Does diversity beget diversity? A scientometric analysis of over 150,000 studies and 49,000 authors published in high-impact medical journals between 2007 and 2022

Abstract

Background: Health research that significantly impacts global clinical practice and policy is often published in high-impact factor (IF) medical journals. These outlets play a pivotal role in the worldwide dissemination of novel medical knowledge. However, researchers identifying as women and those affiliated with institutions in low- and middle-income countries (LMIC) have been largely underrepresented in high-IF journals across multiple fields of medicine. To evaluate disparities in gender and geographical representation among authors who have published in any of five top general medical journals, we conducted scientometric analyses using a large-scale dataset extracted from the New England Journal of Medicine (NEJM), Journal of the American Medical Association (JAMA), The British Medical Journal (BMJ), The Lancet, and Nature Medicine.

Methods: Author metadata from all articles published in the selected journals between 2007 and 2022 were collected using the DimensionsAI platform. The Genderize.io API was then utilized to infer each author's likely gender based on their extracted first name. The World Bank country classification was used to map countries associated with researcher affiliations to the LMIC or the high-income country (HIC) category. We characterized the overall gender and country income category representation across the medical journals. In addition, we computed article-level diversity metrics and contrasted their distributions across the journals.

Findings: We studied 151,536 authors across 49,764 articles published in five top medical journals, over a long period spanning 15 years. On average, approximately one-third (33.1%) of the authors of a given paper were inferred to be women; this result was consistent across the journals we studied. Further, 86.6% of the teams were exclusively composed of HIC authors; in contrast, only 3.9% were exclusively composed of LMIC authors. The probability of serving as the first or last author was significantly higher if the author was inferred to be a man (18.1% vs 16.8%, P < .01) or was affiliated with an institution in a HIC (16.9% vs 15.5%, P < .01). Our primary finding reveals that having a diverse team promotes further diversity, within the same dimension (i.e., gender or geography) and across dimensions. Notably, papers with at least one woman among the authors were more likely to also involve at least two LMIC authors (11.7% versus 10.4% in baseline, P < .001; based on inferred gender); conversely, papers with at least one LMIC author were more likely to also involve at least two women (49.4% versus 37.6%, P < .001; based on inferred gender).

Conclusion: We provide a scientometric framework to assess authorship diversity. Our research suggests that the inclusiveness of high-impact medical journals is limited in terms of both gender and geography. We advocate for medical journals to adopt policies and practices that promote greater diversity and collaborative research. In addition, our findings offer a first step towards understanding the composition of teams conducting medical research globally and an opportunity for individual authors to reflect on their own collaborative research practices and possibilities to cultivate more diverse partnerships in their work.

Figure 1.

Methodological pipeline to analyze authorship diversity in scientific publications (a) and flow diagram depicting exclusion criteria for publications and authors (b).

Figure 2.

Gender (a) and geographical (b) diversity, in terms of team composition, stratified by journal. (a) Bars represent the scenario in which authors with missing gender information are removed from the dataset, while error ranges represent the optimistic and pessimistic imputation strategies described in the Methods section. (b) All numbers refer to the scenario in which authors with missing information are excluded; pessimistic and optimistic scenarios can be found in the Supplemental Figures 3a and 3b.

Figure 3.

Conditional probabilities of being first and last author given author’s gender and the LMICstatus of their affiliation. The asterisk (*) indicates a statistically significant difference between two groups (e.g., author is from LMIC versus is from HIC), with a p-value less than 0.05/4 = 0.0125, as determined by a Chi-squared test, after applying a Bonferroni correction for a familywise error rate of 0.05 and 4 tests. The error bars represent a 95% confidence interval, computed via 100-iteration bootstrap.

Figure 4.

Selected conditional probabilities to assess the impact of team composition in its diversity. Green cells indicate a statistically significant difference between groups (e.g., there is at least one female author in the team versus there is no female author), with a p-value less than 0.05/6 = 0.0083, as determined by a Chi-squared test, after applying a Bonferroni correction for a familywise error rate of 0.05 and 6 tests.