Evidence map›Paper›PMID 41312527›Full record

ArticleChina CDC weekly2025

Characteristics and Influencing Factors of Antimicrobial Resistance in

Yao Peng, Ming Luo, Ziyu Liu, Changyu Zhou, Hongqun Zhao, Zhenpeng Li, Biao Kan, Ning Jiang, Xin Lu

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in China CDC weekly, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Retail wet markets, food handlers, and hospitalised patients as nodes in a shared non-typhoidal Salmonella ecosystem: a one health molecular epidemiological pilot study in Wuhan, China.European journal of clinical microbiology & infectious diseases : official publication of the European Society of Clinical Microbiology · 2026
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Yao Peng *School of Public Health, Nanjing Medical University, Nanjing City, Jiangsu Province, China.
Ming Luo *Yulin Center for Disease Control and Prevention, Yulin City, Guangxi Zhuang Autonomous Region, China.
Ziyu LiuNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
Changyu ZhouYulin Center for Disease Control and Prevention, Yulin City, Guangxi Zhuang Autonomous Region, China.
Hongqun ZhaoNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
Zhenpeng LiNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
Biao KanSchool of Public Health, Nanjing Medical University, Nanjing City, Jiangsu Province, China.
Ning JiangYulin Center for Disease Control and Prevention, Yulin City, Guangxi Zhuang Autonomous Region, China.
Xin LuDepartment of Microbiomics, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Asymptomatic carriers of antibiotic-resistant Methods: Antimicrobial susceptibility testing was performed against 11 antimicrobial agents. We analyzed temporal trends in AMR rates using the Mann-Kendall test and assessed associations between AMR rates and natural or socioeconomic variables using Spearman's rank correlation, Principal Component Regression (PCR), and Least Absolute Shrinkage and Selection Operator (LASSO) regression. An Autoregressive Integrated Moving Average (ARIMA) model was employed to forecast future resistance trends. Results: Resistance to tetracycline (TET) was most prevalent (mean rate: 66.2%). The overall multidrug resistance (MDR) rate was 41.9%, exhibiting a significant increasing trend ( Conclusion: Our findings demonstrate a significant rise in MDR

Indexed as

antimicrobial resistanceasymptomatic workersinfluencing factors

Identifiers

PMID41312527
PMCPMC12647941

What OpenQuestion holds

Textmetadata
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.