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Global Trends and Research Hotspots in Long COVID: A Bibliometric Analysis

Author

Listed:
  • Hongxia Jin

    (Institute of Disaster and Emergency Medicine, Tianjin University, Tianjin 300072, China)

  • Lu Lu

    (Institute of Disaster and Emergency Medicine, Tianjin University, Tianjin 300072, China)

  • Haojun Fan

    (Institute of Disaster and Emergency Medicine, Tianjin University, Tianjin 300072, China)

Abstract

Long COVID is a condition distinguished by long-term sequelae that occur or persist after the convalescence period of COVID-19. During the COVID-19 pandemic, more and more people who tested positive for SARS-CoV-2 experienced long COVID, which attracted the attention of researchers. This study aims to assess the pattern of long COVID research literature, analyze the research topics, and provide insights on long COVID. In this study, we extracted 784 publications from Scopus in the field of long COVID. According to bibliometric analysis, it is found that: developed countries in Europe and America were in leading positions in terms of paper productivity and citations. The International Journal of Environmental Research and Public Health and the Journal of Clinical Medicine were leading journals in the perspective of publications count, and Nature Medicine had the highest number of citations. Author Greenhalgh T has the highest number of papers and citations. The main research topics were: pathophysiology, symptoms, treatment, and epidemiology. The causes of long COVID may be related to organ injury, inflammation, maladaptation of the angiotensin-converting enzyme 2 (ACE2) pathway, and mental factors. The symptoms are varied, including physical and psychological symptoms. Treatment options vary from person to person. Most patients developed at least one long-term symptom. Finally, we presented some possible research opportunities.

Suggested Citation

  • Hongxia Jin & Lu Lu & Haojun Fan, 2022. "Global Trends and Research Hotspots in Long COVID: A Bibliometric Analysis," IJERPH, MDPI, vol. 19(6), pages 1-14, March.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:6:p:3742-:d:776228
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    References listed on IDEAS

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    5. Yueran Duan & Qing Guan, 2021. "Predicting potential knowledge convergence of solar energy: bibliometric analysis based on link prediction model," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(5), pages 3749-3773, May.
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    Cited by:

    1. Gour Gobinda Goswami & Tahmid Labib, 2022. "Modeling COVID-19 Transmission Dynamics: A Bibliometric Review," IJERPH, MDPI, vol. 19(21), pages 1-19, October.

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