Author:
Luo Jiefeng,Chen Zhe,Liu Dan,Li Hailong,He Siyi,Zeng Linan,Yang Mengting,Liu Zheng,Xiao Xue,Zhang Lingli
Abstract
Abstract
Objectives
The main objective of this study is to evaluate the methodological quality and reporting quality of living systematic reviews (LSRs) on Coronavirus disease 2019 (COVID-19), while the secondary objective is to investigate potential factors that may influence the overall quality of COVID-19 LSRs.
Methods
Six representative databases, including Medline, Excerpta Medica Database (Embase), Cochrane Library, China national knowledge infrastructure (CNKI), Wanfang Database, and China Science, Technology Journal Database (VIP) were systematically searched for COVID-19 LSRs. Two authors independently screened articles, extracted data, and then assessed the methodological and reporting quality of COVID-19 LSRs using the "A Measurement Tool to Assess systematic Reviews-2" (AMSTAR-2) tool and "Preferred Reporting Items for Systematic reviews and Meta-Analyses" (PRISMA) 2020 statement, respectively. Univariate linear regression and multivariate linear regression were used to explore eight potential factors that might affect the methodological quality and reporting quality of COVID-19 LSRs.
Results
A total of 64 COVID-19 LSRs were included. The AMSTAR-2 evaluation results revealed that the number of "yes" responses for each COVID-19 LSR was 13 ± 2.68 (mean ± standard deviation). Among them, 21.9% COVID-19 LSRs were rated as "high", 4.7% as "moderate", 23.4% as "low", and 50% as "critically low". The evaluation results of the PRISMA 2020 statement showed that the sections with poor adherence were methods, results and other information. The number of "yes" responses for each COVID-19 LSR was 21 ± 4.18 (mean ± standard deviation). The number of included studies and registration are associated with better methodological quality; the number of included studies and funding are associated with better reporting quality.
Conclusions
Improvement is needed in the methodological and reporting quality of COVID-19 LSRs. Researchers conducting COVID-19 LSRs should take note of the quality-related factors identified in this study to generate evidence-based evidence of higher quality.
Publisher
Springer Science and Business Media LLC
Subject
Health Informatics,Epidemiology
Reference103 articles.
1. Lu H, Stratton CW, Tang YW. Outbreak of pneumonia of unknown etiology in Wuhan, China: The mystery and the miracle. J Med Virol. 2020;92(4):401–2.
2. WHO[homepage on the Internet]. Naming the coronavirus disease (COVID-19) and the virus that causes it. 2020–02–11; Available from:https://www.who.int/emergencies/diseases/novel-coronavirus-2019/technical-guidance/naming-the-coronavirus-disease-(covid-2019)-and-the-virus-that-causes-it, 2022–05–09.
3. World Health Organization[homepage on the Internet]. WHO Coronavirus Disease (COVID-19) Dashboard. 2022; Available from:https://covid19.who.int/. Accessed 03–09, 2022.
4. Zarocostas J. How to fight an infodemic. Lancet (London, England). 2020;395(10225):676.
5. Casigliani V, De Nard F, De Vita E, et al. Too much information, too little evidence: is waste in research fuelling the covid-19 infodemic? BMJ. 2020;370: m2672.
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献