Article numbers as a leading indicator of publication time

Aims: in health professions education (HPE), the use of statistics is commonly associated with somewhat larger samples, whereas smaller samples or single subjects (i.e., N = 1) are usually labelled as needing some kind of ‘qualitative’ approach. However, statistical methods can be very useful in small samples and for individual subjects as well, especially where we have time series of repeated measurements of the same outcome variable(s) of interest. The aim of this article is twofold: to demonstrate an example of a cross-correlation function for single subjects in a HPE context and to suggest a few settings in HPE where this cross-correlation function can be of use.Method: the example uses data from a recent Open Access publication on among others article numbers and publication time in a number of major HPE journals to examine the relation between the number of articles published and median publication time over time in the zero-cost Open-Source statistical program R version 4.0.5.Results: as to be expected, the number of articles published appears somewhat of a leading indicator of publication time: both number of articles in year ‘y’ and number of articles in year ‘y minus 1’ correlate > 0.6 with median publication time in year ‘y’, while correlations of other time differences (e.g., number of articles in year ‘y minus 2’ and median publication time in year ‘y’, or median publication time in year ‘y’ and number of articles in year ‘y plus 1’) are substantially smaller.Conclusion: in line with recent literature, this article demonstrates that the cross-correlation function can be used in the context of small samples and single subjects. While the example focusses on article numbers and publication times, it can equally be applied in for example studying relations between knowledge, skills and attitude in individuals, or relations between behaviors of individuals working in pairs or small groups.

43. SARS-CoV-2 Cycle threshold (Ct) values predict future COVID-19 cases

Background The threat of surging COVID-19 cases prompted many hospitals in the United States to preemptively suspend elective procedures throughout the pandemic. Utilizing samples from a large hospital in Los Angeles, we sought to determine if temporal trends in SARS-CoV-2 Cycle threshold (Ct) values (proxy for viral RNA loads) were predictive for the number of future COVID-19 cases. Methods Nasopharyngeal specimens on symptomatic patients and asymptomatic admissions were tested using the Xpert Xpress SARS-CoV-2 and SARS-CoV-2/Flu/RSV assays (Cepheid). Ct values for all SARS-CoV-2 detections between October 2020 to March 2021 were compiled for analysis. Results A total of 2,114 SARS-CoV-2-positive samples were included. The number of tests performed per week increased dramatically in December peaking the first week of January before returning to pre-surge numbers by Mid-February. Ct values fell during this same period with values in December and January (25.6±7.8 and 27±7.9, respectively) significantly lower than those of the other months (30±9.3 to 37.7±6.3). Average weekly Ct values for all patients were significantly, negatively correlated with the number of tests run the following week (R= -0.71, P< 0.001) and two weeks later (R= -0.75, P< 0.0001). Ct values for patients who were asymptomatic at the time of testing most strongly correlated with total number of tests performed one month later (R= -0.86, P< 0.0001). Average weekly Ct values and number of test run As cases (light grey) increased during December and January, there was a significant decrease in Ct values (dark grey) during that same time period. Average Ct values are a leading indicator of cases Average weekly Ct values for all patients (light grey) were significantly, negatively correlated with the number of tests run the following week (R= -0.71, P<0.001) and two weeks later (R= -0.75, P<0.0001). Ct values for patients who were asymptomatic at the time of testing (dark grey) most strongly correlated with total number of tests performed one month later (R= -0.86, P<0.0001). Conclusion Lower Ct values, representing higher levels of viral RNA, have been associated with risk of intubation and infectivity. During the winter surge, we observed significantly lower Ct values suggesting that the increased transmission and morbidity of COVID-19 was temporarily associated with higher viral loads. Interestingly, Ct values for asymptomatic patients were most strongly associated with number of cases observed 1 months in the future, suggesting that asymptomatic viral load may be a leading indicator for forthcoming outbreaks. Given this association, Ct values may be a useful tool for predicting regional outbreaks of COVID-19 and more judicious cessation of elective procedures. Disclosures All Authors: No reported disclosures

On the Construction of a Leading Indicator Based on News Headlines for Predicting Greek Deposit Outflows

Employing data in a monthly frequency, with a sample period spanning from 2002 to 2018, the purpose of this study is twofold. First, we construct a novel leading indicator based on news headlines drawn from Bloomberg, and second, examine whether this leading indicator able to capture agents’ sentiment affects Greek bank deposit flows’ trajectory. Employing alternative econometric methodologies, we find that this index proxies for depositors’ crisis sentiment and the higher this index becomes, the higher the depositors’ negative sentiment becomes, leading them to withdraw their bank deposits. Overall, in this work, we show that the last decade’s advances in internet technology, which permit us to have direct access to a vast amount of information such as news headlines, offers the possibility of forecasting critical measures in the economy’s banking system, such as the number of bank deposits, which are of crucial importance. Monetary poly authorities or macroprudential regulators could adapt our model to assess the resilience of a bank or the whole banking sector.

Applying the Social Vulnerability Index as a Leading Indicator to Protect Fire-Based Emergency Medical Service Responders’ Health

During emergencies, areas with higher social vulnerability experience an increased risk for negative health outcomes. However, research has not extrapolated this concept to understand how the workers who respond to these areas may be affected. Researchers from the National Institute for Occupational Safety and Health (NIOSH) merged approximately 160,000 emergency response calls received from three fire departments during the COVID-19 pandemic with the CDC’s publicly available Social Vulnerability Index (SVI) to examine the utility of SVI as a leading indicator of occupational health and safety risks. Multiple regressions, binomial logit models, and relative weights analyses were used to answer the research questions. Researchers found that higher social vulnerability on household composition, minority/language, and housing/transportation increase the risk of first responders’ exposure to SARS-CoV-2. Higher socioeconomic, household, and minority vulnerability were significantly associated with response calls that required emergency treatment and transport in comparison to fire-related or other calls that are also managed by fire departments. These results have implications for more strategic emergency response planning during the COVID-19 pandemic, as well as improving Total Worker Health® and future of work initiatives at the worker and workplace levels within the fire service industry.