Gender and ethnic differences in publication of BMJ letters to the editor: an observational study using machine learning

BMJ Open. 2020 Dec 21;10(12):e037269. doi: 10.1136/bmjopen-2020-037269.

Abstract

Objectives: To analyse the relationship between first author's gender and ethnicity (estimated from first name and surname), and chance of publication of rapid responses in the British Medical Journal (BMJ). To analyse whether other features of the rapid response account for any gender or ethnic differences, including the presence of multiple authors, declaration of conflicts of interests, the presence of Twitter handle, word count, reading ease, spelling and grammatical mistakes, and the presence of references.

Design: A retrospective observational study.

Setting: Website of the BMJ (BMJ.com).

Participants: Publicly available rapid responses submitted to BMJ.com between 1998 and 2018.

Main outcome measures: Publication of a rapid response as a letter to the editor in the BMJ.

Results: We analysed 113 265 rapid responses, of which 8415 were published as letters to the editor (7.4%). Statistically significant univariate correlations were found between odds of publication and first author estimated gender and ethnicity, multiple authors, declaration of conflicts of interest, the presence of Twitter handle, word count, reading ease, spelling and grammatical mistakes, and the presence of references. Multivariate analysis showed that first author estimated gender and ethnicity predicted publication after taking into account the other factors. Compared to white authors, black authors were 26% less likely to be published (OR: 0.74, CI: 0.57-0.96), Asian and Pacific Islander authors were 46% less likely to be published (OR: 0.54, CI: 0.49-0.59) and Hispanic authors were 49% less likely to be published (OR: 0.51, CI: 0.41-0.64). Female authors were 10% less likely to be published (OR: 0.90, CI: 0.85-0.96) than male authors.

Conclusion: Ethnic and gender differences in rapid response publication remained after accounting for a broad range of features, themselves all predictive of publication. This suggests that the reasons for the differences of these groups lies elsewhere.

Keywords: epidemiology; health economics; health informatics; health policy; medical education & training; statistics & research methods.

Publication types

  • Observational Study

MeSH terms

  • Black or African American
  • Ethnicity*
  • Female
  • Humans
  • Machine Learning
  • Male
  • Retrospective Studies
  • White People*