Evidence map›Paper›PMID 41322107›Full record

ArticleThe Lancet regional health. Western Pacific2025

Area-level socioeconomic status inequalities shape patterns of antimicrobial resistance in China, 2014-2023: a Bayesian spatiotemporal modelling analysis.

Yi Yang, Jiani Jiang, Can Chen, Rongrong Qu, Yi Liu, Chen Xu, Xiangkun Zeng, Xiaofan Li, Yi Li, Weili Lin and 6 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in The Lancet regional health. Western Pacific, 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. 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

16 authors.

Yi YangDepartment of Emergency Medicine, Second Affiliated Hospital, Department of Epidemiology and Biostatistics, School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, China.
Jiani JiangDepartment of Emergency Medicine, Second Affiliated Hospital, Department of Epidemiology and Biostatistics, School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, China.
Can ChenDepartment of Emergency Medicine, Second Affiliated Hospital, Department of Epidemiology and Biostatistics, School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, China.
Rongrong QuDepartment of Emergency Medicine, Second Affiliated Hospital, Department of Epidemiology and Biostatistics, School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, China.
Yi LiuDepartment of Emergency Medicine, Second Affiliated Hospital, Department of Epidemiology and Biostatistics, School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, China.
Chen XuDepartment of Laboratory Medicine, School of Medicine, Jiangsu University, Zhenjiang, China.
Xiangkun ZengDepartment of Emergency Medicine, Second Affiliated Hospital, Department of Epidemiology and Biostatistics, School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, China.
Xiaofan LiDepartment of Emergency Medicine, Second Affiliated Hospital, Department of Epidemiology and Biostatistics, School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, China.
Yi LiState Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing, China.
Weili LinCollege of Life and Environmental Sciences, Minzu University of China, Beijing, China.
Lili HuangLaboratory Department, Children's Hospital of Soochow University, Suzhou, China.
Mi ZhouDepartment of Pharmacy, Children's Hospital of Soochow University, Suzhou, China.
Mengsha ChenDepartment of Emergency Medicine, Second Affiliated Hospital, Department of Epidemiology and Biostatistics, School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, China.
Rong ZhangDepartment of Clinical Laboratory, Second Affiliated Hospital of Zhejiang University, School of Medicine, Hangzhou, China.
Shigui YangDepartment of Emergency Medicine, Second Affiliated Hospital, Department of Epidemiology and Biostatistics, School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, China.
Ning DongDepartment of Emergency Medicine, Second Affiliated Hospital, Department of Epidemiology and Biostatistics, School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Antimicrobial resistance (AMR) represents an escalating global health challenge, with socioeconomic status (SES) being a significant influencing factor. This study aimed to quantify area-level SES and assess its association with drug-resistant bacteria in China. Methods: AMR data were collected from China Antimicrobial Resistance Surveillance System (CARSS, 2014-2023). Area-level SES across China was captured by County-level Area Deprivation Index (CADI) and Townsend Deprivation Index (TDI). Spatiotemporal distributions of drug-resistant bacteria were explored by spatial autocorrelation and spatiotemporal scan analyses. Key AMR risk factors were identified by GeoDetector analysis. Six Bayesian models were established through the Bayesian spatiotemporal modelling analysis. The Bayesian Spatiotemporal Interaction Hierarchy Model (BSTIHM), demonstrating superior estimative accuracy, was selected to forecast nationwide AMR patterns. Findings: AMR profiles were obtained for totally 34,442,268 isolates of thirteen types of drug-resistant bacteria. The distributions of these bacteria exhibited manifest spatiotemporal heterogeneity nationwide. Both CADI and TDI consistently revealed a distinct socioeconomic gradient, with low area-level SES in western regions, medium in central regions, and high in eastern regions. Spatiotemporal clusters of drug-resistant bacteria were mainly observed in low SES regions. Lower area-level SES (odds ratio (OR) range: 1.054-1.254) and higher antimicrobial usage intensity (OR range: 1.022-1.174) contributed to higher risk of all thirteen types of drug-resistant bacteria. Meanwhile, total wastewater discharge (OR range: 1.064-1.280), PM Interpretation: Area-level SES is a pivotal driver of AMR risk, which might be caused by antibiotic overuse and environmental pollution. Targeted investments in healthcare and environmental systems in lower area-level SES regions are essential to effectively reduce AMR burden. Funding: National Key R&D Program of China, National Natural Science Foundation of China, Natural Science Foundation of Zhejiang Province, and Natural Science Foundation of Jiangsu Province.

Indexed as

Antimicrobial resistanceArea-level socioeconomic status inequalityBayesian spatiotemporal modelling analysisEstimated risk

Identifiers

PMID41322107
PMCPMC12663029

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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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.