Current Trends in Chronic Non-Communicable Disease Management: A Bibliometric Analysis of the Past Two Decades
Bibliographic record
Abstract
Shiyong Xiao,1,* Yongqi Dong,2,* Yuan Xia,3 Hongyan Xu,4 Falin Weng,5 Guohong Liang,6 Qianzhang Yi,7 Chengming Ai8 1Department of Clinical Nutrition, Wushan County People’s Hospital of Chongqing, Chongqing, 404700, People’s Republic of China; 2Department of Gastroenterology, Wushan County People’s Hospital of Chongqing, Chongqing, People’s Republic of China; 3Department of General Practice, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, People’s Republic of China; 4Department of Infectious Diseases, the Second Affiliated Hospital of Chongqing Medical University, Chongqing, People’s Republic of China; 5Department of Geriatric Medicine, Wushan County People’s Hospital of Chongqing, Chongqing, People’s Republic of China; 6Department of Oncology, Wushan County People’s Hospital of Chongqing, Chongqing, People’s Republic of China; 7Department of Radiology, Wushan County People’s Hospital of Chongqing, Chongqing, People’s Republic of China; 8Department of Physical Examination Center, Wushan County People’s Hospital of Chongqing, Chongqing, People’s Republic of China*These authors contributed equally to this workCorrespondence: Chengming Ai, Department of Physical Examination Center, Wushan County People’s Hospital of Chongqing, Wushan County, Chongqing, People’s Republic of China, Email 790298077@qq.comBackground: In recent years, there has been a growing focus on chronic non-communicable diseases (NCD) and their impact on personal and social health. Effective management of NCD is essential for their prevention and treatment. This study aims to utilize bibliometric methods to analyze and summarize the current development and emerging trends in NCD management.Methods: A literature search and screening were conducted on the Web of Science Core Collection database from January 1, 2004, to December 31, 2023. VOSviewer and Citespace software was performed to examine publication volume, authors, institutions, countries, journals, citation frequencies, keywords, clustering, and burst terms, and to create a visual map.Results: A total of 996 valid publications from 464 journals were included in the study. The number of publications exhibited a gradual growth trend over the years. The United States was the most productive and influential country, contributing the highest proportion of both publications and total citations. BMC Health Services Research, Toronto University, and Marshall, Bruce C. were identified as the most productive journal, institution, and author, respectively. Further analysis of keyword co-occurrence and burst detection revealed that the most prevalent keywords were “improving primary care” and “integrated care”.Conclusion: This bibliometric analysis provides a comprehensive overview of the current status and trends in NCD management over the past two decades, providing valuable insights for future research directions. It indicates a potential shift towards enhancing primary healthy care, integrated care, and digital health.Keywords: non-communicable diseases, bibliometric analysis, management, primary health care, integrated care, digital health
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.032 | 0.080 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".