDI (Individual(s) Involved: Self): Consultant; Pfizer (Individual(s) Involved: Self): Consultant; Sanofi (Individual(s) Involved: Self): Consultant; UVinnovators (Individual(s) Involved: Self): Consultant

Exposure to respirable dust among workers fabricating aluminium trihydroxide-containing synthetic countertops

The aim of this study is to characterize personal exposure of workers to respirable particulate matter (PM) generated in cutting and other fabrication activities when fabricating acryl polymer/aluminium trihydroxide synthetic countertops. We collected 29 personal full-day samples of respirable PM from three workers in a small private workshop. We tested differences between- and within-worker variances of mass concentrations using the Kruskall-Wallis test. We used segmented regression to test the means and medians 15-min interval concentrations changes over time and to identify a breakpoint. Respirable PM concentrations ranged nearly 100-fold, from 0.280 to 25.4 mg/m3 with a median of 2.0 mg/m3 (1-min concentrations from 13,920 data points). There were no statistical difference in daily median or geometric mean concentrations among workers, whereas the concentrations were significantly higher on days with three versus two workers present. The 15-min median concentrations (n = 974 measures) increased until 2.35 h (beta 0.177; p < 0.05), representing a 0.70 mg increase in exposure per hour. This was followed by a plateau in concentrations. The high levels of respirable PM we observed among workers fabricating aluminium trihydroxide-containing synthetic countertops highlight an unmet early prevention need.

Spatiotemporal inflection points in human running: Effects of training level and athletic modality

The effect of the different training regimes and histories on the spatiotemporal characteristics of human running was evaluated in four groups of subjects who had different histories of engagement in running-specific training; sprinters, distance runners, active athletes, and sedentary individuals. Subjects ran at a variety of velocities, ranging from slowest to fastest, over 30 trials in a random order. Group averages of maximal running velocities, ranked from fastest to slowest, were: sprinters, distance runners, active athletes, and sedentary individuals. The velocity-cadence-step length (V-C-S) relationship, made by plotting step length against cadence at each velocity tested, was analyzed with the segmented regression method, utilizing two regression lines. In all subject groups, there was a critical velocity, defined as the inflection point, in the relationship. In the velocity ranges below and above the inflection point (slower and faster velocity ranges), velocity was modulated primarily by altering step length and by altering cadence, respectively. This pattern was commonly observed in all four groups, not only in sprinters and distance runners, as has already been reported, but also in active athletes and sedentary individuals. This pattern may reflect an energy saving strategy. When the data from all groups were combined, there were significant correlations between maximal running velocity and both running velocity and step length at the inflection point. In spite of the wide variety of athletic experience of the subjects, as well as their maximum running velocities, the inflection point appeared at a similar cadence (3.0 ± 0.2 steps/s) and at a similar relative velocity (65–70%Vmax). These results imply that the influence of running-specific training on the inflection point is minimal.

Efficient Change-Points Detection For Genomic Sequences Via Cumulative Segmented Regression

Motivation Knowing the number and the exact locations of multiple change points in genomic sequences serves several biological needs. The cumulative segmented algorithm (cumSeg) has been recently proposed as a computationally efficient approach for multiple change-points detection, which is based on a simple transformation of data and provides results quite robust to model mis-specifications. However, the errors are also accumulated in the transformed model so that heteroscedasticity and serial correlation will show up, and thus the variations of the estimated change points will be quite different, while the locations of the change points should be of the same importance in the original genomic sequences. Results In this study, we develop two new change-points detection procedures in the framework of cumulative segmented regression. Simulations reveal that the proposed methods not only improve the efficiency of each change point estimator substantially but also provide the estimators with similar variations for all the change points. By applying these proposed algorithms to Coriel and SNP genotyping data, we illustrate their performance on detecting copy number variations. Supplementary information The proposed algorithms are implemented in R program and are available at Bioinformatics online.

Are American Presidents Becoming Less Rhetorically Complex? Evaluating the Integrative Complexity of Joe Biden and Donald Trump in Historical Context

Are American political leaders becoming simpler in their rhetoric? To evaluate, in the present study we place the two most recent U.S. presidents’ integrative complexity against a historical context for three different types of comparable materials: Presidential Debates, Inaugural Addresses, and State of the Union (SOTU) speeches. Results overwhelmingly suggest that both Joe Biden and Donald Trump are historically simple when compared to the typical president, and that is true both across parties and within their own political party. Further, segmented regression analyses suggest that part of the reason for Biden’s and Trump’s low complexity is the continuation of an ongoing historical decline in complexity among Presidents that began in 1960. However, each president uniquely defies this trend on one material type: Biden is a historical outlier for his low-complexity debates, and Trump is a historical outlier for his low-complexity inauguration speech. Taken as a whole, these data suggest that although American presidents have been declining in complexity, both Biden and Trump are nonetheless uniquely low in complexity in some ways – possibly for reasons that are different for each president.

Healthcare Quality Improvement Analytics: An Example Using Computerized Provider Order Entry

Evaluation of sustainability after quality improvement (QI) projects in healthcare settings is an essential part of monitoring and future QI planning. With limitations in adopting quasi-experimental study design in real-world practice, healthcare professionals find it challenging to present the sustained effect of QI changes effectively. To provide quantitative methodological approaches for demonstrating the sustainability of QI projects for healthcare professionals, we conducted data analyses based on a QI project to improve the computerized provider order entry system to reduce patients’ dosing frequencies in Korea. Data were collected for 5 years: 24-month pre-intervention, 12-month intervention, and 24-month post-intervention. Then, analytic approaches including control chart, Analysis of Variance (ANOVA), and segmented regression were performed. The control chart intuitively displayed how the outcomes changed over the entire period, and ANOVA was used to test whether the outcomes differed between groups. Last, segmented regression analysis was conducted to evaluate longitudinal effects of interventions over time. We found that the impact of QI projects in healthcare settings should be initiated following the Plan–Do–Study–Act cycle and evaluated long-term effects while widening the scope of QI evaluation with sustainability. This study can serve as a guide for healthcare professionals to use a number of statistical methodologies in their QI evaluations.

Was the implementation of standardised tobacco packaging legislation in England associated with changes in smoking prevalence? A segmented regression analysis between 2006 and 2019

Background and aimIn 2016, England initiated the implementation of standardised tobacco packaging, introduced in conjunction with minimum pack sizes and other measures included in the 2014 European Tobacco Products Directive, over the course of a 1-year sell-off period ending in May 2017. These measures have been shown to have been associated with increases in tobacco prices and product diversity. We now investigate the association between implementation of the new legislation and smoking status in England.DesignSegmented regression analysis of repeated cross-sectional surveys using a generalised linear model with individual-level data to test for a change in trend and immediate step change.SettingEngland.ParticipantsParticipants in the Smoking Toolkit Study, which involves repeated, cross‐sectional household surveys of individuals aged 16 years and older in England. The sample included 278 219 individual observations collected between November 2006 and December 2019.InterventionImplementation of standardised packaging legislation (May 2016 and May 2017).MeasurementsIndividual-level current smoking status adjusted for implementation of tobacco control policies, cigarette price, seasonality and autocorrelation.FindingsThe implementation of standardised packaging was associated with a significant step reduction in the odds of being a smoker after May 2017 (OR: 0.93; 95% CI 0.87 to 0.99). The magnitude of the association was similar when modelling the step change in May 2016 at the start of the 1-year policy implementation period (OR: 0.90; 95% CI: 0.83 to 0.97).ConclusionsThis is the first independent study demonstrating that implementation of standardised packaging was associated with a reduction in smoking in England which occurred in anticipation of, rather than after, full policy implementation. It appears that the odds of being a smoker were affected by the prospect of the move to standardised packs and accompanying legislation.

From a Deliberative to an Implementing Mindset a Process-oriented View of the Formation of Academic Entrepreneurial Intention

Background: Previous intention-based research has not considered whether participants are in the motivational or in the actional phase. In turn, this creates a gap of knowledge concerning the cognitive and motivational processes involved in the formation of Entrepreneurial Intention (EI). By applying the Theory of Planned Behavior (TPB), the present study addresses the formation of EI to commercialize research knowledge, focusing on the transition from motivation to implementation in the context of academia. Methods: Drawing on cross-sectional data of 490 researchers, segmented regression analysis was conducted to analyze the influence of entrepreneurial engagement on EI-growth. Multi-group Structural Equalization Modeling (SEM) was then used to test the moderation effects of engagement on the relationship between motivational factors and entrepreneurial intention. Results and Discussion: The analysis revealed a direct influence of engagement on EI, as well as a threshold of EI-growth per the context of a Rubicon crossing after the initiation of the first gestation action. Our data also show a growing influence of endogenous factors (e.g., attitudes and perceived behavior control) on EI during the venture creation process. The second part of the study contributes by testing the effects of entrepreneurial rewards on TPB-antecedents moderated by engagement. Conclusion: Until today, research mostly relied on cross-sectional data to predict and measure the strength of EI in the phase preceding the launch of a new business without considering whether participants are in the motivational or in the actional phase. Our finding highlights the need to shift from focusing entrepreneurship research solely on intentions to now on the process and implementation perspective.