Authorship inequality: a bibliometric study of the concentration of authorship among a diminishing number of individuals in high-impact medical journals, 2008–2019

Abstract

ObjectiveAuthorship and number of publications are important criteria used for making decisions about promotions and research funding awards. Given the increase in the number of author positions over the last few decades, this study sought to determine if there had been a shift in the distribution of authorship among those publishing in high-impact academic medical journals over the last 12 years.DesignThis study analysed the distribution of authorship across 312 222 original articles published in 134 medium-impact to high-impact academic medical journals between 1 January 2008 and 31 December 2019. Additionally, this study compared the trends in author distributions across nine medical specialties and a collection of cross-specialty high-impact journal articles.Primary outcome measuresThe distribution of authorship was assessed using the Gini coefficient (GC), a widely used measure of economic inequality.ResultsThe overall GC for all articles sampled across the 12-year study period was 0.49, and the GCs for the first and last authorship positions were 0.30 and 0.44, respectively. Since 2008, there was a significant positive correlation between year and GC for the overall authorship position (r=0.99, p<0.001) the first author position (r=0.75, p=0.007) and the last author position (r=0.85, p<0.001) indicating increasingly uneven distribution in authorship over time. The cross-specialty high-impact journals exhibited the greatest rate of increase in GC over the study period for the first and last author position of any specialty analysed.ConclusionOverall, these data suggest a growing inequality in authorship across authors publishing in high-impact academic medical journals, especially among the highest impact journals. These findings may have implications for processes such as promotions and allocation of research funding that use authorship metrics as key criteria for making decisions.