COVID-19 meta-analyses: a scoping review and quality assessment

• Autor(a) principal: Pires,Gabriel Natan
• Data de Publicação: 2021
• Outros Autores: Bezerra,Andréia Gomes, Oliveira,Thainá Baenninger de, Chen,Samuel Fen I, Malfatti,Victor Davis Apostolakis, Mello,Victoria Feiner Ferreira de, Niyama,Alyne, Pinto,Vitor Luiz Selva, Andersen,Monica Levy, Tufik,Sergio
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Einstein (São Paulo)
• Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1679-45082021000100204
• topic: Bibliometrics; Coronavirus; COVID-19; Meta-analysis; Betacoronavirus; SARS-CoV-2; Scientometrics; Systematic review

ABSTRACT Objective: To carry out a scoping review of the meta-analyses published regarding about coronavirus disease 2019 (COVID-19), evaluating their main characteristics, publication trends and methodological quality. Methods: A bibliometric search was performed in PubMed®, Scopus and Web of Science, focusing on meta-analyses about COVID-2019 disease. Bibliometric and descriptive data for the included articles were extracted and the methodological quality of the included meta-analyses was evaluated using A Measurement Tool to Assess Systematic Reviews. Results: A total of 348 meta-analyses were considered eligible. The first meta-analysis about COVID-19 disease was published on February 26, 2020, and the number of meta-analyses has grown rapidly since then. Most of them were published in infectious disease and virology journals. The greatest number come from China, followed by the United States, Italy and the United Kingdom. On average, these meta-analyses included 23 studies and 15,200 participants. Overall quality was remarkably low, and only 8.9% of them could be considered as of high confidence level. Conclusion: Although well-designed meta-analyses about COVID-19 disease have already been published, the majority are of low quality. Thus, all stakeholders playing a role in COVID-19 deseases, including policy makers, researchers, publishers and journals, should prioritize well-designed meta-analyses, performed only when the background information seem suitable, and discouraging those of low quality or that use suboptimal methods.