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1.
BMC Med Res Methodol ; 21(1): 1, 2021 01 04.
Article in English | MEDLINE | ID: covidwho-1067186

ABSTRACT

BACKGROUND: Since the start of the COVID-19 outbreak, a large number of COVID-19-related papers have been published. However, concerns about the risk of expedited science have been raised. We aimed at reviewing and categorizing COVID-19-related medical research and to critically appraise peer-reviewed original articles. METHODS: The data sources were Pubmed, Cochrane COVID-19 register study, arXiv, medRxiv and bioRxiv, from 01/11/2019 to 01/05/2020. Peer-reviewed and preprints publications related to COVID-19 were included, written in English or Chinese. No limitations were placed on study design. Reviewers screened and categorized studies according to i) publication type, ii) country of publication, and iii) topics covered. Original articles were critically appraised using validated quality assessment tools. RESULTS: Among the 11,452 publications identified, 10,516 met the inclusion criteria, among which 7468 (71.0%) were peer-reviewed articles. Among these, 4190 publications (56.1%) did not include any data or analytics (comprising expert opinion pieces). Overall, the most represented topics were infectious disease (n = 2326, 22.1%), epidemiology (n = 1802, 17.1%), and global health (n = 1602, 15.2%). The top five publishing countries were China (25.8%), United States (22.3%), United Kingdom (8.8%), Italy (8.1%) and India (3.4%). The dynamic of publication showed that the exponential growth of COVID-19 peer-reviewed articles was mainly driven by publications without original data (mean 261.5 articles ± 51.1 per week) as compared with original articles (mean of 69.3 ± 22.3 articles per week). Original articles including patient data accounted for 713 (9.5%) of peer-reviewed studies. A total of 576 original articles (80.8%) showed intermediate to high risk of bias. Last, except for simulation studies that mainly used large-scale open data, the median number of patients enrolled was of 102 (IQR = 37-337). CONCLUSIONS: Since the beginning of the COVID-19 pandemic, the majority of research is composed by publications without original data. Peer-reviewed original articles with data showed a high risk of bias and included a limited number of patients. Together, these findings underscore the urgent need to strike a balance between the velocity and quality of research, and to cautiously consider medical information and clinical applicability in a pressing, pandemic context. SYSTEMATIC REVIEW REGISTRATION: https://osf.io/5zjyx/.


Subject(s)
Biomedical Research/statistics & numerical data , /prevention & control , Pandemics , /isolation & purification , /virology , China/epidemiology , Humans , India/epidemiology , Italy/epidemiology , United Kingdom/epidemiology , United States/epidemiology
2.
Antimicrob Resist Infect Control ; 10(1): 10, 2021 01 12.
Article in English | MEDLINE | ID: covidwho-1028908

ABSTRACT

BACKGROUND: Translating research into practice is a central priority within the National Institutes of Health (NIH) Roadmap. The underlying aim of the NIH Roadmap is to accelerate the movement of scientific findings into practical health care provisions through translational research. MAIN TEXT: Despite the advances in health sciences, emerging infectious diseases have become more frequent in recent decades. Furthermore, emerging and reemerging pathogens have led to several global public health challenges. A question, and to an extent a concern, arises from this: Why our health care system is experiencing several challenges in encountering the coronavirus outbreak, despite the ever-growing advances in sciences, and the exponential rise in the number of published articles in the first quartile journals and even the ones among the top 1%? CONCLUSION: Two responses could be potentially provided to the above question: First, there seems to be a significant gap between our theoretical knowledge and practice. And second that many scholars and scientists publish papers only to have a longer list of publications, and therefore publishing is viewed as a personal objective, rather than for improving communities' public health.


Subject(s)
/virology , Publications/statistics & numerical data , /physiology , Biomedical Research/standards , Biomedical Research/statistics & numerical data , Humans , Policy , Publications/standards , Publishing/standards , Publishing/statistics & numerical data , /genetics
4.
Front Public Health ; 8: 582205, 2020.
Article in English | MEDLINE | ID: covidwho-983743

ABSTRACT

Background: Given the worldwide spread of the 2019 Novel Coronavirus (COVID-19), there is an urgent need to identify risk and protective factors and expose areas of insufficient understanding. Emerging tools, such as the Rapid Evidence Map (rEM), are being developed to systematically characterize large collections of scientific literature. We sought to generate an rEM of risk and protective factors to comprehensively inform areas that impact COVID-19 outcomes for different sub-populations in order to better protect the public. Methods: We developed a protocol that includes a study goal, study questions, a PECO statement, and a process for screening literature by combining semi-automated machine learning with the expertise of our review team. We applied this protocol to reports within the COVID-19 Open Research Dataset (CORD-19) that were published in early 2020. SWIFT-Active Screener was used to prioritize records according to pre-defined inclusion criteria. Relevant studies were categorized by risk and protective status; susceptibility category (Behavioral, Physiological, Demographic, and Environmental); and affected sub-populations. Using tagged studies, we created an rEM for COVID-19 susceptibility that reveals: (1) current lines of evidence; (2) knowledge gaps; and (3) areas that may benefit from systematic review. Results: We imported 4,330 titles and abstracts from CORD-19. After screening 3,521 of these to achieve 99% estimated recall, 217 relevant studies were identified. Most included studies concerned the impact of underlying comorbidities (Physiological); age and gender (Demographic); and social factors (Environmental) on COVID-19 outcomes. Among the relevant studies, older males with comorbidities were commonly reported to have the poorest outcomes. We noted a paucity of COVID-19 studies among children and susceptible sub-groups, including pregnant women, racial minorities, refugees/migrants, and healthcare workers, with few studies examining protective factors. Conclusion: Using rEM analysis, we synthesized the recent body of evidence related to COVID-19 risk and protective factors. The results provide a comprehensive tool for rapidly elucidating COVID-19 susceptibility patterns and identifying resource-rich/resource-poor areas of research that may benefit from future investigation as the pandemic evolves.


Subject(s)
Biomedical Research/statistics & numerical data , Data Interpretation, Statistical , Pandemics/statistics & numerical data , Protective Factors , Research Report , Humans , Risk Factors
6.
Clin Sci (Lond) ; 134(24): 3233-3235, 2020 12 23.
Article in English | MEDLINE | ID: covidwho-975035

ABSTRACT

As this extraordinary year, blemished by COVID-19, comes to an end, I look back as Editor-in-Chief to the many great successes and new initiatives of Clinical Science. Despite the challenges we all faced during 2020, our journal has remained strong and vibrant. While we have all adapted to new working conditions, with life very different to what it was pre-COVID-19, the one thing that remains intact and secure is the communication of scientific discoveries through peer-reviewed journals. I am delighted to share with you some of the many achievements of our journal over the past year and to highlight some exciting new activities planned for 2021.


Subject(s)
Biomedical Research/standards , Editorial Policies , Periodicals as Topic/standards , Biomedical Research/statistics & numerical data , Biomedical Research/trends , /immunology , /administration & dosage , Forecasting , Humans , Pandemics/prevention & control , Periodicals as Topic/statistics & numerical data , Periodicals as Topic/trends , /physiology
10.
BMJ Open ; 10(10): e039326, 2020 10 29.
Article in English | MEDLINE | ID: covidwho-894875

ABSTRACT

OBJECTIVE: Clinical trial data sharing has the potential to accelerate scientific progress, answer new lines of scientific inquiry, support reproducibility and prevent redundancy. Vivli, a non-profit organisation, operates a global platform for sharing of individual participant-level trial data and associated documents. Sharing of these data collected from each trial participant enables combining of these data to drive new scientific insights or assess reproducibility-not possible with the aggregate or summary data tables historically made available. We report on our initial experience including key metrics, lessons learned and how we see our role in the data sharing ecosystem. We also describe how Vivli is addressing the needs of the COVID-19 challenge through a new dedicated portal that provides a direct search function for COVID-19 studies, availability for fast-tracked request review and data sharing. DATA SUMMARY: The Vivli platform was established in 2018 and has partnered with 28 diverse members from industry, academic institutions, government platforms and non-profit foundations. Currently, 5400 trials representing 3.6 million participants are shared on the platform. From July 2018 to September 2020, Vivli received 201 requests. To date, 106 of 201 requests received approval, 5 have been declined, 27 withdrew and 27 are in the revision stage. CONCLUSIONS: The pandemic has only magnified the necessity for data sharing. If most data are shared and in a manner that allows interoperability, then we have hope of moving towards a cohesive scientific understanding more quickly not only for COVID-19 but also for all diseases. Conversely, if only isolated pockets of data are shared then society loses the opportunity to close vital gaps in our understanding of this rapidly evolving epidemic. This current challenge serves to highlight the value of data sharing platforms-critical enablers that help researchers build on prior knowledge.


Subject(s)
Clinical Trials as Topic , Coronavirus Infections , Data Management , Information Dissemination/methods , Information Services , Pandemics , Pneumonia, Viral , Public Health/trends , Betacoronavirus , Biomedical Research/methods , Biomedical Research/statistics & numerical data , Coronavirus Infections/prevention & control , Coronavirus Infections/therapy , Data Management/methods , Data Management/organization & administration , Data Management/trends , Humans , Information Services/organization & administration , Information Services/trends , Pandemics/prevention & control , Pneumonia, Viral/prevention & control , Pneumonia, Viral/therapy , Research Design
11.
J Psychiatr Res ; 132: 198-206, 2021 01.
Article in English | MEDLINE | ID: covidwho-894073

ABSTRACT

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.


Subject(s)
Biomedical Research/statistics & numerical data , Disease Outbreaks , Hemorrhagic Fever, Ebola , Influenza A Virus, H1N1 Subtype , Influenza, Human , Mental Health/statistics & numerical data , Periodicals as Topic/statistics & numerical data , Bibliometrics , Humans
12.
PLoS Biol ; 18(10): e3000913, 2020 10.
Article in English | MEDLINE | ID: covidwho-874141

ABSTRACT

The COVID-19 pandemic has motivated many open and collaborative analytical research projects with real-world impact. However, despite their value, such activities are generally overlooked by traditional academic metrics. Science is ultimately improved by analytical work, whether ensuring reproducible and well-documented code to accompany papers, developing and maintaining flexible tools, sharing and curating data, or disseminating analysis to wider audiences. To increase the impact and sustainability of modern science, it will be crucial to ensure these analytical activities-and the people who do them-are valued in academia.


Subject(s)
Biomedical Research , Coronavirus Infections/epidemiology , Pneumonia, Viral/epidemiology , Access to Information , Biological Science Disciplines/statistics & numerical data , Biomedical Research/statistics & numerical data , Pandemics , Publishing , Reward , Software , Universities
13.
Cancer Cell ; 38(5): 591-593, 2020 11 09.
Article in English | MEDLINE | ID: covidwho-837855

ABSTRACT

The COVID-19 pandemic is profoundly changing cancer researchers and cancer research. Leaders from different fields and at different career stages share their perspectives.


Subject(s)
Betacoronavirus/isolation & purification , Biomedical Research/statistics & numerical data , Biomedical Research/standards , Coronavirus Infections/epidemiology , Neoplasms/diagnosis , Neoplasms/therapy , Pneumonia, Viral/epidemiology , Coronavirus Infections/virology , Humans , Neoplasms/virology , Pandemics , Pneumonia, Viral/virology , United States/epidemiology
14.
BMJ Open ; 10(10): e044566, 2020 10 05.
Article in English | MEDLINE | ID: covidwho-835491

ABSTRACT

OBJECTIVES: To analyse enrolment to interventional trials during the first wave of the COVID-19 pandemic in England and describe the barriers to successful recruitment in the circumstance of a further wave or future pandemics. DESIGN: We analysed registered interventional COVID-19 trial data and concurrently did a prospective observational study of hospitalised patients with COVID-19 who were being assessed for eligibility to one of the RECOVERY, C19-ACS or SIMPLE trials. SETTING: Interventional COVID-19 trial data were analysed from the clinicaltrials.gov and International Standard Randomized Controlled Trial Number databases on 12 July 2020. The patient cohort was taken from five centres in a respiratory National Institute for Health Research network. Population and modelling data were taken from published reports from the UK government and Medical Research Council Biostatistics Unit. PARTICIPANTS: 2082 consecutive admitted patients with laboratory-confirmed SARS-CoV-2 infection from 27 March 2020 were included. MAIN OUTCOME MEASURES: Proportions enrolled, and reasons for exclusion from the aforementioned trials. Comparisons of trial recruitment targets with estimated feasible recruitment numbers. RESULTS: Analysis of trial registration data for COVID-19 treatment studies enrolling in England showed that by 12 July 2020, 29 142 participants were needed. In the observational study, 430 (20.7%) proceeded to randomisation. 82 (3.9%) declined participation, 699 (33.6%) were excluded on clinical grounds, 363 (17.4%) were medically fit for discharge and 153 (7.3%) were receiving palliative care. With 111 037 people hospitalised with COVID-19 in England by 12 July 2020, we determine that 22 985 people were potentially suitable for trial enrolment. We estimate a UK hospitalisation rate of 2.38%, and that another 1.25 million infections would be required to meet recruitment targets of ongoing trials. CONCLUSIONS: Feasible recruitment rates, study design and proliferation of trials can limit the number, and size, that will successfully complete recruitment. We consider that fewer, more appropriately designed trials, prioritising cooperation between centres would maximise productivity in a further wave.


Subject(s)
Biomedical Research , Coronavirus Infections , Pandemics , Patient Selection , Pneumonia, Viral , Randomized Controlled Trials as Topic , Betacoronavirus/isolation & purification , Biomedical Research/organization & administration , Biomedical Research/statistics & numerical data , Coronavirus Infections/epidemiology , Coronavirus Infections/therapy , Eligibility Determination , Female , Health Services Accessibility/statistics & numerical data , Hospitalization/statistics & numerical data , Humans , Male , Middle Aged , Pneumonia, Viral/epidemiology , Pneumonia, Viral/therapy , Prospective Studies , Randomized Controlled Trials as Topic/methods , Randomized Controlled Trials as Topic/statistics & numerical data , Registries/statistics & numerical data , United Kingdom
17.
Health Educ Behav ; 47(6): 861-869, 2020 12.
Article in English | MEDLINE | ID: covidwho-744938

ABSTRACT

When a pandemic outbreak occurs, it seems logical that related scientific production should increase substantially; however, it is important to recognize its interdisciplinary usefulness to find a solution to the problem. The main aim of this research is to analyse the main keywords of the scientific research about COVID-19, by subject area. To discover the influence of certain terms and their transferability, synergies, and future trends, a cluster analysis of the keywords was performed. The results show that Health Sciences dominate the publications with 88.23% of the total volume. As expected, the largest volume of research was dedicated to medical aspects of the disease, like experimental treatments, its physiopathology, or its respiratory syndrome. However, other fields, like Social Sciences (6.07%), Technology (2.68%), Physical Sciences (1.95%), and Arts and Humanities (1.08%), also played an important role in research on COVID-19.


Subject(s)
Bibliometrics , Coronavirus Infections/epidemiology , Periodicals as Topic/statistics & numerical data , Pneumonia, Viral/epidemiology , Research/statistics & numerical data , Betacoronavirus , Biomedical Research/statistics & numerical data , Humans , Pandemics , Social Sciences/statistics & numerical data
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