Characteristics of scientific articles on COVID-19 published during the initial three months of the pandemic: protocol for a meta-epidemiological study
Creators: name: Meursinge Reynders, Reint
name: Di Girolamo, Nicola
Publisher: name: Open Science Framework
Year: 2022
Subjects: subject: Medicine and Health Sciences

description: This manuscript presents a protocol for a meta-epidemiological study on the characteristics of scientific articles on COVID-19 published during the initial three months of the pandemic. TITLE Characteristics of scientific articles on COVID-19 published during the initial three months of the pandemic: protocol for a meta-epidemiological study AUTHORS Nicola Di Girolamo and Reint Meursinge Reynders ORCID NUMBERS Nicola Di Girolamo ORCID: 0000-0001-5203-9765 Reint Meursinge Reynders ORCID: 0000-0002-2233-9748 AFFILIATIONS AUTHORS Nicola Di Girolamo 1,2 (Associate professor and editor) 1 Center for veterinary health sciences, Oklahoma State University, 2065 W. Farm Road, Stillwater, Oklahoma 74078, USA. 2 EBMVet, Via Sigismondo Trecchi 20, Cremona CR 26100, Italy. Email: nicoladiggi@gmail.com Reint Meursinge Reynders 3,4 (Senior scientist and associate editor) 3 Department of oral and maxillofacial surgery, Amsterdam University Medical Center, University of Amsterdam, Meibergdreef 9, 1105 AZ Amsterdam, The Netherlands. 4 Private practice of orthodontics, Via Matteo Bandello 15, 20123 Milan, Italy. Email: reyndersmail@gmail.com CORRESPONDING AUTHOR Nicola Di Girolamo1,2 (Associate professor and editor) 1 Center for veterinary health sciences, Oklahoma State University, 2065 W. Farm Road, Stillwater, Oklahoma 74078, USA. 2 EBMVet, Via Sigismondo Trecchi 20, Cremona CR 26100, Italy. Email: nicoladiggi@gmail.com ABSTRACT Objectives: (1) What is the prevalence of primary and secondary articles on COVID-19 in the first 3 months of the pandemic? (2) What are the characteristics of both primary and secondary articles on COVID-19? (3) How does the COVID-19 publishing pattern compare with the publishing pattern of the last pandemic, i.e., Swine Flu pandemic? Design: Meta-epidemiological cross-sectional study Study sample: All research articles on Covid-19 indexed in PubMed (Medline) up to 2 April 2020. Main outcome measures: Predominantly prevalence statistics KEYWORDS Covid-19; Coronavirus; SARS-nCoV-2; Swine Flu; H1N1 strain; study design; research quality; healthcare policy; evidence-based medicine INTRODUCTION The outbreak of the coronavirus disease 2019 (COVID-19) pandemic has led to prolific publishing on this health issue. Here, we will assess the scientific literature to analyze the characteristics of articles published on COVID-19 during the first 3 months of this pandemic. We will assess the type, quantity, and quality of this literature to get an insight in the available knowledge base for controlling this pandemic early on. We will divide the literature in primary and secondary articles. Primary articles refer to original research studies that create new or confirming data on a problem, e.g. an health issue. Secondary articles use these data to assess the current knowledge status on this problem. We will also compare the publishing pattern during the first 3 months of the COVID-19 pandemic with this pattern during the most recent pandemic, i.e., the H1N1 pandemic. OBJECTIVES Our main objectives are summarized in the following 3 research questions: (1) What is the prevalence of primary and secondary articles on COVID-19 in the first 3 months of the pandemic? (2) What are the characteristics of both primary and secondary articles? (3) How does the publishing pattern during the first 3 months of the COVID-19 pandemic compare with the publishing pattern of the last pandemic, i.e., Swine Flu pandemic? METHODS Type of study We will conduct a cross-sectional (meta-epidemiological) study. Reporting of the study We will use Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement as the framework for reporting this study (von Elm 2007). We will report our methods for the Swine Flu sample at the end of the methods section. Eligibility criteria Any type of article published on COVID-19 will be eligible. This implies that a broad spectrum of articles ranging from letters to the editors to randomized controlled trials will be included. No eligibility criteria will be applied to specific participants, interventions, comparators, outcomes, endpoints, or settings of the articles. Articles that did not present an abstract as well as abstracts that were not reported in English will be excluded. Information sources and search strategy We will search eligible articles on COVID-19 in PubMed (MEDLINE) till April 2 2020. Our search strategy for PubMed (MEDLINE) is presented in Table 1 and will be prepared for the eligible search dates. We will use the search string “(COVID-19 OR COVID)” and apply the filter ‘Abstract’. Table 1. Search strategy for COVID-19 Search: COVID-19 OR COVID Filters: Abstract ((((((("covid 19"[All Fields] OR "covid 2019"[All Fields]) OR "severe acute respiratory syndrome coronavirus 2"[Supplementary Concept]) OR "severe acute respiratory syndrome coronavirus 2"[All Fields]) OR "2019 ncov"[All Fields]) OR "sars cov 2"[All Fields]) OR "2019ncov"[All Fields]) OR (("wuhan"[All Fields] AND ("coronavirus"[MeSH Terms] OR "coronavirus"[All Fields])) AND (2019/12/1:2019/12/31[Date - Publication] OR 2020/1/1:2020/12/31[Date - Publication]))) OR "COVID"[All Fields] Translations COVID-19: "COVID-19"[All Fields] OR "COVID-2019"[All Fields] OR "severe acute respiratory syndrome coronavirus 2"[Supplementary Concept] OR "severe acute respiratory syndrome coronavirus 2"[All Fields] OR "2019-nCoV"[All Fields] OR "SARS-CoV-2"[All Fields] OR "2019nCoV"[All Fields] OR (("Wuhan"[All Fields] AND ("coronavirus"[MeSH Terms] OR "coronavirus"[All Fields])) AND (2019/12[PDAT] OR 2020[PDAT])) Selection of articles and data extraction Two operators (Nicola di Girolamo and Reint Meursinge Reynders) will conduct the selection of articles and the data extraction procedures independently. These operators pilot tested these methods on 40 articles to calibrate operators and to fine-tune research methods, e.g., the data extraction forms (Table 2). Disagreements during the selection of articles and data extraction procedures will be resolved through discussions between these operators. A methodologist will be consulted in the case of persisting disagreements. We will report the flow of our selection of articles and will include our raw data with the final research study. Table 2. Data extraction forms Title: Report the title of the article PMID: Report the PubMed-Indexed for MEDLINE (PMID) Authors: Report the authors of the article Citation: Report the citations of the article First author: Report the first author of the article Journal/Book: Report the name of the journal or book Publication year: Report the year of publication of the article Creation date: Report the date of the creation of the Digital Object Identifier (DOI) DOI: Report the DOI DOI LINK: Report the link to the DOI Language of full text: Report the language in which the full text of the article was published Language if not English: Report the language of the full text if not English Country: Report the country of the first institution of the first author Type of study: Report the type of study, e.g., human medical research/in vitro/in silico/review/guidelines etc. Study design: Report the study design. Only the design of human medical research studies will be reported Sample size: Report the sample size. Only the sample size of human medical research studies will be reported Objectives: Report whether the objectives of the article were reported in the abstract. Possible answers: Yes/No Limitations/problems reported in the abstract: Report whether limitations/or problems of the article were reported in the abstract. Possible answers: Yes/No Additional comments: Report possible additional comments Conclusions: Copy and paste the conclusions reported in the abstract Classification of the included articles We will classify each included article as either a primary or a secondary article. Primary articles cover original research studies and secondary articles cover perspectives and syntheses of the available knowledge on COVID-19 such as, viewpoints, commentaries, guidelines, reviews etc. Primary articles will be divided in 5 groups, i.e., human medical research, in silico, in vitro, animal research, and surveys or studies on health professionals. These groups will be further subdivided according to the pertinent research designs. Our classification of articles will not be exclusively based on the labels assigned by the authors of the articles, because of the potential risk of mislabeling (Esene 2014). We will weigh the author’ labels before assigning our final classification. Abstract assessment We will screen the abstracts to assess whether objectives and limitations of the article were reported or not. Limitations will be subdivided in methodological and general limitations. The former refers to reporting of at least one methodological limitation in the abstract. General limitations refer to reporting some sort of limitation not inherent to the design of the paper, e.g., further research on this topic is needed etc. Information sources and search strategy for the Swine Flu sample We will search PubMed (MEDLINE) for eligible articles on H1N1 during the first 3 months of the Swine Flu outbreak (04/25/09-7/25/09). Our search strategy for PubMed (MEDLINE) is presented in Table 3. We will use the search string “((H1N1) OR "swine influenza")” and apply the filter ‘Abstract’. This search strategy will be prepared for the eligible search dates. Table 3. Search strategy for H1N1 Search: (H1N1) OR (swine influenza) Filters: Abstract "H1N1"[All Fields] OR (((("orthomyxoviridae infections"[MeSH Terms] OR ("orthomyxoviridae"[All Fields] AND "infections"[All Fields])) OR "orthomyxoviridae infections"[All Fields]) OR ("swine"[All Fields] AND "influenza"[All Fields])) OR "swine influenza"[All Fields]) Translations swine influenza: "orthomyxoviridae infections"[MeSH Terms] OR ("orthomyxoviridae"[All Fields] AND "infections"[All Fields]) OR "orthomyxoviridae infections"[All Fields] OR ("swine"[All Fields] AND "influenza"[All Fields]) OR "swine influenza"[All Fields] Selection of articles, data extraction, and classification of the included articles for the Swine Flu sample For the selection of articles, data extraction, and the classification of the included articles for the Swine Flu sample we will adopt the methods presented for the COVID-19 sample. We will report on the classification of primary and secondary articles in this sample. Data analysis We will use descriptive statistics expressed as medians with interquartile ranges (IQR) and ranges or absolute counts and percentages. Multivariate logistic regression models will be developed to explore factors associated with the primary outcomes and to provide odds ratio adjusted for confounders (Peng &amp; So, 2002; Heck, Thomas &amp; Tabata, 2013). Variables will be retained in the models regardless of their statistical significance. Goodness of fit will be assessed with Hosmer-Lemshow test and Nagelkerke R squared. Data analyses and figures will be done with SPSS (version 24, IBM) and R 3.6.3 (R Core Team, 2020, www.R-project.org/). Outcomes Table 4 summarizes our planned outcomes. For most of these outcomes we will calculate prevalence statistics. We did not involve patients or other stakeholders in developing our research questions or outcomes. Table 4. Summary of planned outcomes Published papers on COVID-19 Primary and secondary articles on COVID-19 Type of primary articles on COVID-19 Research design of primary articles on COVID-19 Type of secondary articles on COVID-19 Articles on COVID-19 per country Journals on COVID-19 Reporting of objectives in the abstract in included articles on COVID-19 Reporting of limitations in the abstract in included articles on COVID-19 Published papers on H1N1 Primary and secondary articles on H1N1 Type of primary articles on H1N1 Research design of primary articles on H1N1 Type of secondary articles on H1N1 Differences in publishing trends between COVID-19 and H1N1 Differences between the protocol and the final study Originally we also planned to extract whether included studies were part of multi center projects. After the completion of our pilot tests we decided not to extract these data, because they could not be extracted reliably. This manuscript represents the latest version of our protocol, i.e., the protocol after the completion of our pilot tests. All differences between this protocol and the methods used in the study will be reported in the final manuscript of the study with rationale. REFERENCES Esene 2014 Esene IN, Ngu J, El Zoghby M, et al. Case series and descriptive cohort studies in neurosurgery: the confusion and solution. Childs Nerv Syst. 2014;30(8):1321–1332. doi:10.1007/s00381-014-2460-1. Von Elm 2007 von Elm E, Altman DG, Egger M, Pocock SJ, Gøtzsche PC, Vandenbroucke JP; STROBE Initiative. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. Ann Intern Med. 2007 Oct 16;147(8):573-7.