COVID-19-related medical research: a meta-research and critical appraisal.


Journal

BMC medical research methodology
ISSN: 1471-2288
Titre abrégé: BMC Med Res Methodol
Pays: England
ID NLM: 100968545

Informations de publication

Date de publication:
04 01 2021
Historique:
received: 29 09 2020
accepted: 08 12 2020
entrez: 5 1 2021
pubmed: 6 1 2021
medline: 26 1 2021
Statut: epublish

Résumé

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. 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. 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). 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/.

Sections du résumé

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/.

Identifiants

pubmed: 33397292
doi: 10.1186/s12874-020-01190-w
pii: 10.1186/s12874-020-01190-w
pmc: PMC7780085
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't Systematic Review

Langues

eng

Sous-ensembles de citation

IM

Pagination

1

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Auteurs

Marc Raynaud (M)

Paris Translational Research Epidemiology and Biostatistics Department, Hôpital Necker, 149 rue de Sèvres, 75015, Paris, France.

Huanxi Zhang (H)

The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China.

Kevin Louis (K)

Paris Translational Research Epidemiology and Biostatistics Department, Hôpital Necker, 149 rue de Sèvres, 75015, Paris, France.

Valentin Goutaudier (V)

Paris Translational Research Epidemiology and Biostatistics Department, Hôpital Necker, 149 rue de Sèvres, 75015, Paris, France.
Montpellier University Hospital, Montpellier, France.

Jiali Wang (J)

The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China.

Quentin Dubourg (Q)

Pitié-Salpêtrière University Hospital, Assistance Publique-Hôpitaux de Paris, Sorbonne University, Paris, France.

Yongcheng Wei (Y)

The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China.

Zeynep Demir (Z)

Paris Translational Research Epidemiology and Biostatistics Department, Hôpital Necker, 149 rue de Sèvres, 75015, Paris, France.
Paediatrics Unit, Necker University Hospital, Paris, France.

Charlotte Debiais (C)

Paris Translational Research Epidemiology and Biostatistics Department, Hôpital Necker, 149 rue de Sèvres, 75015, Paris, France.

Olivier Aubert (O)

Paris Translational Research Epidemiology and Biostatistics Department, Hôpital Necker, 149 rue de Sèvres, 75015, Paris, France.

Yassine Bouatou (Y)

Paris Translational Research Epidemiology and Biostatistics Department, Hôpital Necker, 149 rue de Sèvres, 75015, Paris, France.

Carmen Lefaucheur (C)

Immunology and Nephrology Department, Saint Louis Hospital, Paris, France.

Patricia Jabre (P)

Cochrane Pre-hospital and Emergency Care, Necker University Hospital, Paris, France.

Longshan Liu (L)

The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China.

Changxi Wang (C)

The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China.

Xavier Jouven (X)

Paris Translational Research Epidemiology and Biostatistics Department, Hôpital Necker, 149 rue de Sèvres, 75015, Paris, France.

Peter Reese (P)

Paris Translational Research Epidemiology and Biostatistics Department, Hôpital Necker, 149 rue de Sèvres, 75015, Paris, France.
University of Pennsylvania School of Medicine, Philadelphia, PA, USA.

Jean-Philippe Empana (JP)

Paris Translational Research Epidemiology and Biostatistics Department, Hôpital Necker, 149 rue de Sèvres, 75015, Paris, France.

Alexandre Loupy (A)

Paris Translational Research Epidemiology and Biostatistics Department, Hôpital Necker, 149 rue de Sèvres, 75015, Paris, France. alexandre.loupy@inserm.fr.

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Classifications MeSH