Visual Bibliometric Analysis of Research Hotspots and Trends in Antimicrobial Resistance in China

Summary

This bibliometric analysis maps antimicrobial resistance research trends and emerging hotspots in China from 2016–2025 using CiteSpace. The findings highlight increasing attention to novel therapeutics and technology-enabled approaches, alongside contrasting MRSA and CRKP resistance trends, emphasizing the need for continued surveillance and targeted antimicrobial stewardship.

Abstract

To systematically analyze the research landscape of antimicrobial resistance (AMR) in China from 2016–2025, identify hotspots, and detect emerging trends using bibliometric visualization techniques. We compiled 3,096 publications from the Web of Science Core Collection (including Science Citation Index Expanded, SCI-E) and China National Knowledge Infrastructure (CNKI). Using CiteSpace, we performed co-authorship network analysis, keyword co-occurrence mapping, and cluster analysis employing the Log-Likelihood Ratio (LLR) algorithm. Burst detection via Kleinberg's algorithm identified research frontiers, while modularity Q (>0.3) and silhouette metrics validated network structural quality. Rising from 357 in 2016 to 365 in 2022, with accelerated growth following the 2019–2022 period. Six major research clusters emerged with significant structural validity (modularity Q = 0.8108). Burst analysis identified mobilized colistin resistance-1 (mcr-1, burst strength 9.12) and carbapenemase (8.52) as the strongest emerging signals. Resistance surveillance revealed divergent trends: carbapenem-resistant Klebsiella pneumoniae increased alarmingly from 5.6% to 20.0%, whereas methicillin-resistant Staphylococcus aureus declined from 48.8% to 25.8%. Author network analysis revealed a highly centralized structure with limited cross-regional collaboration. Novel therapeutics, including antimicrobial peptides, phage therapy, and machine learning applications, demonstrated significant emergence. Overall, the bibliometric patterns suggest that AMR research in China has evolved from descriptive surveillance toward mechanistic investigation and increasing attention to innovative interventions. The contrasting decline in methicillin-resistant Staphylococcus aureus and rise in carbapenem-resistant pathogens highlight the need for continued national surveillance, stronger inter-regional collaboration, targeted antimicrobial stewardship, and cautious translation of emerging technologies into clinical practice.

Introduction

Antimicrobial resistance (AMR) poses a critical threat to global public health, with the World Health Organization identifying it as one of the top ten health threats facing humanity¹. The economic burden of AMR is substantial, with projections suggesting that by 2050, resistant infections could cause 10 million deaths annually worldwide if current trends continue². The fundamental mechanisms of antimicrobial resistance in bacteria involve complex genetic and phenotypic adaptations, including enzymatic drug modification, target site alterations, efflux pump overexpression, and biofilm formation, which collectively threaten the efficacy of existing therapeutic armamentarium³. Recent global assessments have further emphasized the expansive scope of this challenge, with antimicrobial resistance now recognized as a complex ecological phenomenon requiring coordinated action across human health, animal agriculture, and environmental sectors⁴. AMR also has broader implications for sustainable development, including environmental and socioeconomic targets⁵. The challenge is particularly acute in combating resistance in malaria, HIV, and tuberculosis, where multidrug-resistant strains threaten decades of progress in infectious disease control⁶. Historical analyses of AMR epidemiology in China have documented distinctive characteristics compared to Western populations, including unique resistance gene profiles and transmission dynamics shaped by healthcare practices and antibiotic consumption patterns⁷.

China, with its large population and substantial antibiotic consumption, faces particularly severe AMR challenges. The per capita antibiotic use in China remains significantly higher than in many European countries, creating selective pressure that drives resistance development⁸. Recognizing the severity of this crisis, the Chinese government has implemented the National Action Plan to Contain Antimicrobial Resistance (2016–2020, 2022–2025) and established the China Antimicrobial Resistance Surveillance System (CARSS) to monitor resistance patterns nationwide⁹. From a regional perspective, Southeast Asia has emerged as a critical hotspot for AMR research and dissemination, with bibliometric analyses revealing rapidly growing research output paralleling China's trajectory¹⁰. Global trends and projections suggest that without decisive intervention, the mortality burden of AMR will disproportionately affect middle-income countries, with some projections indicating that the Asia-Pacific region may account for nearly half of all resistance-related deaths by mid-century¹¹. These sobering forecasts have catalyzed international commitments to antimicrobial stewardship, though implementation gaps persist between policy formulation and clinical practice worldwide¹². These initiatives have generated extensive data on resistance epidemiology, molecular mechanisms, and clinical outcomes, providing a rich source for bibliometric analysis. Recent One Health frameworks, therefore, emphasize coordinated interventions across human health, animal health, agriculture, and environmental sectors, supported by antimicrobial stewardship and rapid diagnostic technologies¹³.

Bibliometric analysis, utilizing mathematical and statistical methods to quantitatively analyze published literature, enables researchers to uncover research hotspots, track evolutionary trajectories, and predict future directions¹⁴. Visualization tools such as CiteSpace transform complex citation data into intuitive knowledge maps, revealing hidden patterns in scientific literature¹⁵. Previous bibliometric studies have mapped research trends in specific AMR domains, including global antifungal resistance research¹⁶. However, comprehensive bibliometric analysis integrating Chinese- and English-language research on AMR in China remains limited. In contrast to previous bibliometric analyses focused on specific AMR domains, the present study integrates Chinese-language CNKI and English-language Web of Science records and separately contextualizes the bibliometric findings using national CARSS surveillance trends. This study aims to provide a systematic bibliometric analysis of AMR research in China from 2016–2025, utilizing CiteSpace software to generate knowledge maps and provide evidence-based recommendations for future research priorities and policy development. This combined design provides a bilingual view of the research landscape while allowing bibliometric trends to be interpreted alongside, but independently from, national resistance-surveillance data.

Protocol

Data sources

A comprehensive literature search was conducted across both international and domestic databases to ensure global coverage of AMR research related to China. For international literature, we systematically searched the Web of Science Core Collection (including Science Citation Index Expanded, SCI-E) and the China National Knowledge Infrastructure (CNKI) from January 1, 2016, to December 31, 2025. Exact Boolean Query for SCI-E: TS = ((“antimicrobial resistance” OR “antibiotic resistance” OR “drug resistance”) AND (“China” OR “Chinese” OR “Mainland China”)). For CNKI, the Subject field was searched using the Chinese-language equivalents of “antibiotic resistance,” “antimicrobial resistance,” and “drug resistance,” combined with “China.” No formal keyword-synonym merging or thesaurus-based preprocessing was performed before the CiteSpace analysis. The final search was executed on April 28, 2026, to allow sufficient time for publications issued through December 31, 2025, to be indexed in the databases. The database publication-year filter was restricted to 2016–2025. Records labeled as Early Access were additionally checked during screening, and articles with a first online publication date in 2026 were excluded from the analysis. Keyword labels were checked against the original CiteSpace output to ensure consistent terminology in the text, tables, and figures.

For the CNKI search, 983 records were retrieved from the Peking University Core Journals (PKU Core), 802 from the Chinese Science Citation Database (CSCD), and 462 from the World Journal Clout Index (WJCI). Because a single article may be indexed in more than one of these categories, these numbers represent category-specific retrieval records and are not mutually exclusive publication counts. The three record sets were therefore merged before eligibility screening. Duplicate entries were identified first by exact DOI matching. For records without a DOI, normalized article titles were compared together with the first-author name and publication year. Records with incomplete or inconsistent metadata were reviewed manually. When the same article appeared in two or more CNKI indexing categories, it was retained only once in the combined CNKI dataset, irrespective of the number of categories in which it was indexed. After within-CNKI deduplication and eligibility screening, 1,096 unique Chinese-language articles were retained. No journal impact-factor threshold was applied, because the purpose of this study was to characterize the overall AMR research landscape rather than to restrict the corpus according to journal prestige. Formal methodological-quality or risk-of-bias assessment of individual publications was not performed because the objective of this study was to characterize the overall bibliometric landscape rather than to evaluate study-level methodological quality.

Across SCI-E and CNKI, 5,286 records were identified initially. The same DOI-, title-, author-, and year-based criteria were subsequently applied to identify potential overlaps between the CNKI and Web of Science datasets. A total of 2,190 records were removed during combined deduplication and eligibility screening, leaving 3,096 publications for analysis. The complete literature identification, screening, and inclusion process is presented in Supplementary Figure 1.

Inclusion criteria were: studies focusing on bacterial AMR in China; articles reporting resistance rates, molecular epidemiology, clinical outcomes, or intervention strategies; and publications between January 1, 2016, and December 31, 2025. Duplicate records, 2026 publications, reviews, or meta-analyses without original datasets, and studies limited to veterinary or environmental AMR without a human-health component were excluded. Reviews and meta-analyses without new primary datasets were excluded because the analysis was designed to characterize original research output. Restricting the corpus to original research improves the comparability of bibliometric indicators, including publication counts, co-authorship patterns, keyword frequencies, and citation-related measures, because evidence-synthesis articles aggregate prior studies and may have substantially different citation and keyword profiles. After removing duplicates across databases and screening titles/abstracts, a total of 3,096 publications were included for bibliometric analysis, comprising 1,096 Chinese-language articles (35.4%) and 2000 English-language publications (64.6%).

Analytical methods

CiteSpace (Drexel University, Philadelphia, PA, USA)¹⁷ was used for bibliometric analysis. The time span was 2016–2025 with a 1-year slice length. Node types were selected separately for author, institution, and keyword analyses. Node selection used the g-index with k = 10; the displayed configuration also used LRF = 2.5, L/N = 10, LBY = 5, and e = 1.0. Pathfinder pruning was applied to the author, keyword, and cluster networks to reduce redundant links while retaining the principal network structure. The 1-year time slice was selected to preserve annual changes within the 10-year study window.

Four main analytical modules were utilized: co-authorship analysis to map collaboration networks among authors and institutions; keyword co-occurrence analysis to identify research themes and their relationships; cluster analysis using the Log-Likelihood Ratio (LLR) algorithm to extract thematic clusters with automatic labeling; and burst detection using Kleinberg’s algorithm to identify emerging trends and research frontiers. Network metrics, including modularity Q and mean silhouette S, were used to evaluate cluster quality. A modularity Q value greater than 0.3 indicates a meaningful community structure, whereas a mean silhouette value greater than 0.7 indicates high within-cluster consistency and reliable cluster assignment¹⁸.

Statistical analysis of resistance trends

Resistance rate trends for major pathogens were derived from the China Antimicrobial Resistance Surveillance System (CARSS) annual reports (2016–2025), which aggregate data from participating sentinel hospitals nationwide. These surveillance data were analyzed independently of the bibliometric corpus and were not extracted from, pooled across, or included among the 3,096 Web of Science and CNKI publications used for the CiteSpace analyses.

For each pathogen–antimicrobial combination, we extracted the annual resistance rate (percentage of non-duplicate isolates resistant to the specified agent), the total number of isolates tested (denominator), and the number of sentinel hospitals contributing data. Susceptibility testing was performed using standardized broth microdilution or disk diffusion methods, with interpretations based on Clinical and Laboratory Standards Institute (CLSI) guidelines (M100 documents, editions 2016–2025).

To assess temporal trends, we performed linear regression analysis with the resistance rate as the dependent variable and year as the independent variable, covering the full 10-year period (2016–2025). The regression slope (β) and coefficient of determination (R²) were calculated for each pathogen–antimicrobial pair. To account for potential changes in surveillance coverage (e.g., expansion from 1,398 hospitals in 2016 to 2,349 hospitals in 2025), we also performed sensitivity analyses weighting each year's resistance rate by the inverse of the number of participating hospitals and by the geographic distribution of reporting sites. These weighted models yielded trend directions and significance levels consistent with the unweighted analyses; therefore, unweighted results are presented for simplicity and interpretability. All statistical analyses were performed using R version 4.3.2 (R Foundation for Statistical Computing, Vienna, Austria). A two-sided p-value <0.05 was considered statistically significant.

Results

Publication trends

A total of 3,096 publications were included in the analysis. Total annual output increased from 357 publications in 2016 to a peak of 365 in 2022 and subsequently declined continuously to 146 in 2025 (Figure 1). English-language output increased from 130 publications in 2016 to 295 in 2022 before decreasing to 69 in 2025. Chinese-language output decreased from 227 publications in 2016 to 93 in 2019, increased during 2020–2021, ...

Discussion

The bibliometric patterns suggest that AMR research in China gradually broadened from descriptive surveillance and pathogen characterization toward resistance mechanisms, genomic approaches, and intervention-oriented research. The overlapping keyword clusters and burst periods indicate cumulative thematic diversification rather than three discrete and mutually exclusive stages. The timing of these changes overlapped with the National Action Plans to Contain Antimicrobial Resistance for 2016–2020 and 2022–2025...

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
CiteSpace	Drexel University, Philadelphia, PA, USA	version 6.4.R1	To perform comprehensive bibliometric analysis, including co-authorship analysis, keyword co-occurrence analysis, cluster analysis, and burst detection.
China National Knowledge Infrastructure (CNKI)	China National Knowledge Infrastructure	Not applicable	To compile research articles provided by Chinese domestic sources.
R	R software	version 4.3.2
Web of Science Core Collection (SCI-E)	Web of Science	Not applicable	To systematically search and retrieve international literature.