Visual analysis of hotspots and trends in global disease burden research based on CiteSpace
10.3969/j.issn.1006-2483.2026.01.007
- VernacularTitle:基于Citespace可视化疾病负担研究热点和趋势分析
- Author:
Jing XU
1
;
Yuanyuan XU
2
;
Yunshang CUI
2
Author Information
1. Chinese Preventive Medicine Association, Beijing 100062 , China
2. Chinese Center for Disease Control and Prevention, Beijing 100026, China
- Publication Type:Journal Article
- Keywords:
Disease burden;
Bibliometrics;
CiteSpace;
Visual analysis;
Research trends
- From:
Journal of Public Health and Preventive Medicine
2026;37(1):34-39
- CountryChina
- Language:Chinese
-
Abstract:
Objective To analyze the current status, hotspots, and trends of global disease burden research from 2015 to 2024 based on bibliometric methods, and to provide references for public health policy-making and academic research. Methods Disease burden-related literature was retrieved from the Web of Science Core Collection database, and visual analysis was conducted using CiteSpace 6.2.R4 software, including publication volume, subject distribution, national/institutional collaboration, author co-citation networks, keyword co-occurrence, clustering, and burst analysis. Results A total of 1 852 valid articles were included. The annual publication volume showed a growing trend, and entered a rapid growth phase after 2020 (with an average annual growth rate of 17.24%). The United States, China, the United Kingdom, and Germany had the highest publication volumes, with public health, internal medicine, and epidemiology being the main subject areas. The author co-citation network indicated that Younossi ZM, Lozano R, and others were core authors, while the Chinese University of Hong Kong and Aga Khan University were at the core of the institutional collaboration network. High-frequency keywords were focused on “prevalence”, “mortality”, and “risk factors”, and clustering analysis formed seven themes, including disease burden assessment methods (such as disability-adjusted life years), specific diseases (such as chronic obstructive pulmonary disease), and data resources (such as National Health and Nutrition Examination Survey). Burst analysis divided the research into three stages: 2015-2017 focused on infectious diseases (such as hepatitis C), 2018-2019 shifted to chronic non-communicable diseases (such as metabolic syndrome), and 2020-2024 focused on emerging fields such as viral-related diseases and cancer treatment. Conclusion Global disease burden research exhibits characteristics of multidisciplinary crossover. In the future, it is necessary to strengthen interdisciplinary collaboration, integrate big data and artificial intelligence technologies, focus on emerging health issues, and promote the transformation of research findings into policy.
