Evidence map›Paper›PMID 41607897›Full record

ArticleFrontiers in public health2025

Three-year nationwide survey of microbiology laboratory equipment conditions in China's CDC: setting new benchmarks.

Peihua Niu, Zhenlu Sun, Yu Zhang, Dapeng Zhang, Ruiqing Zhang, Yiming Zhao, Fengyu Tian, Jieqiong Zhang, Hongmei Zheng, Jianqiang Guo and 3 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

13 authors.

Peihua Niu *National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, NHC Key Laboratory of Medical Virology and Viral Diseases, National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
Zhenlu Sun *Yantai Center for Disease Control and Prevention, Yantai, China.
Yu ZhangNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, NHC Key Laboratory of Medical Virology and Viral Diseases, National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
Dapeng ZhangNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, NHC Key Laboratory of Medical Virology and Viral Diseases, National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
Ruiqing ZhangNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, NHC Key Laboratory of Medical Virology and Viral Diseases, National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
Yiming ZhaoNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, NHC Key Laboratory of Medical Virology and Viral Diseases, National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
Fengyu TianNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, NHC Key Laboratory of Medical Virology and Viral Diseases, National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
Jieqiong ZhangNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, NHC Key Laboratory of Medical Virology and Viral Diseases, National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
Hongmei ZhengNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, NHC Key Laboratory of Medical Virology and Viral Diseases, National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
Jianqiang GuoNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, NHC Key Laboratory of Medical Virology and Viral Diseases, National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
Ping ChengNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, NHC Key Laboratory of Medical Virology and Viral Diseases, National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
Xuejun MaNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, NHC Key Laboratory of Medical Virology and Viral Diseases, National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
Ji WangNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, NHC Key Laboratory of Medical Virology and Viral Diseases, National Institute for Viral 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

Background: This study evaluates the penetration and evolution of laboratory equipment in China's Centers for Disease Control and Prevention (CDCs) from 2021 to 2023, establishing benchmarks for optimal equipment configuration and operational capacity enhancement. Methods: Data were collected from CDCs at provincial, municipal, and county levels over three years using stratified sampling and structured questionnaires. These captured information on equipment penetration and user satisfaction. Statistical analyses, including Chi-square tests, ANOVA, and Fisher's exact tests, identified trends and disparities in equipment distribution across administrative tiers. Results: Analysis of 540 questionnaires from 376 CDCs showed that essential equipment like Biological Safety Cabinets (BSCs) and qPCR machines achieved over 95% penetration across all levels. Municipal CDCs reported the largest increases in equipment acquisition, followed by provincial CDCs, with minimal changes at the county level. The penetration of domestic qPCR machines rose from 21.8% in 2021% to 28.3% in 2023, with domestic brands closing the gap with international counterparts. Post-COVID-19, strategies for optimizing equipment utilization appear promising. Conclusion: This study provides a valuable dataset on microbiology laboratory equipment in China's CDCs, revealing distinct patterns in equipment retention and acquisition. Disparities in qPCR machine and High-throughput Sequencer (HTS) penetration and satisfaction rates highlight a shift toward domestic manufacturing. The findings offer essential benchmarks for optimizing laboratory infrastructure and enhancing public health responses.

Indexed as

BenchmarkingLaboratoriesChinaCOVID-19HumansSurveys and QuestionnairesCDCChinalaboratory equipmentmicrobiology laboratoriespenetration ratepublic health

Identifiers

PMID41607897
PMCPMC12835382

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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.