Bibliometric mapping of microbiology research topics (2012–16): a comparison by socioeconomic development and infectious disease vulnerability values

Dehdarirad, Tahereh; Sotudeh, Hajar; Freer, Jonathan

doi:10.1093/femsle/fnz004

Cited by 12 publications

(9 citation statements)

References 27 publications

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“…14 Another study also assessed themes in microbiology and identified broad themes (e.g., animal models). 13 Our results offer a greater granularity and a more extensive variety of topics (not exclusively microbiological), which can be grouped into these existing microbiological themes. Organism-specific trends were verified in the existing literature.…”

Section: General Topic Overviewmentioning

confidence: 77%

“…[9][10][11][12] Other studies aimed at broader levels by identifying research topics in microbiology between 1945-2016 and 2012-2016. 13,14 These approaches were either too narrow by investigating merely parts of the AMR field or too broad to identify AMR and related subgroups. So far, no comprehensive study is available to provide information on global AMR research activities.…”

Section: Introductionmentioning

confidence: 99%

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Mapping Twenty Years of Antimicrobial Resistance Research Trends

et al. 2021

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BackgroundAntimicrobial resistance (AMR) is a global threat to health and healthcare. In response to the growing AMR burden, research funding also increased. However, a comprehensive overview of the research output, including conceptual, temporal, and geographical trends, is missing.Therefore, this study uses topic modelling, a machine learning approach, to reveal the scientific evolution of AMR research and its trends, and provides an interactive user interface for further analyses. MethodsStructural topic modelling (STM) was applied on a text corpus resulting from a PubMed query comprising AMR articles (1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017)(2018). A topic network was established and topic trends were analysed by frequency, proportion, and importance over time and space. FindingsIn total, 88 topics were identified in 158616 articles from 166 countries. AMR publications increased by 450% between 1999 and 2018, emphasizing the vibrancy of the field. Prominent topics in 2018 were Strategies for emerging resistances and diseases, Nanoparticles, and Stewardship. Emerging topics included Water and environment, and Sequencing.Geographical trends showed prominence of Multidrug-resistant tuberculosis (MDR-TB) in the WHO African Region, corresponding with the MDR-TB burden. China and India were growing contributors in recent years, following the United States of America as overall lead contributor. InterpretationThis study provides a comprehensive overview of the AMR research output thereby revealing the AMR research response to the increased AMR burden. Both the results and the publicly available interactive database serve as a base to inform and optimise future research.

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Section: General Topic Overviewmentioning

confidence: 77%

Section: Introductionmentioning

confidence: 99%

Mapping Twenty Years of Antimicrobial Resistance Research Trends

et al. 2021

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“…The ones most closely related to our research methods are Liu et al [ 11 ] and Dehdarirad et al, [ 12 ] but the study of Liu et al [ 11 ] did not involve assigning MeSH terms. Only Tooth injuries was searched as the MeSH within PubMed.…”

Section: Discussionmentioning

confidence: 99%

“…Only Tooth injuries was searched as the MeSH within PubMed. Also, Dehdarirad et al [ 12 ] 's term map was based on MeSH in VOSviewer's built-in function. Most studies have visualized with VOSviewer after using MeSH in search strategies or matching with text words.…”

Section: Discussionmentioning

confidence: 99%

“…Most studies have visualized with VOSviewer after using MeSH in search strategies or matching with text words. [ 12 – 21 ] For example, Gan et al [ 13 ] and Hernandez-Vasquez et al [ 14 ] did use text words to search and extract MeSH terms for co-word analysis. Mazaheri et al [ 16 ] did not involve AKs assigned by MeSH but investigated AKs matching or not matching with index term categories.…”

Section: Discussionmentioning

confidence: 99%

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