Detecting drug-drug interactions between therapies for COVID-19 and concomitant medications through the FDA adverse event reporting system.

Background: COVID-19 patients with underlying medical conditions are vulnerable to drug-drug interactions (DDI) due to the use of multiple medications. We conducted a discovery-driven data analysis to identify potential DDIs and associated adverse events (AEs) in COVID-19 patients from the FDA Adverse Event Reporting System (FAERS), a source of post-market drug safety. Materials and Methods: We investigated 18,589 COVID-19 AEs reported in the FAERS database between 2020 and 2021. We applied multivariate logistic regression to account for potential confounding factors, including age, gender, and the number of unique drug exposures. The significance of the DDIs was determined using both additive and multiplicative measures of interaction. We compared our findings with the Liverpool database and conducted a Monte Carlo simulation to validate the identified DDIs. Results: Out of 11,337 COVID-19 drug-Co-medication-AE combinations investigated, our methods identified 424 signals statistically significant, covering 176 drug-drug pairs, composed of 13 COVID-19 drugs and 60 co-medications. Out of the 176 drug-drug pairs, 20 were found to exist in the Liverpool database. The empirical p-value obtained based on 1,000 Monte Carlo simulations was less than 0.001. Remdesivir was discovered to interact with the largest number of concomitant drugs (41). Hydroxychloroquine was detected to be associated with most AEs (39). Furthermore, we identified 323 gender- and 254 age-specific DDI signals. Conclusion: The results, particularly those not found in the Liverpool database, suggest a subsequent need for further pharmacoepidemiology and/or pharmacology studies.

Identifying Stock Prices Using an Advanced Hybrid ARIMA-Based Model: A Case of Games Catalogs

At the beginning of 2020, the COVID-19 pandemic struck the world, affecting the pace of life and the economic behavioral patterns of people around the world, with an impact exceeding that of the 2008 financial crisis, causing a global stock market crash and even the first recorded negative oil prices. Under the impact of this pandemic, due to the global large-scale quarantine and lockdown measures, game stocks belonging to the stay-at-home economy have become the focus of investors from all over the world. Therefore, under such incentives, this study aims to construct a set of effective prediction models for the price of game stocks, which could help relevant stakeholders-especially investors-to make efficient predictions so as to achieve a profitable investment niche. Moreover, because stock prices have the characteristics of a time series, and based on the relevant discussion in the literature, we know that ARIMA (the autoregressive integrated moving average) prediction models have excellent prediction performance. In conclusion, this study aims to establish an advanced hybrid model based on ARIMA as an excellent prediction technology for the price of game stocks, and to construct four groups of different investment strategies to determine which technical models of investment strategies are suitable for different game stocks. There are six important directions, experimental results, and research findings in the construction of advanced models: (1) In terms of the experiment, the data are collected from the daily closing prices of game-related stocks on the Taiwan Stock Exchange, and the sample range is from 2014 to 2020. (2) In terms of the performance verification, the return on investment is used as the evaluation standard to verify the availability of the ARIMA prediction model. (3) In terms of the research results, the accuracy of the model in predicting the prices of listed stocks can reach the 95% confidence interval predicted by the model 14 days after the closing price, and the OTC stocks fall within the 95% confidence interval for 3 days. (4) In terms of the empirical study of the rate of return, the investors can obtain a better rate of return than the benchmark strategy by trading the game stocks based on the indices set by the ARIMA model in this study. (5) In terms of the research findings, this study further compares the rate of return of trading strategies with reference to the ARIMA index and the rate of return of trading strategies with reference to the monitoring indicator, finding no significant difference between the two. (6) Different game stocks apply for different technical models of investment strategies.

Identification of Applicable YouTubers for Hotels: A Case Study of Integrated Hybrid MCDM Model

The coronavirus disease 2019 (COVID-19) pandemic has caused a serious business recession in various walks of life, particularly in the full-service hotel industry. YouTube has one billion active users and is undoubtedly a social media platform that companies use to build relationships with customers and create value for brands. Marketers should be aware of YouTubers' significant influence on complex decision-making processes. Given the above reasons, identifying a YouTuber attracts the concerns of various industries;thus, this important issue is focused on and offered the study's rationality. This study proposes an integrated hybrid MCDM model to organize the four key techniques of FDM, DEMATEL, ANP, and TOPSIS to identify YouTubers for hotels. Consequently, 12 key criteria and four core dimensions were identified to improve the decision of optimal YouTubers for promoting sustainable development and increasing the efficiency of decision-making. From the limited literature review, the proposed hybrid model was not observed regarding YouTuber identification of hotels;thus, this study provides a superior application contribution to address this important and interesting topic for academicians and practitioners.

Prenatal Telehealth During the Pandemic: Sociodemographic and Clinical Associations

Objectives: Like other areas of care affected by the COVID-19 pandemic, telehealth (both audio and video) was rapidly adopted in the obstetric setting. We performed a retrospective analysis of electronic health record (EHR) data to characterize the sociodemographic and clinical factors associated with telehealth utilization among patients who received prenatal care. Materials and Methods: The study period covered March 23rd, 2020 to July 2nd, 2020, during which time 2,521 patients received prenatal care at a large academic medical center. We applied a generalized logistic regression to measure the relationship between the patients’ sociodemographic factors (in terms of age, race, ethnicity, urbanization level, and insurance type), pregnancy complications (namely, type 2 diabetes, chronic hypertension, and fetal growth restriction), and telehealth usage, as documented in the EHR. Results: During the study period, 2,521 patients had 16,516 prenatal care visits. 938 (37.2%) of the patients participated in at least one of 1,934 virtual prenatal care visits. Prenatal visits were more likely to be conducted through telehealth for patients who were older than 25 years old and lived in rural areas. In addition, patients who were with type 2 diabetes were more likely to use telehealth in their prenatal care (adjusted Odds Ratio (aOR) 7.247 [95% Confidence Interval (95% CI) 4.244 – 12.933]). By contrast, patients from racial and ethnic minority groups were less likely to have a telehealth encounter compared to white or non-Hispanic patients (aOR 0.603 [95% CI 0.465 – 0.778] and aOR 0.663 [95% CI 0.471 – 0.927], respectively). Additionally, patients who were on state-level Medicaid were less likely to use telehealth (aOR 0.495 [95% CI 0.402 – 0.608]). Discussion: Disparities in telehealth use for prenatal care suggest further investigations into access barriers. Hispanic patients who had low English language proficiency may not willing to see doctors via virtual care. Availability of high-speed internet and/or hardware may hold these patients who were insured through state-level Medicaid back due to poverty. Future work is advised to minimize access barriers to telehealth in its implementation. Conclusions: While telehealth expanded prenatal care access for childbearing women during the COVID-19 pandemic, this study suggested that there were non-trivial differences in the demographics of patients who utilized such settings.

Telehealth Uptake into Primary Care During the COVID-19 Pandemic.

Telehealth is designed to provide health services through the use of electronic information and telecommunication technologies. It has quickly become an important tool to ensure continued care in response to the COVID-19 pandemic while mitigating the risk of viral exposure for patients and providers. This study compared the number of monthly telehealth visits in primary care settings at a large academic medical center from 2019 and 2020. To investigate what health conditions are suitable for telehealth visits, we report on the ten ICD-10 codes with the largest number of telehealth visits.

Telehealth Use in the COVID-19 Pandemic: A Retrospective Study of Prenatal Care.

Telehealth is an alternative care delivery model to in-person care. It uses electronic information and telecommunication technologies to provide remote clinical care to patients, especially those living in rural areas that lack sufficient access to health care services. Like other areas of care affected by the COVID-19 pandemic, the prevalence of telehealth has increased in prenatal care. This study reports on telehealth use in prenatal care at a large academic medical center in Middle Tennessee, USA. We examine the electronic health records of over 2500 women to characterize 1) the volume of prenatal visits participating in telehealth, 2) disparities in obstetric patients using telehealth, and 3) the impact of telehealth use on obstetric outcomes, including duration of intrapartum hospital stays, preterm birth, Cesarean rate, and newborn birthweight. Our results show that telehealth mainly was used in the second and third trimesters, especially for consulting services. In addition, we found that certain demographics correlated with lower telehealth utilization, including patients who were under 26 years old, were Black and/or Hispanic, were on a state-sponsored health insurance program, and those who lived in urban areas. Furthermore, no significant differences were found on preterm birth and Cesarean between the patients who used telehealth in their prenatal care and those who did not.

Detecting Drug-Drug Interactions in COVID-19 Patients.

COVID-19 patients with multiple comorbid illnesses are more likely to be using polypharmacy to treat their COVID-19 disease and comorbid conditions. Previous literature identified several DDIs in COVID-19 patients; however, various DDIs are unrecognized. This study aims to discover novel DDIs by conducting comprehensive research on the FDA Adverse Event Reporting System (FAERS) data from January 2020 to March 2021. We applied seven algorithms to discover DDIs. In addition, the Liverpool database containing DDI confirmed by clinical trials was used as a gold standard to determine novel DDIs in COVID-19 patients. The seven models detected 2,516 drug-drug pairs having adverse events (AEs), 49 out of which were confirmed by the Liverpool database. The remaining 2,467 drug pairs tested to be significant by the seven models can be candidate DDIs for clinical trial hypotheses. Thus, the FAERS database, along with informatics approaches, provides a novel way to select candidate drug-drug pairs to be examined in COVID-19 patients.

Prognostic value of computed tomographic findings in acute respiratory distress syndrome and the response to prone positioning.

BACKGROUND: Prone positioning enables the redistribution of lung weight, leading to the improvement of gas exchange and respiratory mechanics. We aimed to evaluate whether the initial findings of acute respiratory distress syndrome (ARDS) on computed tomography (CT) are associated with the subsequent response to prone positioning in terms of oxygenation and 60-day mortality. METHODS: We retrospectively included patients who underwent prone positioning for moderate to severe ARDS from October 2014 to November 2020 at a medical center in Taiwan. A semiquantitative CT rating scale was used to quantify the extent of consolidation and ground-glass opacification (GGO) in the sternal, central and vertebral regions at three levels (apex, hilum and base) of the lungs. A prone responder was identified by a 20% increase in the ratio of arterial oxygen pressure (PaO2) to the fraction of oxygen (FiO2) or a 20 mmHg increase in PaO2. RESULTS: Ninety-six patients were included, of whom 68 (70.8%) were responders. Compared with nonresponders, responders had a significantly greater median dorsal-ventral difference in CT-consolidation scores (10 vs. 7, p = 0.046) but not in CT-GGO scores (- 1 vs. - 1, p = 0.974). Although dorsal-ventral differences in neither CT-consolidation scores nor CT-GGO scores were associated with 60-day mortality, high total CT-GGO scores (≥ 15) were an independent factor associated with 60-day mortality (odds ratio = 4.07, 95% confidence interval, 1.39-11.89, p = 0.010). CONCLUSIONS: In patients with moderate to severe ARDS, a greater difference in the extent of consolidation along the dependent-independent axis on CT scan is associated with subsequent prone positioning oxygenation response, but not clinical outcome regarding survival. High total CT-GGO scores were independently associated with 60-day mortality.

Effects of Governmental Intervention on Foodborne Disease Events: Evidence from China.

Foodborne disease events (FDEs) endanger residents' health around the world, including China. Most countries have formulated food safety regulation policies, but the effects of governmental intervention (GI) on FDEs are still unclear. So, this paper purposes to explore the effects of GI on FDEs by using Chinese provincial panel data from 2011 to 2019. The results show that: (i) GI has a significant negative impact on FDEs. Ceteris paribus, FDEs decreased by 1.3% when government expenditure on FDEs increased by 1%. (ii) By strengthening food safety standards and guiding enterprises to offer safer food, government can further improve FDEs. (iii) However, GI has a strong negative externality. Although GI alleviates FDEs in local areas, it aggravates FDEs in other areas. (iv) Compared with the eastern and coastal areas, the effects of GI on FDEs in the central, western, and inland areas are more significant. GI is conducive to ensuring Chinese health and equity. Policymakers should pay attention to two tasks in food safety regulation. Firstly, they should continue to strengthen GI in food safety issues, enhance food safety certification, and strive to ensure food safety. Secondly, they should reinforce the co-governance of regional food safety issues and reduce the negative externality of GI.

Data visualization in introductory business statistics to strengthen students' practical skills

The objective of this study is to present and discuss how data visualization can be incorporated into teaching approaches by business faculty in introductory business statistics to strengthen business students' practical skills. Data visualization lessens difficulties in learning statistics by providing opportunities to illustrate analytical findings in graphic form, which is essential for learners with different learning styles. Familiarizing students with Excel, Python, or other software in introductory business statistics is beneficial in helping them attain statistical literacy by analyzing real‐world data such as COVID‐19 statistics. Using such data equips students with knowledge of statistical implementation—a core skill in the business world. [ FROM AUTHOR] Copyright of Teaching Statistics is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

A 3D structural SARS-CoV-2-human interactome to explore genetic and drug perturbations.

Emergence of new viral agents is driven by evolution of interactions between viral proteins and host targets. For instance, increased infectivity of SARS-CoV-2 compared to SARS-CoV-1 arose in part through rapid evolution along the interface between the spike protein and its human receptor ACE2, leading to increased binding affinity. To facilitate broader exploration of how pathogen-host interactions might impact transmission and virulence in the ongoing COVID-19 pandemic, we performed state-of-the-art interface prediction followed by molecular docking to construct a three-dimensional structural interactome between SARS-CoV-2 and human. We additionally carried out downstream meta-analyses to investigate enrichment of sequence divergence between SARS-CoV-1 and SARS-CoV-2 or human population variants along viral-human protein-interaction interfaces, predict changes in binding affinity by these mutations/variants and further prioritize drug repurposing candidates predicted to competitively bind human targets. We believe this resource ( http://3D-SARS2.yulab.org ) will aid in development and testing of informed hypotheses for SARS-CoV-2 etiology and treatments.

Comparable Studies of Financial Bankruptcy Prediction Using Advanced Hybrid Intelligent Classification Models to Provide Early Warning in the Electronics Industry

In recent years in Taiwan, scholars who study financial bankruptcy have mostly focused on individual listed and over-the-counter (OTC) industries or the entire industry, while few have studied the independent electronics industry. Thus, this study investigated the application of an advanced hybrid Z-score bankruptcy prediction model in selecting financial ratios of listed companies in eight related electronics industries (semiconductor, computer, and peripherals, photoelectric, communication network, electronic components, electronic channel, information service, and other electronics industries) using data from 2000 to 2019. Based on 22 financial ratios of condition attributes and one decision attribute recommended and selected by experts and in the literature, this study used five classifiers for binary logistic regression analysis and in the decision tree. The experimental results show that for the Z-score model, samples analyzed using the five classifiers in five groups (1:1–5:1) of different ratios of companies, the bagging classifier scores are worse (40.82%) than when no feature selection method is used, while the logistic regression classifier and decision tree classifier (J48) result in better scores. However, it is significant that the bagging classifier score improved to over 90% after using the feature selection technique. In conclusion, it was found that the feature selection method can be effectively applied to improve the prediction accuracy, and three financial ratios (the liquidity ratio, debt ratio, and fixed assets turnover ratio) are identified as being the most important determinants affecting the prediction of financial bankruptcy in providing a useful reference for interested parties to evaluate capital allocation to avoid high investment risks.

Assessing Neonatal Intensive Care Unit Structures and Outcomes Before and During the COVID-19 Pandemic: Network Analysis Study.

BACKGROUND: Health care organizations (HCOs) adopt strategies (eg. physical distancing) to protect clinicians and patients in intensive care units (ICUs) during the COVID-19 pandemic. Many care activities physically performed before the COVID-19 pandemic have transitioned to virtual systems during the pandemic. These transitions can interfere with collaboration structures in the ICU, which may impact clinical outcomes. Understanding the differences can help HCOs identify challenges when transitioning physical collaboration to the virtual setting in the post-COVID-19 era. OBJECTIVE: This study aims to leverage network analysis to determine the changes in neonatal ICU (NICU) collaboration structures from the pre- to the intra-COVID-19 era. METHODS: In this retrospective study, we applied network analysis to the utilization of electronic health records (EHRs) of 712 critically ill neonates (pre-COVID-19, n=386; intra-COVID-19, n=326, excluding those with COVID-19) admitted to the NICU of Vanderbilt University Medical Center between September 1, 2019, and June 30, 2020, to assess collaboration between clinicians. We characterized pre-COVID-19 as the period of September-December 2019 and intra-COVID-19 as the period of March-June 2020. These 2 groups were compared using patients' clinical characteristics, including age, sex, race, length of stay (LOS), and discharge dispositions. We leveraged the clinicians' actions committed to the patients' EHRs to measure clinician-clinician connections. We characterized a collaboration relationship (tie) between 2 clinicians as actioning EHRs of the same patient within the same day. On defining collaboration relationship, we built pre- and intra-COVID-19 networks. We used 3 sociometric measurements, including eigenvector centrality, eccentricity, and betweenness, to quantify a clinician's leadership, collaboration difficulty, and broad skill sets in a network, respectively. We assessed the extent to which the eigenvector centrality, eccentricity, and betweenness of clinicians in the 2 networks are statistically different, using Mann-Whitney U tests (95% CI). RESULTS: Collaboration difficulty increased from the pre- to intra-COVID-19 periods (median eccentricity: 3 vs 4; P<.001). Nurses had reduced leadership (median eigenvector centrality: 0.183 vs 0.087; P<.001), and neonatologists with broader skill sets cared for more patients in the NICU structure during the pandemic (median betweenness centrality: 0.0001 vs 0.005; P<.001). The pre- and intra-COVID-19 patient groups shared similar distributions in sex (~0 difference), race (4% difference in White, and 3% difference in African American), LOS (interquartile range difference in 1.5 days), and discharge dispositions (~0 difference in home, 2% difference in expired, and 2% difference in others). There were no significant differences in the patient demographics and outcomes between the 2 groups. CONCLUSIONS: Management of NICU-admitted patients typically requires multidisciplinary care teams. Understanding collaboration structures can provide fine-grained evidence to potentially refine or optimize existing teamwork in the NICU.

The Public's Risk Information Seeking and Avoidance in China During Early Stages of the COVID-19 Outbreak.

This study uses the Planned Risk Information Seeking Model (PRISM) to estimate the public's information seeking and avoidance intentions during the COVID-19 outbreak based on an online sample of 1031 Chinese adults and provides support for the applicability of PRISM framework in the situation of a novel high-level risk. The results indicate that information seeking is primarily directed by informational subjective norms (ISN) and perceived seeking control (PSC), while the main predictors of information avoidance include ISN and attitude toward seeking. Because ISN are the strongest predictor of both information seeking and avoidance, the way the public copes with COVID-19 information may be strongly affected by individuals' social environment. Furthermore, a significant relationship between risk perception and affective risk response is identified. Our results also indicate that people who perceive greater knowledge of COVID-19 are more likely to report greater knowledge insufficiency, which results in less information avoidance.

Collaboration Structures in COVID-19 Critical Care: Retrospective Network Analysis Study.

BACKGROUND: Few intensive care unit (ICU) staffing studies have examined the collaboration structures of health care workers (HCWs). Knowledge about how HCWs are connected to the care of critically ill patients with COVID-19 is important for characterizing the relationships among team structures, care quality, and patient safety. OBJECTIVE: We aimed to discover differences in the teamwork structures of COVID-19 critical care by comparing HCW collaborations in the management of critically ill patients with and without COVID-19. METHODS: In this retrospective study, we used network analysis methods to analyze the electronic health records (EHRs) of 76 critically ill patients (with COVID-19: n=38; without COVID-19: n=38) who were admitted to a large academic medical center, and to learn about HCW collaboration. We used the EHRs of adult patients who were admitted to the COVID-19 ICU at the Vanderbilt University Medical Center (Nashville, Tennessee, United States) between March 17, 2020, and May 31, 2020. We matched each patient according to age, gender, and their length of stay. Patients without COVID-19 were admitted to the medical ICU between December 1, 2019, and February 29, 2020. We used two sociometrics-eigencentrality and betweenness-to quantify HCWs' statuses in networks. Eigencentrality characterizes the degree to which an HCW is a core person in collaboration structures. Betweenness centrality refers to whether an HCW lies on the path of other HCWs who are not directly connected. This sociometric was used to characterize HCWs' broad skill sets. We measured patient staffing intensity in terms of the number of HCWs who interacted with patients' EHRs. We assessed the statistical differences in the core and betweenness statuses of HCWs and the patient staffing intensities of COVID-19 and non-COVID-19 critical care, by using Mann-Whitney U tests and reporting 95% CIs. RESULTS: HCWs in COVID-19 critical care were more likely to frequently work with each other (eigencentrality: median 0.096) than those in non-COVID-19 critical care (eigencentrality: median 0.057; P<.001). Internal medicine physicians in COVID-19 critical care had higher core statuses than those in non-COVID-19 critical care (P=.001). Nurse practitioners in COVID-19 care had higher betweenness statuses than those in non-COVID-19 care (P<.001). Compared to HCWs in non-COVID-19 settings, the EHRs of critically ill patients with COVID-19 were used by a larger number of internal medicine nurse practitioners (P<.001), cardiovascular nurses (P<.001), and surgical ICU nurses (P=.002) and a smaller number of resident physicians (P<.001). CONCLUSIONS: Network analysis methodologies and data on EHR use provide a novel method for learning about differences in collaboration structures between COVID-19 and non-COVID-19 critical care. Health care organizations can use this information to learn about the novel changes that the COVID-19 pandemic has imposed on collaboration structures in urgent care.

Emergency department response to coronavirus disease 2019 outbreak with a fever screening station and "graded approach" for isolation and testing.

BACKGROUND: Ever since coronavirus disease 2019 (COVID-19) emerged in Wuhan, China, in December 2019, it has had a devastating effect on the world through exponential case growth and death tolls in at least 146 countries. Rapid response and timely modifications in the emergency department (ED) for infection control are paramount to maintaining basic medical services and preventing the spread of COVID-19. This study presents the unique measure of combining a fever screening station (FSS) and graded approach to isolation and testing in a Taiwanese medical center. METHODS: An FSS was immediately set up outside the ED on January 27, 2019. A graded approach was adopted to stratify patients into "high risk," "intermediate risk," and "undetermined risk" for both isolation and testing. RESULTS: A total of 3755 patients were screened at the FSS, with 80.3% visiting the ED from home, 70.9% having no travel history, 21.4% having traveled to Asia, and 10.0% of TVGH staff. Further, 54.9% had fever, 35.5% had respiratory symptoms, 3.2% had gastrointestinal symptoms, 0.6% experienced loss of smell, and 3.1% had no symptoms; 81.3% were discharged, 18.6% admitted, and 0.1% died. About 1.9% were admitted to the intensive care unit, 10.3% to the general ward, and 6.4% were isolated. Two patients tested positive for COVID-19 (0.1%) and 127 (3.4%) tested positive for atypical infection; 1471 patients were tested for COVID-19; 583 were stratified as high-risk, 781 as intermediate-risk, and 107 as undetermined-risk patients. CONCLUSION: Rapid response for infection control is a paramount in the ED to confront the COVID-19 outbreak. The FFS helped divide the flow of high- and intermediate-risk patients; it also decreased the ED workload during a surge of febrile patients. A graded approach to testing uses risk stratification to prevent nosocomial infection of asymptomatic patients. A graded approach to isolation enables efficient allocation of scarce medical resources according to risk stratification.