Coronavirus disease 2019 (COVID-19): an evidence map of medical literature

Research prioritisation in preparedness for and response to outbreaks of high-consequence pathogens: a scoping review

BACKGROUND

Priority setting for research on epidemic/pandemic-prone pathogens is essential for the allocation of limited resources to optimise impact. It involves the identification of gaps in knowledge crucial to effective preparedness and response to outbreaks. This review maps priority-setting exercises, reviews their approaches to research prioritisation and describes associated monitoring and evaluation processes for research priorities on high-consequence pathogens.

METHODS

Using search terms associated with high-consequence pathogens, as defined by the WHO (2020), EMERGE (2019), European CDC (2022) and the Association of Southeast Asian Nations (2021), and research prioritisation, we searched WHO Global Index Medicus; Ovid Medline; Ovid Embase; Ovid Global Health; and Scopus. Grey literature sources were Google Scholar and the WHO websites, complemented by recommendations from stakeholder consultation. Two independent reviewers screened abstracts and full-texts including documents describing research prioritisation activities. Results were analysed using descriptive statistics and narrative synthesis.

RESULTS

We identified 125 publications presenting priority setting activities on 17 high-consequence pathogens published between 1975 and 2022. Most (62%) were related to SARS-CoV-2, 5.6% to Ebola virus and 5% to Zika virus. Three different broad approaches to setting priorities were identified, most (53%) involved external consultations with experts. Few (6%) indicated plans to monitor progress against set priorities.

CONCLUSIONS

Our results highlight the diversity in research prioritisation practice in the context of high-consequence pathogens and a limited application of the existing standards in health research prioritisation. An increased uptake of these standards and harmonisation of practice may improve quality and confidence and ultimately improve alignment of funded research with the resulting priorities.

Examining self-described policy-relevant evidence base for policymaking: an evidence map of COVID-19 literature

Background

Evidence-based policymaking is a paradigm aimed at increasing the use of evidence by actors involved in policymaking processes. The COVID-19 pandemic highlighted a heavy reliance on emerging evidence for policymaking during emergencies.

Objective

This study describes the focus and types of evidence in journal articles self-described as relevant to policymaking using the COVID-19 pandemic as a case study, identifying gaps in evidence and highlighting author stated perceived biases specifically in evidence-based policy making.

Design

Evidence mapping.

Data sources

We systematically searched SCOPUS, PubMed and LexisNexis for literature identifying policy-relevant evidence available on the COVID-19 pandemic.

Eligibility criteria

The study included only peer-reviewed literature identified as 'article', 'book chapter', 'review' covering the period from January 2020 to December 2022. Inclusion criteria required that articles have an abstract, authorship attribution and are written in English.

Data extraction and synthesis

A minimum of two authors independently extracted and coded for every level and final outputs were compared for consistency.

Results

A total of 213 articles met the inclusion criteria and were reviewed in this study. Lead authorship affiliations were from 50 countries with 70% of the outputs from developed economies including USA (20.2%), UK (18.3%) and Australia (7.5%). The most common purpose of the articles was the presentation of research findings the authors considered of relevance to policy (60.1%), followed by work that examined the impact of policy (28.6%) or highlighted or supported a policy need (22.5%), while some papers had multiple stated purposes. The most common challenges in policymaking identified by the authors of the reviewed papers were process failures and poor evidence utilisation during policymaking.

Conclusions

The evidence map identified the need for an interdisciplinary policy approach involving relevant stakeholders and driven by quality research as a progressive step towards prevention of future public health crises/pandemics.

Identify the most appropriate imputation method for handling missing values in clinical structured datasets: a systematic review

BACKGROUND AND OBJECTIVES

Comprehending the research dataset is crucial for obtaining reliable and valid outcomes. Health analysts must have a deep comprehension of the data being analyzed. This comprehension allows them to suggest practical solutions for handling missing data, in a clinical data source. Accurate handling of missing values is critical for producing precise estimates and making informed decisions, especially in crucial areas like clinical research. With data's increasing diversity and complexity, numerous scholars have developed a range of imputation techniques. To address this, we conducted a systematic review to introduce various imputation techniques based on tabular dataset characteristics, including the mechanism, pattern, and ratio of missingness, to identify the most appropriate imputation methods in the healthcare field.

MATERIALS AND METHODS

We searched four information databases namely PubMed, Web of Science, Scopus, and IEEE Xplore, for articles published up to September 20, 2023, that discussed imputation methods for addressing missing values in a clinically structured dataset. Our investigation of selected articles focused on four key aspects: the mechanism, pattern, ratio of missingness, and various imputation strategies. By synthesizing insights from these perspectives, we constructed an evidence map to recommend suitable imputation methods for handling missing values in a tabular dataset.

RESULTS

Out of 2955 articles, 58 were included in the analysis. The findings from the development of the evidence map, based on the structure of the missing values and the types of imputation methods used in the extracted items from these studies, revealed that 45% of the studies employed conventional statistical methods, 31% utilized machine learning and deep learning methods, and 24% applied hybrid imputation techniques for handling missing values.

CONCLUSION

Considering the structure and characteristics of missing values in a clinical dataset is essential for choosing the most appropriate data imputation technique, especially within conventional statistical methods. Accurately estimating missing values to reflect reality enhances the likelihood of obtaining high-quality and reusable data, contributing significantly to precise medical decision-making processes. Performing this review study creates a guideline for choosing the most appropriate imputation methods in data preprocessing stages to perform analytical processes on structured clinical datasets.

Perspectives on the Impact of the COVID-19 Pandemic on Research: A Cross-Sectional Survey of Pediatric Otolaryngologists

OBJECTIVE

To investigate the perspectives of pediatric otolaryngologists on the impact of the coronavirus disease 2019 (COVID-19) pandemic on their research.

METHODS

Two surveys were sent to members of the American Society of Pediatric Otolaryngology (ASPO) in 2019 and 2021 to assess research perspectives before and during the COVID-19 pandemic. The surveys contained questions about research engagement, barriers, time allocation, and shifts in research focus.

RESULTS

The COVID-19 pandemic reshaped research within pediatric otolaryngology, with a mixed impact on the amount of time allocated to research endeavors. Almost half of respondents reported a change in research focus to COVID-19-related studies. Protected time and funding were significant pre-pandemic barriers, whereas reduced staff, collaboration opportunities, and enrollment limitations emerged as key pandemic-related obstacles. A personal commitment to research was most strongly correlated with time spent on this endeavor. During the pandemic, women were less likely to report an increase in research activity when compared to men, possibly due to a disproportionate burden of caregiving on women during school closures and stay-at-home orders.

CONCLUSION

Overall, the pandemic prompted both increases and decreases in research time allocation, depending on individual circumstances and priorities. Despite new challenges, pediatric otolaryngologists remain committed to research and have continued to remain productive.

Artificial intelligence in emergency medicine. A systematic literature review

Motivation and objective: Emergency medicine is becoming a popular application area for artificial intelligence methods but remains less investigated than other healthcare branches. The need for time-sensitive decision-making on the basis of high data volumes makes the use of quantitative technologies inevitable. However, the specifics of healthcare regulations impose strict requirements for such applications. Published contributions cover separate parts of emergency medicine and use disparate data and algorithms. This study aims to systematize the relevant contributions, investigate the main obstacles to artificial intelligence applications in emergency medicine, and propose directions for further studies.

METHODS

The contributions selection process was conducted with systematic electronic databases querying and filtering with respect to established exclusion criteria. Among the 380 papers gathered from IEEE Xplore, ACM Digital Library, Springer Library, ScienceDirect, and Nature databases 116 were considered to be a part of the survey. The main features of the selected papers are the focus on emergency medicine and the use of machine learning or deep learning algorithms.

FINDINGS AND DISCUSSION

The selected papers were classified into two branches: diagnostics-specific and triage-specific. The former ones are focused on either diagnosis prediction or decision support. The latter covers such applications as mortality, outcome, admission prediction, condition severity estimation, and urgent care prediction. The observed contributions are highly specialized within a single disease or medical operation and often use privately collected retrospective data, making them incomparable. These and other issues can be addressed by creating an end-to-end solution based on human-machine interaction.

CONCLUSION

Artificial intelligence applications are finding their place in emergency medicine, while most of the corresponding studies remain isolated and lack higher generalization and more sophisticated methodology, which can be a matter of forthcoming improvements.

Artificial intelligence and machine learning in prehospital emergency care: A scoping review

Our scoping review provides a comprehensive analysis of the landscape of artificial intelligence (AI) applications in prehospital emergency care (PEC). It contributes to the field by highlighting the most studied AI applications and identifying the most common methodological approaches across 106 included studies. The findings indicate a promising future for AI in PEC, with many unique use cases, such as prognostication, demand prediction, resource optimization, and the Internet of Things continuous monitoring systems. Comparisons with other approaches showed AI outperforming clinicians and non-AI algorithms in most cases. However, most studies were internally validated and retrospective, highlighting the need for rigorous prospective validation of AI applications before implementation in clinical settings. We identified knowledge and methodological gaps using an evidence map, offering a roadmap for future investigators. We also discussed the significance of explainable AI for establishing trust in AI systems among clinicians and facilitating real-world validation of AI models.

Handling missing values in healthcare data: A systematic review of deep learning-based imputation techniques

OBJECTIVE

The proper handling of missing values is critical to delivering reliable estimates and decisions, especially in high-stakes fields such as clinical research. In response to the increasing diversity and complexity of data, many researchers have developed deep learning (DL)-based imputation techniques. We conducted a systematic review to evaluate the use of these techniques, with a particular focus on the types of data, intending to assist healthcare researchers from various disciplines in dealing with missing data.

MATERIALS AND METHODS

We searched five databases (MEDLINE, Web of Science, Embase, CINAHL, and Scopus) for articles published prior to February 8, 2023 that described the use of DL-based models for imputation. We examined selected articles from four perspectives: data types, model backbones (i.e., main architectures), imputation strategies, and comparisons with non-DL-based methods. Based on data types, we created an evidence map to illustrate the adoption of DL models.

RESULTS

Out of 1822 articles, a total of 111 were included, of which tabular static data (29%, 32/111) and temporal data (40%, 44/111) were the most frequently investigated. Our findings revealed a discernible pattern in the choice of model backbones and data types, for example, the dominance of autoencoder and recurrent neural networks for tabular temporal data. The discrepancy in imputation strategy usage among data types was also observed. The "integrated" imputation strategy, which solves the imputation task simultaneously with downstream tasks, was most popular for tabular temporal data (52%, 23/44) and multi-modal data (56%, 5/9). Moreover, DL-based imputation methods yielded a higher level of imputation accuracy than non-DL methods in most studies.

CONCLUSION

The DL-based imputation models are a family of techniques, with diverse network structures. Their designation in healthcare is usually tailored to data types with different characteristics. Although DL-based imputation models may not be superior to conventional approaches across all datasets, it is highly possible for them to achieve satisfactory results for a particular data type or dataset. There are, however, still issues with regard to portability, interpretability, and fairness associated with current DL-based imputation models.

Radiology During the COVID-19 Pandemic: Mapping Radiology Literature in 2020

OBJECTIVES

Our aim was to assess articles published in the field of radiology, nuclear medicine, and medical imaging in 2020 and analyze the linkage of radiology-related topics with coronavirus disease 2019 (COVID-19) through literature mapping along with a bibliometric analysis for publications.

METHODS

We performed a search on the Web of Science Core Collection database for articles in the field of radiology, nuclear medicine, and medical imaging published in 2020. We analyzed the included articles using VOS viewer software, where we analyzed the co-occurrence of keywords, representing major topics discussed. Of the resulting topics, a literature map was created and linkage analysis was done.

RESULTS

A total of 24,748 articles were published in the field of radiology, nuclear medicine, and medical imaging in 2020. We found a total of 61,267 keywords; only 78 keywords occurred more than 250 times. COVID-19 had 449 occurrences, 29 links, with a total link strength of 271. MRI was the topic most commonly appearing in 2020 radiology publications, while "computed tomography" had the highest linkage strength with COVID-19, with a linkage strength of 149, representing 54.98% of the total COVID-19 linkage strength, followed by "radiotherapy, and "deep and machine learning". The top cited paper had a total of 1,687 citations. Nine out of the 10 most cited articles discussed COVID-19 and included "COVID-19" or "coronavirus" in their title, including the top cited paper.

CONCLUSION

While MRI was the topic that dominated, CT had the highest linkage strength with COVID-19 and represented the topic of top cited articles in 2020 radiology publications.

Text classification technique for discovering country-based publications from international COVID-19 publications

Objective

The significant increase in the number of COVID-19 publications, on the one hand, and the strategic importance of this subject area for research and treatment systems in the health field, on the other hand, reveals the need for text-mining research more than ever. The main objective of the present paper is to discover country-based publications from international COVID-19 publications with text classification techniques.

Methods

The present paper is applied research that has been performed using text-mining techniques such as clustering and text classification. The statistical population is all COVID-19 publications from PubMed Central® (PMC), extracted from November 2019 to June 2021. Latent Dirichlet allocation (LDA) was used for clustering, and support vector machine (SVM), scikit-learn library, and Python programming language were used for text classification. Text classification was applied to discover the consistency of Iranian and international topics.

Results

The findings showed that seven topics were extracted using the LDA algorithm for international and Iranian publications on COVID-19. Moreover, the COVID-19 publications show the largest share in the subject area of "Social and Technology in COVID-19" at the international (April 2021) and national (February 2021) levels with 50.61% and 39.44%, respectively. The highest rate of publications at international and national levels was in April 2021 and February 2021, respectively.

Conclusion

One of the most important results of this study was discovering a common trend and consistency of Iranian and international publications on COVID-19. Accordingly, in the topic category "Covid-19 Proteins: Vaccine and Antibody Response," Iranian publications have a common publishing and research trend with international ones.

Trends, characteristics, and impact of global scientific production on mental health of health workers in the context of coronavirus disease 2019 (COVID-19)

BACKGROUND

Healthcare workers are under significant constant stress as a result of the coronavirus disease 2019 (COVID-19) pandemic. The aim of this study, therefore, was to analyze bibliometrically the impact, trend, and characteristics of scientific production related to the mental health of health professionals during the COVID-19 pandemic.

MATERIALS AND METHODS

A bibliometric analysis of the scientific production on the mental health of health professionals and COVID-19 in Scopus from December 2019 to December 2021 was performed. An advanced search was designed using Boolean operators in Scopus and applied in April 2022. The metadata was entered into Microsoft Excel for the elaboration of the tables, SciVal to obtain the bibliometric indicators, and VosViewer to plot collaborative networks.

RESULTS

A total of 1393 manuscripts, 1007 of which met the eligibility criteria, were found on the mental health of health workers and COVID-19. The country with the highest academic production was the United States and Harvard University with 27 manuscripts as the most productive institution. The scientific journal with the highest scientific production was the International Journal of Environmental Research and Public Health with 138 manuscripts and 1580 citations, and the author with the most citations per publication was Carnnasi Claudia with 69.8.

CONCLUSION

The countries with the highest economic income occupy the first places in scientific production on the mental health of health workers during the COVID-19 pandemic, with the United States as the leader. There is a gap in the scientific knowledge on the mental health of healthcare workers during the COVID-19 pandemic in middle- and low-income countries.

Opioid Use Disorder Treatments: An Evidence Map

BACKGROUND

Evidence maps are emerging data visualization of a systematic review. There are no published evidence maps summarizing opioid use disorder (OUD) interventions.

AIM

Our aim was to publish an interactive summary of all peer-reviewed interventional and observational trials assessing the treatment of OUD and common clinical outcomes.

METHODS

PubMed, Embase, PsycInfo, Cochrane Central Register of Clinical Trials, and Web of Science were queried using multiple OUD-related MESH terms, without date limitations, for English-language publications. Inclusions were human subjects, treatment of OUD, OUD patient or community-level outcomes, and systematic reviews of OUD interventions. Exclusions were laboratory studies, reviews, and case reports. Two reviewers independently scanned abstracts for inclusion before coding eligible full-text articles by pre-specified filters: research design, study population, study setting, intervention, outcomes, sample size, study duration, geographical region, and funding sources.

RESULTS

The OUD Evidence Map (https://med.nyu.edu/research/lee-lab/research/opioid-use-disorder-treatment-evidence-map) identified and assessed 12,933 relevant abstracts through 2020. We excluded 9455 abstracts and full text reviewed 2839 manuscripts; 888 were excluded, 1591 were included in the final evidence map. The most studied OUD interventions were methadone (n = 754 studies), buprenorphine (n = 499), and naltrexone (n = 134). The most common outcomes were heroin/opioid use (n = 708), treatment retention (n = 557), and non-opioid drug use (n = 368). Clear gaps included a wider array of opioid agonists for treatment, digital behavioral interventions, studies of OUD treatments in criminal justice settings, and overdose as a clinical outcome.

CONCLUSION

This OUD Evidence Map highlights the importance of pharmacologic interventions for OUD and reductions in opioid use. Future iterations will update results annually and scan policy-level interventions.

Eye-Related COVID-19: A Bibliometric Analysis of the Scientific Production Indexed in Scopus

This paper analyzes, from a bibliometric viewpoint, those publications that relate COVID-19 and eye indexed in Scopus since the beginning of the pandemic, and it identifies the resulting main research lines. A bibliographic search in the Scopus database was conducted for publications that simultaneously include ocular and visual manifestations and aspects with COVID-19, from 1 January 2020 to 16 March 2021, and the obtained bibliographic information was processed with VOSviewer (v. 1.6.16). A total of 2206 documents were retrieved, and 60% were original articles. The USA published the most studies (24.6%). The retrieved documents had a total of 18,634 citations. The h index of the set of retrieved documents was 58. The Indian Journal of Ophthalmology was the most productive journal, while JAMA Neurology and The Lancet accounted for 20% of citations. Three keyword clusters representing hotspots in this field were identified. Eye-related COVID-19 research is an emerging field with plenty of scientific evidence whose growth is expected to increase as the aspects and manifestations of the disease, its treatment and the effect of vaccination on it become known.

The impact of COVID-19 pandemic on air pollution: a global research framework, challenges, and future perspectives

As a result of extreme modifications in human activity during the COVID-19 pandemic, the status of air quality has recently been improved. This bibliometric study was conducted on a global scale to quantify the impact of the COVID-19 pandemic on air pollution, identify the emerging challenges, and discuss the future perspectives during the course of the ongoing COVID-19 pandemic. For this, we have estimated the scientific production trends between 2020 and 2021 and investigated the contributions of countries, institutions, authors, and most prominent journals metrics network analysis on the topic of COVID-19 combined with air pollution research spanning the period between January 01, 2020, and June 21, 2021. The search strategy retrieved a wide range of 2003 studies published in scientific journals from the Web of Sciences Core Collection (WoSCC). The findings indicated that (1) publications on COVID-19 pandemic and air pollution were 990 (research articles) in 2021 with 1870 citations; however, the year 2020 witnessed only 830 research articles with a large number 16,600 of citations. (2) China ranked first in the number of publications (n = 365; 18.22% of the global output) and was the main country in international cooperation network, followed by the USA (n = 278; 13.87% of the global output) and India (n = 216; 10.78 of the total articles). (3) By exploring the co-occurrence and links strengths of keywords "COVID-19" (1075; 1092), "air pollution" (286; 771), "SARS-COV-2" (252; 1986). (4) The lessons deduced from the COVID-19 pandemic provide defined measures to reduce air pollution globally. The outcomes of the present study also provide useful guidelines for future research programs and constitute a baseline for researchers in the domain of environmental and health sciences to estimate the potential impact of the COVID-19 pandemic on air pollution.

Scientometric Analysis of Global Scientific Publications on COVID-19 and Diabetes with an Emphasis on Middle Eastern Countries

BACKGROUND

Due to the worldwide spread of COVID-19, various countries have designed scientific studies on different aspects of the disease. Patients with diabetes mellitus (DM) have been proven to be at higher risk of COVID-19-related complications, hospitalization, and death.

OBJECTIVES

The aim was to conduct a scientometric analysis of scholarly outputs on diabetes and COVID-19.

METHODS

Web of Science was searched for scientific publications on diabetes and COVID-19 by Middle Eastern researchers until September 14, 2021. Collected data were analyzed for document type, subject area, countries, top journals, citation number, and authors' collaboration network using VOS viewer 1.6.15 and bibliometrix R-package 4.1.1.

RESULTS

Overall, the characteristics of 603 documents on DM and COVID-19 were analyzed. The top three productive countries in the field were Iran, Turkey, and Saudi Arabia. The top affiliation was from Iran; "Tehran University of Medical Sciences" (n = 168), followed by "Shahid Beheshti University of Medical Sciences" (n = 82). The total citation number was 3704 times. The highest cited paper (348) was a systematic review from Iran, published in arch Acad Emerg Med. The top source was "Diabetes & Metabolic Syndrome: Clinical Research & Reviews," with 26 documents.

CONCLUSIONS

The current study provides an overview of the quantity and quality of published scholarly documents on the intersection of DM and COVID-19 in the region. Our findings help scientists find the existing gaps, manage the research budgets, identify active authors and scientific institutes to collaborate with, and use their experience to produce new knowledge in the future.

Impact of COVID-19 on Otolaryngology Literature

OBJECTIVES/HYPOTHESIS

To understand the effect of the COVID-19 pandemic on the volume, quality, and impact of otolaryngology publications.

STUDY DESIGN

Retrospective analysis.

METHODS

Fifteen of the top peer-reviewed otolaryngology journals were queried on PubMed for COVID and non-COVID-related articles from April 1, 2020 to March 31, 2021 (pandemic period) and pre-COVID articles from the year prior. Information on total number of submissions and rate of acceptance were collected from seven top-ranked journals.

RESULTS

Our PubMed query returned 759 COVID articles, 4,885 non-COVID articles, and 4,200 pre-COVID articles, corresponding to a 34% increase in otolaryngology publications during the pandemic period. Meta-analysis/reviews and miscellaneous publication types made up a larger portion of COVID publications than that of non-COVID and pre-COVID publications. Compared to pre-COVID articles, citations per article 120 days after publication and Altmetric Attention Score were higher in both COVID articles (citations/article: 2.75 ± 0.45, P < .001; Altmetric Attention Score: 2.05 ± 0.60, P = .001) and non-COVID articles (citations/article: 0.03 ± 0.01, P = .002; Altmetric Attention Score: 0.67 ± 0.28, P = .016). COVID manuscripts were associated with a 1.65 times higher acceptance rate compared to non-COVID articles (P < .001).

CONCLUSIONS

COVID-19 was associated with an increase in volume, citations, and attention for both COVID and non-COVID articles compared to pre-COVID articles. However, COVID articles were associated with lower evidence levels than non-COVID and pre-COVID articles.

LEVEL OF EVIDENCE

3 Laryngoscope, 132:1364-1373, 2022.

Immediate consequences and solutions used to maintain medical education during the COVID-19 pandemic for residents and medical students: a restricted review

BACKGROUND

The COVID-19 outbreak has dramatically impacted medical education, both bedside and academic teaching had to be adapted to comply with the reorganisation of care and social distancing measures.

OBJECTIVES

To overview the impact of the pandemic on medical education, including the pedagogical responses adopted and their assessment by medical students and residents.

MATERIAL AND METHODS

This restricted systematic review was performed using Rayyan QCRI, to select observational or interventional articles and field experience reports assessing the impact of the COVID-19 pandemic on medical education for medical students and residents. Study design, study population, geographical origin, use of an educational tools (including softwares and social media), their type and assessment, were recorded. For studies evaluating a specific tool the Medical Education Research Study Quality Instrument (MERSQI) was used to assess study quality.

RESULTS

The literature search identified 1480 references and 60 articles were selected. Most articles focused on residents (41/60; 69%), and half (30/60; 50%) involved surgical specialties. Online courses were the most frequently used pedagogical tool (52/60; 88%). Simulation tools were used more frequently in articles involving surgical specialties (15/29; 52%) compared with medical specialties (2/14; 12%) (p=0.01). Only four studies reported the assessment of pedagogical tools by medical students, their MERSQI scores ranged from 5.5/18 to 9.0/18.

CONCLUSION

Medical education was highly impacted by the COVID-19 pandemic particularly in surgical specialties. Online courses were the most frequently attempted solution to cope with social distancing constraints. Medical students' assessment of pedagogical tools was mostly positive, but the methodological quality of those studies was limited.

Contribution of Saudi Arabia to Regional and Global Publications on COVID-19–Related Research: A Bibliometric and Visualization Analysis

Background

At the global level and in Saudi Arabia, COVID-19 remains a major public health problem. The COVID-19 pandemic contributed substantially to a surge in publications on the novel coronavirus responsible for this pandemic. This research is intended to assess the increasing contribution of Saudi Arabia to the global research on COVID-19.

Methods

A bibliometric analysis of all Saudi-affiliated publications on COVID-19 documented between December 2019 and October 2021 was conducted in October 2021 using the Web of Science advanced search builder.

Results

A total of 175,615 global publications on COVID-19 were retrieved in the search. Among these, 9118 (5.2%) publications were from Arab nations. Among the Arab nations, Saudi Arabia (n = 3615) had the highest number of COVID-19 publications, followed by Egypt (n = 2053) and the United Arab Emirates (n = 1057), respectively. Globally, Saudi Arabia ranked 15th among the countries with the highest publication productivity, and the rank was 11th after standardization based on the population size and the gross domestic product. International collaborations were mainly with the researchers from Egypt, followed by the United States, India, Pakistan, and the United Kingdom. King Saud University was the most productive among all institutes in terms of COVID-19-related publications at both local and regional levels.

Conclusion

Saudi Arabia is the leading Arabian nation and one of the top fifteen nations worldwide in terms of COVID-19 research output. Further efforts are warranted from the researchers based in Saudi Arabia in the direction of increasing the quality and the number of publications at the global level. This can be achieved by timely response, proper planning, understating the global research progress, and enhancing the knowledge exchange and collaboration with the other local and international institutes.

Leveraging Large-Scale Electronic Health Records and Interpretable Machine Learning for Clinical Decision Making at the Emergency Department: Protocol for System Development and Validation

Background

There is a growing demand globally for emergency department (ED) services. An increase in ED visits has resulted in overcrowding and longer waiting times. The triage process plays a crucial role in assessing and stratifying patients’ risks and ensuring that the critically ill promptly receive appropriate priority and emergency treatment. A substantial amount of research has been conducted on the use of machine learning tools to construct triage and risk prediction models; however, the black box nature of these models has limited their clinical application and interpretation.

Objective

In this study, we plan to develop an innovative, dynamic, and interpretable System for Emergency Risk Triage (SERT) for risk stratification in the ED by leveraging large-scale electronic health records (EHRs) and machine learning.

Methods

To achieve this objective, we will conduct a retrospective, single-center study based on a large, longitudinal data set obtained from the EHRs of the largest tertiary hospital in Singapore. Study outcomes include adverse events experienced by patients, such as the need for an intensive care unit and inpatient death. With preidentified candidate variables drawn from expert opinions and relevant literature, we will apply an interpretable machine learning–based AutoScore to develop 3 SERT scores. These 3 scores can be used at different times in the ED, that is, on arrival, during ED stay, and at admission. Furthermore, we will compare our novel SERT scores with established clinical scores and previously described black box machine learning models as baselines. Receiver operating characteristic analysis will be conducted on the testing cohorts for performance evaluation.

Results

The study is currently being conducted. The extracted data indicate approximately 1.8 million ED visits by over 810,000 unique patients. Modelling results are expected to be published in 2022.

Conclusions

The SERT scoring system proposed in this study will be unique and innovative because of its dynamic nature and modelling transparency. If successfully validated, our proposed solution will establish a standard for data processing and modelling by taking advantage of large-scale EHRs and interpretable machine learning tools.

International Registered Report Identifier (IRRID)

DERR1-10.2196/34201

Towards Knowledge-Based Tourism Chinese Question Answering System

With the rapid development of the tourism industry, various travel websites are emerging. The tourism question answering system explores a large amount of information from these travel websites to answer tourism questions, which is critical for providing a competitive travel experience. In this paper, we propose a framework that automatically constructs a tourism knowledge graph from a series of travel websites with regard to tourist attractions in Zhejiang province, China. Backed by this domain-specific knowledge base, we developed a tourism question answering system that also incorporates the underlying knowledge from a large-scale language model such as BERT. Experiments on real-world datasets demonstrate that the proposed method outperforms the baseline on various metrics. We also show the effectiveness of each of the question answering components in detail, including the query intent recognition and the answer generation.

Bibliometric Analysis of International Scientific Production on Pharmacologic Treatments for SARS-CoV-2/COVID-19 During 2020

Background

COVID-19 is causing a grave global health and economic crisis and the fight against the pandemic has led to unprecedented scientific activity. Bibliometrics could be a useful tool for guiding future researches lines and promoting international collaboration for an effective treatment. For this purpose, we have conducted a bibliometric analysis of scientific publications on drugs and therapies used to treat COVID-19 during 2020.

Methods

Data source: Web of Science. We gathered data on scientific production relating to drugs used to treat COVID-19. We calculated impact factors and analyzed production by institution, country, and journal, visualizing our results in bibliometric networks.

Results

In 1 year, production relating to COVID-19 exceeded 100 000 publications, with over 6,500 on Drugs and COVID-19. Research into hydroxychloroquine and chloroquine, remdesivir, lopinavir and ritonavir, tocilizumab and convalescent plasma is particularly noteworthy. Mean citations/study range from 11.9 to 15.4. Producer institutions fall into three groups: one in the US and centered on Harvard Medical School; another in Europe led by INSERS; and another in China led by Huazhong University of Science and Technology. Production by journal is widespread but the Journal of Medical Virology, International Journal of Antimicrobial Agents, and American Journal of Transplantation are noteworthy.

Conclusions

The volume of research that is currently under way is comparable to the magnitude of the pandemic itself. Such a high volume of studies is infrequent and the impact they have achieved has no known precedent. The producing countries are those with highest incidence of the pandemic and greatest scientific potential; moreover, inter-agency and international collaboration has reached extraordinarily high levels.

A Bibliometric Analysis on Global Psychological and Behavioral Research Landscape on COVID-19 Pandemic

The COVID-19 pandemic outbreak in December 2019 has spread globally. The ongoing psychological and behavioral effects of the COVID-19 pandemic, which poses a major challenge to humanity, are of concern to researchers. To understand the academic community’s attention, focus and research collaboration on psychological and behavioral research during the COVID-19 pandemic, we conducted a macro analysis using a bibliometric approach. Using the topic selection strategy of TS = (“COVID-19” OR “coronavirus disease 2019” OR “SARS-CoV-2” OR “2019-nCoV”) AND TS = (“behavio*”) AND TS = (“psycholog*”), 2096 high-quality research articles and reviews were downloaded as data from the Web of Science core collection on 16 November 2021. Through analysis and visualization, the following conclusions are drawn in this study: (1) The popularity and importance of psychological and behavioral research under COVID-19 has increased significantly and needs further attention; (2). Related research focuses on eight hotspots, with quarantine, health care workers, the elderly, students, pregnant women, family, consumers, social media and emergency preparedness knowledge as the focus of the research object; and (3) Research collaboration is relatively high at the author, organizational and national levels. However, low-income countries need to get more attention. Furthermore, this article would help researchers make decisions for the research of psychological and behavioral issues under COVID-19 and planning for future prospects to contribute to academic development and applied methodology.

Comparison of COVID-19 and non-COVID-19 papers

OBJECTIVE

The need to generate evidence related to COVID-19, the acceleration of publication and peer-review process and the competition between journals may have influenced the quality of COVID-19 papers. Our objective was to compare the characteristics of COVID-19 papers against those of non-COVID-19 papers and identify the variables in which they differ.

METHOD

We conducted a journal-matched case-control study. Cases were COVID-19 papers and controls were non-COVID-19 papers published between March 2020 and January 2021. Journals belonging to five different Journal Citations Reports categories were selected. Within each selected journal, a COVID-19 paper (where there was one) and another non-COVID-19 paper were selected. Conditional logistic regression models were fitted.

RESULTS

We included 81 COVID-19 and 143 non-COVID-19 papers. Descriptive observational studies and analytical observational studies had, respectively, a 55-fold (odds ratio [OR]: 55.12; 95% confidence interval [95%CI]: 7.41-409.84) and 19-fold (OR: 19.28; 95%CI: 3.09-120.31) higher likelihood of being COVID-19 papers, respectively, and also a higher probability of having a smaller sample size (OR: 7.15; 95%CI: 2.33-21.94). COVID-19 papers had a higher probability of being cited since their publication (OR: 4.97; 95%CI: 1.63-15.10).

CONCLUSIONS

The characteristics of COVID-19 papers differed from those of non-COVID-19 papers published in the first months of the pandemic. In order to ensure the publication of good scientific evidence the quality of COVID-19-papers should be preserved.

A systematic scoping review of research on COVID-19 impacts on eating disorders: A critical appraisal of the evidence and recommendations for the field

OBJECTIVE

Research investigating the effects of COVID-19 on eating disorders is growing rapidly. A comprehensive evaluation of this literature is needed to identify key findings and evidence gaps to better inform policy decisions related to the management of eating disorders during and after this crisis. We conducted a systematic scoping review synthesizing and appraising this literature.

METHOD

Empirical research on COVID-19 impacts on eating disorder severity, prevalence, and demand for treatment was searched. No sample restrictions were applied. Findings (n = 70 studies) were synthesized across six themes: (a) suspected eating disorder cases during COVID-19; (b) perceived pandemic impacts on symptoms; (c) symptom severity pre versus during the pandemic; (d) pandemic-related correlates of symptom severity; (e) impacts on carers/parents; and (f) treatment experiences during COVID-19.

RESULTS

Pandemic impacts on rates of probable eating disorders, symptom deterioration, and general mental health varied substantially. Symptom escalation and mental health worsening during-and due to-the pandemic were commonly reported, and those most susceptible included confirmed eating disorder cases, at-risk populations (young women, athletes, parent/carers), and individuals highly anxious or fearful of COVID-19. Evidence emerged for increased demand for specialist eating disorder services during the pandemic. The forced transition to online treatment was challenging for many, yet telehealth alternatives seemed feasible and effective.

DISCUSSION

Evidence for COVID-19 effects is mostly limited to participant self-report or retrospective recall, cross-sectional and descriptive studies, and samples of convenience. Several novel pathways for future research that aim to better understand, monitor, and support those negatively affected by the pandemic are formulated.

Multi-country collaboration in responding to global infectious disease threats: lessons for Europe from the COVID-19 pandemic

Since 2005, the world has faced several public health emergencies of international concern arising from infectious disease outbreaks. Of these, the COVID-19 pandemic has had by far the greatest health and economic consequences. During these emergencies, responses taken by one country often have an impact on other countries. The implication is that coordination between countries is likely to achieve better outcomes, individually and collectively, than each country independently pursuing its own self-interest. During the COVID-19 pandemic, gaps in multilateral cooperation on research and information sharing, vaccine development and deployment, and travel policies have hampered the speed and equity of global recovery. In this Health Policy article, we explore how multilateral collaboration between countries is crucial to successful responses to public health emergencies linked to infectious disease outbreaks. Responding to future global infectious disease threats and other health emergencies will require the creation of stronger mechanisms for multilateral collaboration before they arise. A change to the governance of multilateral institutions is a logical next step, with a focus on providing equal ownership and leadership opportunities to all member countries. Europe can be an example and advocate for stronger and better governed multilateral institutions.

One Year on: An Overview of Singapore's Response to COVID-19-What We Did, How We Fared, How We Can Move Forward

Background-One year has passed since the first COVID-19 case in Singapore. This scoping review commemorates Singaporean researchers that have expanded the knowledge on this novel virus. We aim to provide an overview of healthcare-related articles published in peer-reviewed journals, authored by the Singapore research community about COVID-19 during the first year of the pandemic. Methods-This was reported using the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) protocol. It included healthcare-related articles about COVID-19 published between 23 January 2020 and 22 January 2021 with a Singapore-affiliated author. MEDLINE, Embase, Scopus, Web of Science, CINAHL, PsycINFO, Google Scholar, and local journals were searched. The articles were screened independently by two reviewers. Results-The review included 504 articles. Most of the articles narrated the changes to hospital practice (210), while articles on COVID-19 pathology (94) formed most of the non-narrative papers. Publications on public health (61) and the indirect impacts to clinical outcomes (45) were other major themes explored by the research community. The remaining articles detailed the psychological impact of the pandemic (35), adaptations of medical education (30), and narratives of events (14). Conclusion-Amidst a resurgence of community cases involving variant COVID-19 strains, the resources from the research community will provide valuable guidance to navigate these uncertain times.

Science Mapping of the Global Knowledge Base on Management, Leadership, and Administration Related to COVID-19 for Promoting the Sustainability of Scientific Research

The pandemic caused by the COVID-19 virus has resulted in inevitable radical changes across almost all areas of daily life, with the pandemic having revealed perhaps the greatest crisis humanity has faced in modern history. This study aims to provide thematic and methodological recommendations for future sustainable research programs through a bibliometric analysis of publications focused on management, leadership, and administration related to COVID-19. The data for the study were obtained from the Web of Science Core Collection (WoSCC) bibliographic database and then analyzed according to thematic content analysis and bibliometric methodology. The study’s units of analysis include countries, journals, keywords, research models, sample/study group, and time to publication. VOSviewer software and visualization maps were used to report the findings obtained from the analyzed data. When the study’s results are evaluated regarding the number of related publications and total citations, it can be revealed that Anglo-American-, Chinese-, and European-centered dominance continues in COVID-19-related studies. The vast majority of publications on this subject area are concentrated in the field of health. In addition, the study’s findings revealed that the examined articles were generally published in journals considered as prestigious, have high impact factors, are published in the English language, and with articles published in a short time after a much-reduced editorial/review and publishing process. Unlike previous bibliometric reviews, this research comprehensively analyzed the management-, leadership-, and administration-oriented publications related to COVID-19 with a holistic approach, providing essential findings and recommendations for future sustainable thematic research opportunities.

A Global Overview of COVID-19 Research in the Pediatric Field: Bibliometric Review

BACKGROUND

Since the beginning of the COVID-19 pandemic, a great number of papers have been published in the pediatric field.

OBJECTIVE

We aimed to assess research around the globe on COVID-19 in the pediatric field by bibliometric analysis, identifying publication trends and topic dissemination and showing the relevance of publishing authors, institutions, and countries.

METHODS

The Scopus database was comprehensively searched for all indexed documents published between January 1, 2020, and June 11, 2020, dealing with COVID-19 in the pediatric population (0-18 years). A machine learning bibliometric methodology was applied to evaluate the total number of papers and citations, journal and publication types, the top productive institutions and countries and their scientific collaboration, and core keywords.

RESULTS

A total of 2301 papers were retrieved, with an average of 4.8 citations per article. Of this, 1078 (46.9%) were research articles, 436 (18.9%) were reviews, 363 (15.8%) were letters, 186 (8.1%) were editorials, 7 (0.3%) were conference papers, and 231 (10%) were categorized as others. The studies were published in 969 different journals, headed by The Lancet. The retrieved papers were published by a total of 12,657 authors from 114 countries. The most productive countries were the United States, China, and Italy. The four main clusters of keywords were pathogenesis and clinical characteristics (keyword occurrences: n=2240), public health issues (n=352), mental health (n=82), and therapeutic aspects (n=70).

CONCLUSIONS

In the pediatric field, a large number of articles were published within a limited period on COVID-19, testifying to the rush to spread new findings on the topic in a timely manner. The leading authors, countries, and institutions evidently belonged to the most impacted geographical areas. A focus on the pediatric population was often included in general articles, and pediatric research about COVID-19 mainly focused on the clinical features, public health issues, and psychological impact of the disease.

Scientometric analyzing the output of researchers and organizations on COVID-19 for better conducting the scientific efforts: with a glance to endocrinology

PURPOSE

As COVID-19 spreads rapidly and could affect the people and also lead to their death, especially individuals with underlying diseases, such as diabetes, the research community is also active in publishing novel research about it. Analyzing scientific outputs in this topic can represent an overview of publications. For this purpose, this study was conducted to determine status of publishing research works related to COVID-19 and analyzing the all documents published and indexed in Web of Science database and illustrate the co-occurrence and co-authority of hot papers in this documents.

METHODOLOGY

Our search strategy was based on using the related key terms including COVID-19, coronavirus, SARS2, etc., to find out all the published scientific works related to coronavirus disease indexed in web of science (WoS) in 2020. We then extracted the all hot papers and especially hot papers in endocrinology category and analyzed them. The data saved and imported in VOSviewer and ScientoPy programs for analysis and illustration of our data. We have shown our analysis in the tables, figures and maps.

RESULTS

Totally, 56,402 records and 309 hot papers were retrieved. 3 of these hot papers were in endocrinology category. The most common type of publication was original papers followed by editorial papers in the second rank. The country with the most published documents was the USA followed by China. The journals of "British Medical Journal" and "Journal of Medical Virology" were ranked as the first and second sources, respectively. The "Harvard University" was the top organization with high proportion of scientific publications and "the Lancet" was the top-ranked journal that published highly-cited papers.

CONCLUSION

The literature on COVID-19 is increasing with a high and fast growth. In this regard, there is a need to evaluate these publications once in a while and their results should be published to use this information for more effective management of future research works with emphasizes on the gaps of researches and more citable documents and allocation of budgets on more needed research and don't carry out the duplicates research. This would be helpful for prevention, control, and treatment of COVID-19 that is now among the most common topics in the world.

Publishing in pandemic times: A bibliometric analysis of early medical publications on Kawasaki-like disease (MIS-C, PIMS-TS) related to SARS-CoV-2

INTRODUCTION

At the end of April 2020, three European pediatric societies published an alert on a new hyperinflammatory disorder linked to SARS-CoV-2. This disease has alternatively been called Kawasaki-like disease, pediatric inflammatory multisystem syndrome temporally associated with SARS-CoV-2 infection (PIMS-TS), and multisystem inflammatory syndrome in children (MIS-C). These alerts provide a clear starting point from which to study the early response of the medical and scientific community to a new disease in terms of scientific publications, and to compare the timeline of this response with levels of general public interest. To this aim, we conducted a bibliometric analysis of articles on this disease published between 1 April and 5 July 2020.

METHOD

A literature search was performed using PubMed and in three preprint repositories. For each article, the name used for the disease in the title, the number of authors, the number of patients, the citations according to Google Scholar, the journal impact factor, and the Altmetric score were retrieved. Google search trends for the terms "Kawasaki" and "COVID," "COVID-19," and "coronavirus" were also retrieved, as was the number of Reuters news articles published on the topic. These data were compared longitudinally on a weekly basis. The quality of the reporting of the study was evaluated using the STROBE guidelines for observational studies with more than three patients and using the CARE guidelines for case reports of three or fewer patients.

RESULTS

Eighty-six articles were included, among which ten were preprints (three of which were subsequently published) and 49 were clinical articles (57%). A total of 857 patients were described. The median number of authors per article was five (range, 1-45), the median number of patients was four (1-186), the median number of citations was one (0-170), the median Altmetric score was 12 (0-7242), and the median journal impact factor was 3.7 (1-74.7). For the clinical articles, the median percentage of STROBE or CARE checklist items satisfied was 70% (IQR, 56.75-79.25; range, 40-90). Guideline adherence was significantly higher for observational studies than for case reports (median percentage of checklist items satisfied, 78.5% vs 61.5%; P<0.001); however, guideline adherence did not differ significantly between peer-reviewed and preprint articles (median percentage of checklist items satisfied, 57% vs 72%; P=0.205). The only statistically significant difference between clinical articles and other types of articles was the number of authors (median, 7 vs 2; P=2.53E-9). Fifty-seven of the 86 articles were authored by researchers from just three countries (the USA, 31; France, 14; and the UK, 12). The names most frequently used in the title were Kawasaki-like disease (n=37), followed by MIS-C (n=27), PIM-TS (n=14), and other names involving the term "inflammatory" (n=12). Google searches for related terms peaked between weeks 18 and 21, following the initial alerts and decreased rapidly thereafter. The number of Reuters articles on the subject was correlated with Google search trends (ρ: 0.86, 95% CI [0.59; 0.96]; P=0.00016), but the number of medical articles published was not (ρ: -0.54, 95% CI [-0.87; 0.14]; P=0.11). The first small case series was published less than 2 weeks after the initial alert; however, if all articles had been deposited as preprints when they were submitted to journals, the cumulative number of reported cases would have been 300% higher in week 18 (3 vs 1), 400% higher in week 19 (44 vs 11), 70% higher in week 20 (124 vs 73), and 54% higher in week 21 (129 vs 84).

CONCLUSION

In a period of 9 weeks after the initial alerts from European pediatric societies, 85 medical articles were published, involving 856 patients (one case report was published before the alerts), allowing rapid dissemination of research information. However, general public interest followed the news cycle rather than scientific releases. The quality of the reporting, as assessed by adherence to STROBE or CARE guidelines, was adequate with more than two-thirds of checklist items satisfied. Learned societies play an important role in the early dissemination of up-to-date peer-reviewed information. Preprint deposition should be encouraged to accelerate the dissemination of research information.

Artificial Intelligence Applications for COVID-19 in Intensive Care and Emergency Settings: A Systematic Review

Background: Little is known about the role of artificial intelligence (AI) as a decisive technology in the clinical management of COVID-19 patients. We aimed to systematically review and critically appraise the current evidence on AI applications for COVID-19 in intensive care and emergency settings. Methods: We systematically searched PubMed, Embase, Scopus, CINAHL, IEEE Xplore, and ACM Digital Library databases from inception to 1 October 2020, without language restrictions. We included peer-reviewed original studies that applied AI for COVID-19 patients, healthcare workers, or health systems in intensive care, emergency, or prehospital settings. We assessed predictive modelling studies and critically appraised the methodology and key findings of all other studies. Results: Of fourteen eligible studies, eleven developed prognostic or diagnostic AI predictive models, all of which were assessed to be at high risk of bias. Common pitfalls included inadequate sample sizes, poor handling of missing data, failure to account for censored participants, and weak validation of models. Conclusions: Current AI applications for COVID-19 are not ready for deployment in acute care settings, given their limited scope and poor quality. Our findings underscore the need for improvements to facilitate safe and effective clinical adoption of AI applications, for and beyond the COVID-19 pandemic.

Cross-Cultural Adaptation, Reliability, and Validity of a Brazilian of Short Version of the Posttraumatic Diagnostic Scale

Background: A short version of the Posttraumatic Diagnostic Scale (PDS) comprising only re-experiencing symptom items has been recently validated on Japanese adults. This short-version-PDS had good psychometric properties among Japanese adults with and without posttraumatic stress disorder (PTSD). The aim of this study was to translate and culturally validate the short-version-PDS for the Brazilian sociolinguistic context.

Methods: A translation of the short-version-PDS was performed based on established guidelines. We enrolled 53 patients with PTSD as a potential comorbidity. The translation and cross-cultural adaptation of the short-version-PDS included forward and back-translation by a Japanese Brazilian researcher and a certified translator; synthesis was achieved by consensus, backward translation, pilot test, and finalization. Content validity coefficient (CVC) was used to assess quality of adaptation. Internal consistency was calculated using Cronbach's alpha coefficient. Spearman correlations were between the new short-version-PDS and the Brazilian version of the posttraumatic Stress Disorder Checklist for DSM-5 (PCL-5), and a receiver operating characteristic (ROC) curve was used to determine the best cut-off values for the short-version-PDS.

Results: The short-version-PDS was well accepted by all subjects, none of the questions were experienced as inappropriate, and all questions of the 3 items were judged important. Item 1 presented CVCt = 0.92; item 2 had a CVCt = 0.87 and item 3 had a CVCt = 0.95. The internal consistency of the final version as measured by Cronbach's alpha was 0.78. The short-version-PDS scale correlated positively with the DSM-5 scale with a Spearman rho of 0.64 (95%CI [0.4-0.8], p < 0.001). The receiver operating characteristic (ROC) curve value was 0.97 (95%CI [0.9-1.0], p < 0.001). The cut-off score for a maximum Youden Index of 0.8 to distinguish moderate from severe from slight PTSD was > 31.0 with sensitivity and specificity are 86.4 and 93.5%, respectively.

Conclusions: This Brazilian Portuguese version of the short-version-PDS had good psychometric properties among Brazilian adults with and without PTSD. Transferability and generalizability of the cut-off scores should be further analyzed.

Utilizing machine learning dimensionality reduction for risk stratification of chest pain patients in the emergency department

BACKGROUND

Chest pain is among the most common presenting complaints in the emergency department (ED). Swift and accurate risk stratification of chest pain patients in the ED may improve patient outcomes and reduce unnecessary costs. Traditional logistic regression with stepwise variable selection has been used to build risk prediction models for ED chest pain patients. In this study, we aimed to investigate if machine learning dimensionality reduction methods can improve performance in deriving risk stratification models.

METHODS

A retrospective analysis was conducted on the data of patients > 20 years old who presented to the ED of Singapore General Hospital with chest pain between September 2010 and July 2015. Variables used included demographics, medical history, laboratory findings, heart rate variability (HRV), and heart rate n-variability (HRnV) parameters calculated from five to six-minute electrocardiograms (ECGs). The primary outcome was 30-day major adverse cardiac events (MACE), which included death, acute myocardial infarction, and revascularization within 30 days of ED presentation. We used eight machine learning dimensionality reduction methods and logistic regression to create different prediction models. We further excluded cardiac troponin from candidate variables and derived a separate set of models to evaluate the performance of models without using laboratory tests. Receiver operating characteristic (ROC) and calibration analysis was used to compare model performance.

RESULTS

Seven hundred ninety-five patients were included in the analysis, of which 247 (31%) met the primary outcome of 30-day MACE. Patients with MACE were older and more likely to be male. All eight dimensionality reduction methods achieved comparable performance with the traditional stepwise variable selection; The multidimensional scaling algorithm performed the best with an area under the curve of 0.901. All prediction models generated in this study outperformed several existing clinical scores in ROC analysis.

CONCLUSIONS

Dimensionality reduction models showed marginal value in improving the prediction of 30-day MACE for ED chest pain patients. Moreover, they are black box models, making them difficult to explain and interpret in clinical practice.

The Arab region's contribution to global COVID-19 research: Bibliometric and visualization analysis

BACKGROUND

At the global level and in the Arab world, particularly in low-income countries, COVID-19 remains a major public health issue. As demonstrated by an incredible number of COVID-19-related publications, the research science community responded rapidly. Therefore, this study was intended to assess the growing contribution of the Arab world to global research on COVID-19.

METHODS

For the period between December 2019 and March 2021, the search for publications was conducted via the Scopus database using terms linked to COVID-19. VOSviewer 1.6.16 software was applied to generate a network map to assess hot topics in this area and determine the collaboration patterns between different countries. Furthermore, the research output of Arab countries was adjusted in relation to population size and gross domestic product (GDP).

RESULTS

A total of 143,975 publications reflecting the global overall COVID-19 research output were retrieved. By restricting analysis to the publications published by the Arab countries, the research production was 6131 documents, representing 4.26% of the global research output regarding COVID-19. Of all these publications, 3990 (65.08%) were original journal articles, 980 (15.98%) were review articles, 514 (8.38%) were letters and 647 (10.55%) were others, such as editorials or notes. The highest number of COVID-19 publications was published by Saudi Arabia (n = 2186, 35.65%), followed by Egypt (n = 1281, 20.78%) and the United Arab Emirates (UAE), (n = 719, 11.73%). After standardization by population size and GDP, Saudi Arabia, UAE and Lebanon had the highest publication productivity. The collaborations were mostly with researchers from the United States (n = 968), followed by the United Kingdom (n = 661). The main research lines identified in COVID-19 from the Arab world are related to: public health and epidemiology; immunological and pharmaceutical research; signs, symptoms and clinical diagnosis; and virus detection.

CONCLUSIONS

A novel analysis of the latest Arab COVID-19-related studies is discussed in the current study and how these findings are connected to global production. Continuing and improving future collaboration between developing and developed countries will also help to facilitate the sharing of responsibilities for COVID-19 in research results and the implementation of policies for COVID-19.

Outbreaks of publications about emerging infectious diseases: the case of SARS-CoV-2 and Zika virus

BACKGROUND

Outbreaks of infectious diseases generate outbreaks of scientific evidence. In 2016 epidemics of Zika virus emerged, and in 2020, a novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) caused a pandemic of coronavirus disease 2019 (COVID-19). We compared patterns of scientific publications for the two infections to analyse the evolution of the evidence.

METHODS

We annotated publications on Zika virus and SARS-CoV-2 that we collected using living evidence databases according to study design. We used descriptive statistics to categorise and compare study designs over time.

RESULTS

We found 2286 publications about Zika virus in 2016 and 21,990 about SARS-CoV-2 up to 24 May 2020, of which we analysed a random sample of 5294 (24%). For both infections, there were more epidemiological than laboratory science studies. Amongst epidemiological studies for both infections, case reports, case series and cross-sectional studies emerged first, cohort and case-control studies were published later. Trials were the last to emerge. The number of preprints was much higher for SARS-CoV-2 than for Zika virus.

CONCLUSIONS

Similarities in the overall pattern of publications might be generalizable, whereas differences are compatible with differences in the characteristics of a disease. Understanding how evidence accumulates during disease outbreaks helps us understand which types of public health questions we can answer and when.

Comparison of performances of top emergency medicine journals in terms of COVID-19 publications in 2020

INTRODUCTION

The aim of this study is to evaluate the qualitative and quantitative contribution of Emergency Medicine (EM) journals to scientific literature on COVID-19 and compare the journals in terms of publications.

MATERIAL AND METHODS

We performed a comparison of top EM journals by hand-search in terms of COVID-19 publications and citations between January 1st and December 31st, 2020. Publications were also categorized according to research field, country and article types. Data were given as numbers and percentages.

RESULTS

Among 18 EM journals, Resuscitation ranked in the first place and American Journal of Emergency Medicine ranked in the last place according to Impact Factor. In these journals, 512 (12.2%) articles related to COVID-19 were published. The American Journal of Emergency Medicine and Internal and Emergency Medicine published the greatest amount of publications related to COVID-19 (n = 71). The American Journal of Emergency Medicine was also the leading journal in terms of "Total Citations to COVID-19 Articles" (n = 1192). Western Journal of Emergency Medicine published the greatest proportion of COVID-19 articles (Total COVID-19 Articles/Total Articles = 0.3). World Journal of Emergency Surgery ranked in the first place in terms of citations per COVID-19 articles (n = 33.2). The most common studied field was Effects of COVID-19 on the Health System (n = 222). The US was the most productive country with 188 COVID-19 publications and 1411 citations to these publications, followed by Italy.

CONCLUSION

The contribution of EM journals to COVID-19 literature is controversial. "Effects of COVID-19 on the Health System" is the most studied field. "Clinical Properties, Ethical Issues and Treatment Methods" are neglected fields in EM journals.

A Bibliometric Network Analysis of Coronavirus during the First Eight Months of COVID-19 in 2020

The COVID-19 pandemic has affected all aspects of society. Researchers worldwide have been working to provide new solutions to and better understanding of this coronavirus. In this research, our goal was to perform a Bibliometric Network Analysis (BNA) to investigate the strategic themes, thematic evolution structure and trends of coronavirus during the first eight months of COVID-19 in the Web of Science (WoS) database in 2020. To do this, 14,802 articles were analyzed, with the support of the SciMAT software. This analysis highlights 24 themes, of which 11 of the more important ones were discussed in-depth. The thematic evolution structure shows how the themes are evolving over time, and the most developed and future trends of coronavirus with focus on COVID-19 were visually depicted. The results of the strategic diagram highlight 'CHLOROQUINE', 'ANXIETY', 'PREGNANCY' and 'ACUTE-RESPIRATORY-SYNDROME', among others, as the clusters with the highest number of associated citations. The thematic evolution. structure presented two thematic areas: "Damage prevention and containment of COVID-19" and "Comorbidities and diseases caused by COVID-19", which provides new perspectives and futures trends of the field. These results will form the basis for future research and guide decision-making in coronavirus focused on COVID-19 research and treatments.

Network graph representation of COVID-19 scientific publications to aid knowledge discovery

Introduction

Numerous scientific journal articles related to COVID-19 have been rapidly published, making navigation and understanding of relationships difficult.

Methods

A graph network was constructed from the publicly available COVID-19 Open Research Dataset (CORD-19) of COVID-19-related publications using an engine leveraging medical knowledge bases to identify discrete medical concepts and an open-source tool (Gephi) to visualise the network.

Results

The network shows connections between diseases, medications and procedures identified from the title and abstract of 195 958 COVID-19-related publications (CORD-19 Dataset). Connections between terms with few publications, those unconnected to the main network and those irrelevant were not displayed. Nodes were coloured by knowledge base and the size of the node related to the number of publications containing the term. The data set and visualisations were made publicly accessible via a webtool.

Conclusion

Knowledge management approaches (text mining and graph networks) can effectively allow rapid navigation and exploration of entity inter-relationships to improve understanding of diseases such as COVID-19.

Coronavirus (COVID-19): A Systematic Review and Meta-analysis to Evaluate the Significance of Demographics and Comorbidities

Background

The unprecedented outbreak of a contagious respiratory disease caused by a novel coronavirus has led to a pandemic since December 2019, claiming millions of lives. The study systematically reviews and summarizes COVID-19's impact based on symptoms, demographics, comorbidities, and demonstrates the association of demographics in cases and mortality in the United States.

Methods

PubMed and Google Scholar were searched from December 2019- August 2020, and articles restricted to the English language were collected following PRISMA guidelines. US CDC data was used for establishing statistical significance of age, sex, and race.

Results

Among 3745 patients in China, mean age is 50.63 (95% CI: 36.84, 64.42) years, and 55.7 % (95% CI: 52.2, 59.2) were males. Symptoms included fever 86.5% (82.7, 90.0), fatigue 41.9% (32.7, 51.4), dyspnea 29.0% (21.2, 37.5), cough 66.0% (61.3, 70.6), mucus 66% (61.3, 70.6), lymphopenia 18.9% (5.2, 38.0). Prevalent comorbidities were hypertension 16.4% (12.5, 20.8), diabetes 8.9% (7.0, 11.1), CVD 10.9% (6.1, 16.7), ARDS 14.6% (4.9, 27.8), malignancy 1.5 (0.05, 2.8), 1.3% (0.08, 1.9), COPD 1.3 (0.08, 1.9). 63.5 % (33.5, 88.7) received oxygen therapy, 20.8% (8.9, 35.7) were in ventilation, 23.5% (5.9, 47.8) were at the ICU. 86.5% (76.8, 94) had antiviral, 73.9% (55.3, 89.0) had antibiotics, 30% (20.6, 40.2) corticosteroids treatment.In the US, the odds ratio of infection in males to females is 0.873 (CI: 0.052,14.791), while the odds of dying from infection is 1.378 (CI: 0.081, 23.528) for males. The prevalence of infection is higher in females; case and death rates are higher in whites and Hispanics than other races; the death rate is higher in males irrespective of race and age; death rate per 100,000 population increases monotonically with age.

Conclusion

Results showed that metabolic diseases comprising CVD, diabetes, hypertension, and respiratory diseases, including COPD, ARDS, are the most common comorbidities to severe condition and poor prognosis in covid-19 patients. Following the recent FDA's guidance for designing Covid-19 vaccine trials, stratification factors of age, race, sex, and comorbidities need consideration in allocation. This study aimed to provide clinical researchers, health policy planners a detailed insight into the coronavirus disease.

Mental health research in response to the COVID-19, Ebola, and H1N1 outbreaks: A comparative bibliometric analysis

INTRODUCTION

Both the COVID-19 pandemic and its management have had a negative impact on mental health worldwide. There is a growing body of research on mental health as it relates to the pandemic. The objective of this study is to use bibliometric analyses to assess the mental health research output related to the COVID-19 pandemic and compare it to that of the West Africa Ebola and H1N1 outbreaks.

METHODOLOGY

We performed comprehensive searches in Embase, PubMed, and Scopus databases, and included all types of documents related to the three outbreaks published since the respective beginnings up to August 26, 2020.

RESULTS

Despite the shorter time since the beginning of the COVID-19 pandemic, relative to Ebola and H1N1, we found a much greater number of mental health documents related to COVID-19 (n = 3070) compared to the two other outbreaks (127 for Ebola and 327 for H1N1). The proportion of documents in the top 10% journals was 31% for COVID-19, 24% for Ebola, and 40% for H1N1. Authors affiliated with institutions located in high-income countries published or contributed to 79% of all documents followed by authors from upper-middle-income countries (23%), lower-middle-income countries (10%), and low-income countries (2%). Approximately 19% of the documents reported receiving funding and 23% were the product of international collaboration.

CONCLUSION

Mental health research output is already greater for COVID-19 compared to Ebola and H1N1 combined. A minority of documents reported funding, was the product of international collaboration, or was published by authors located in low-income countries during the three outbreaks in general, and the COVID-19 pandemic in particular.

Impact of COVID-19 on pediatric emergencies and hospitalizations in Singapore

BACKGROUND

Coronavirus disease 2019 (COVID-19) has impacted the provision of health services in all specialties. We aim to study the impact of COVID-19 on the utilization of pediatric hospital services including emergency department (ED) attendances, hospitalizations, diagnostic categories and resource utilization in Singapore.

METHODS

We performed a retrospective review of ED attendances and hospital admissions among children < 18 years old from January 1st to August 8th 2020 in a major pediatric hospital in Singapore. Data were analyzed in the following time periods: Pre-lockdown (divided by the change in Disease Outbreak Response System Condition (DORSCON) level), during-lockdown and post-lockdown. We presented the data using proportions and percentage change in mean counts per day with the corresponding 95% confidence intervals (CIs).

RESULTS

We attended to 58,367 children with a mean age of 5.1 years (standard deviation, SD 4.6). The mean ED attendance decreased by 331 children/day during lockdown compared to baseline (p < 0.001), attributed largely to a drop in respiratory (% change - 87.9, 95% CI - 89.3 to - 86.3, p < 0.001) and gastrointestinal infections (% change - 72.4, 95%CI - 75.9 to - 68.4, p < 0.001). Trauma-related diagnoses decreased at a slower rate across the same periods (% change - 40.0, 95%CI - 44.3 to - 35.3, p < 0.001). We saw 226 children with child abuse, with a greater proportion of total attendance seen post-lockdown (79, 0.6%) compared to baseline (36, 0.2%) (p < 0.001). In terms of ED resource utilization, there was a decrease in the overall mean number of procedures performed per day during the lockdown compared to baseline, driven largely by a reduction in blood investigations (% change - 73.9, 95%CI - 75.9 to - 71.7, p < 0.001).

CONCLUSIONS

We highlighted a significant decrease in infection-related presentations likely attributed to the lockdown and showed that the relative proportion of trauma-related attendances increased. By describing the impact of COVID-19 on health services, we report important trends that may provide guidance when planning resources for future pandemics.

A conceptual framework for Emergency department design in a pandemic

Background

The current COVID-19 pandemic is highlighting gaps around the world in the design and workflow of Emergency Departments (ED). These gaps have an impact on both patient care and staff safety and represent a risk to public health. There is a need for a conceptual framework to guide ED design and workflow to address these challenges. Such a framework is important as the ED environment will always remain vulnerable to infectious diseases outbreaks in the future.

Aims

This paper aims to address issues and principles around ED design and workflow amidst the COVID-19 pandemic. We propose a conceptual framework and checklist for EDs to be prepared for future outbreaks as well.

Methods

A scoping literature review was conducted, of the experiences of EDs in managing outbreaks such as SARS, H1N1 and COVID-19. The combined experiences of the authors and the experiences from the literature were grouped under common themes to develop the conceptual framework.

Results

Four key principles were derived- (1) situational awareness, surveillance and perimeter defence, (2) ED staff protection, (3) surge capacity management and (4) ED recovery. The findings were integrated in a proposed conceptual framework to guide ED design in response to an infectious disease outbreak. There are various elements which need to be considered at ED input, throughput and output. These elements can be categorised into (1) system (workflow, protocols and communication), (2) staff (human resources), (3) space (infrastructure), and (4) supply (logistics) and are placed in a checklist for pragmatic use.

Conclusion

The ED needs to be in a constant state of preparedness. A framework can be useful to guide ED design and workflow to achieve this. As all ED systems are different with varying capabilities, our framework may help EDs across the world prepare for infectious disease outbreaks.