Evidence map›Paper›PMID 40900168›Full record

ArticleInternational orthopaedics2025

Prevalence of radiographic knee osteoarthritis in China: a national survey of thirty thousand, four hundred and fifty five individuals cross-sectional study.

Hongzhi Lv, Zhiyong Hou, Yanbin Zhu, Ran Sun, Jianzhao Wang, Guangzhao Hou, Bing Yin, Song Liu, Guang Yang, Xiao Chen and 7 more

Abstract read
In one paragraph

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

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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

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2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

17 authors.

Hongzhi Lv *The Third Hospital of Hebei Medical University, Shijiazhuang, China.
Zhiyong Hou *The Third Hospital of Hebei Medical University, Shijiazhuang, China.
Yanbin Zhu *The Third Hospital of Hebei Medical University, Shijiazhuang, China.
Ran Sun *The Third Hospital of Hebei Medical University, Shijiazhuang, China.
Jianzhao Wang *The Third Hospital of Hebei Medical University, Shijiazhuang, China.
Guangzhao Hou *The Third Hospital of Hebei Medical University, Shijiazhuang, China.
Bing YinThe Third Hospital of Hebei Medical University, Shijiazhuang, China.
Song LiuThe Third Hospital of Hebei Medical University, Shijiazhuang, China.
Guang YangThe Third Hospital of Hebei Medical University, Shijiazhuang, China.
Xiao ChenThe Third Hospital of Hebei Medical University, Shijiazhuang, China.
Bo LiuThe Third Hospital of Hebei Medical University, Shijiazhuang, China.
Peizhi YuwenThe Third Hospital of Hebei Medical University, Shijiazhuang, China.
Fei ZhangThe Third Hospital of Hebei Medical University, Shijiazhuang, China.
Shilun LiThe Third Hospital of Hebei Medical University, Shijiazhuang, China.
Juan Wang *The Third Hospital of Hebei Medical University, Shijiazhuang, China. 84133719@qq.com.
Wei Chen *The Third Hospital of Hebei Medical University, Shijiazhuang, China. 3085034028@qq.com.
Yingze Zhang *The Third Hospital of Hebei Medical University, Shijiazhuang, China. dryzzhang@126.com.

Funding

2025 government-funded clinical medicine talent cultivation project ZF2025136Beijing-tianjin-hebei Basic Research Cooperation project Grant No. J230007
6 · The paper itself

Abstract

backgroundKnee osteoarthritis (KOA) is a major health burden for adults and the elderly globally; however, national radiological and epidemiological data and predictive models for KOA are lacking in China. Most of the existing studies are limited to regional samples, which cannot accurately reflect disease burden and risk factors.

objectivesThis study aimed to systematically analyze the prevalence and risk factors of adult radiology KOA in China for the first time using a national representative sample and to develop a prediction model to provide a basis for public health strategies.

methodsA multi-stage stratified random sampling method was employed to select 30 cities and 10 rural counties from 10 provinces, consisting of 30,455 participants aged 50 years and older who had lived in the area for at least five years. Knee X-rays were assessed using the Kellgren-Lawrence grading system, and demographic, clinical, and geographic data were collected. Samples were randomly divided into modeling and validation groups. A predictive model was developed using multiple logistic regression, and its performance was validated using receiver operating characteristic curves, calibration plots, and decision curve analysis. Further, an interactive web calculator based on R Shiny was developed.

resultsThis study enrolled 31,206 individuals. Questionnaires from 751 (2.5%) individuals were ultimately excluded due to missing items, insufficient responses, or logical errors. After exclusions, 30,455 (97.5%) individuals participated in the Chinese National KOA Study, consisting of 11,605 (38%) and 18,850 (62%) from urban and rural areas and 13,444 (44%) and 17,011 (56%) men and women, respectively. A total of 9,145 participants were diagnosed with radiographic KOA, and 3,515 participants, including 969 men and 2,546 women, had symptomatic knees. The population-weighted prevalence of radiographic KOA in China was 27.9 (95% confidence interval: 24.8-31.1) per 1000 people. A predictive model for KOA was developed, and its validity was verified among male and female patients. Significant risk factors for men included age, education, body mass index (BMI), central obesity, and residence in the hills; whereas, for women, age, education, BMI, previous knee impairment, more than two childbirths, and hypertension were risk factors. Two interactive web calculators based on R shiny were developed to access the probability of KOA. The website address for male patients was https://kneeosteoarthritisnomogram.shinyapps.io/DynNomapp/ , and for female patients was https://femalekneeosteoarthritisnomogram.shinyapps.io/DynNomapp/ .

conclusionsOur results provide detailed information on knee joint incidence, distribution, and risk factors, which is considered the latest clinical evidence basis for national healthcare planning and prevention efforts in China and other regions. To facilitate KOA prevention, public health policies focusing on risk factors for KOA, such as maintaining a healthy weight, implementing health management, and reducing underlying diseases, should be implemented. Further, men should avoid living in mountainous areas and women should have fewer childbirths and not have knee impairments.

Indexed as

Knee JointOsteoarthritis, KneeAgedChinaCross-Sectional StudiesFemaleHealth SurveysHumansMaleMiddle AgedPrevalenceRadiographyRisk FactorsKnee osteoarthritisNational prevalencePrediction modelRisk factors

Identifiers

PMID40900168
PMCPMC12488765

What OpenQuestion holds

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LicenceCC BY
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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.