Paper mill or paper mine? A tentative answer to the sharp increase in research papers based on the Global Burden of Disease database.

OBJECTIVE: Recent studies have raised concerns arising from the exploitation ("mining") of public health databases for low-quality, mass-produced papers. However, it remains challenging to disambiguate whether such papers originate from paper mills (commercial entities that sell authorships on mass-produced papers) or from the uncoordinated action of individuals facilitated by AI tools and templated workflows. Our study aims to address this question for one particular database, the Global Burden of Disease Study (GBD). We selected this database after noticing that one of our papers on Bayesian age-period-cohort models has recently been experiencing a rapid surge in geographically clustered citations from GBD papers with Chinese affiliations. METHODS: We collected bibliometric and article-level metadata from GBD papers to search for indicators of mass-produced research. Moreover, we assessed 713 full-text articles for reported R versions, availability of code and data, and declaration of generative AI use. For 180 articles, we qualitatively screened the figures for graphical similarities. Finally, we conducted an exploratory scoping investigation of online platforms (social media sites, vendor websites) dedicated to do-it-yourself workflows for secondary analyses of public health data. RESULTS: Although we cannot rule out paper mill involvement, our findings suggest that the geographically clustered increase in GBD publications from China is at least partially driven by independent authors. The wide variety of R versions listed in 477 articles points against centralized paper production. Moreover, despite broad graphical similarities in figure styles that suggest the use of shared visualization tools, substantial variation in ancillary details suggest independent authors finalizing figures. This is corroborated by the identification of an online ecosystem of proprietary tools and services specializing in streamlined do-it-yourself workflows for conducting, writing, and publishing secondary analyses of public health data. CONCLUSION: Appropriate efforts should be directed towards evaluating the quality of the identified workflows. Stakeholders in scientific integrity should monitor online platforms dedicated to the rapid production of papers, especially in light of the increasing focus on AI-assisted workflows. Code sharing should be mandated for data-driven secondary analyses. Paywalls and proprietary software licenses hinder transparency and reusability, underscoring the importance of free and open-source software for trustworthy and reproducible research.