Evidence map›Paper›PMID 41255597›Full record

ArticleChina CDC weekly2025

County-Level Hotspot Identification and Spatial Regression Analysis of Health Loss from Kashin-Beck Disease - China, 2019 and 2023.

Ying Liu, Fang Qi, Haoyu Du, Haonan Li, Shicong Zheng, Qian Yu, Hexuan Dong, Chenxi Wang, Jiaxin Li, Yue Zhao and 2 more

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. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

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0 citing papers in PubMed.

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4 · The record

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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

12 authors.

Ying LiuInstitute for Kashin-Beck Disease Control and Prevention, Center for Endemic Disease Control, Chinese Center for Disease Control and Prevention, Harbin Medical University, Harbin City, Heilongjiang Province, China.
Fang QiInstitute for Kashin-Beck Disease Control and Prevention, Center for Endemic Disease Control, Chinese Center for Disease Control and Prevention, Harbin Medical University, Harbin City, Heilongjiang Province, China.
Haoyu DuInstitute for Kashin-Beck Disease Control and Prevention, Center for Endemic Disease Control, Chinese Center for Disease Control and Prevention, Harbin Medical University, Harbin City, Heilongjiang Province, China.
Haonan LiInstitute for Kashin-Beck Disease Control and Prevention, Center for Endemic Disease Control, Chinese Center for Disease Control and Prevention, Harbin Medical University, Harbin City, Heilongjiang Province, China.
Shicong ZhengInstitute for Kashin-Beck Disease Control and Prevention, Center for Endemic Disease Control, Chinese Center for Disease Control and Prevention, Harbin Medical University, Harbin City, Heilongjiang Province, China.
Qian YuInstitute for Kashin-Beck Disease Control and Prevention, Center for Endemic Disease Control, Chinese Center for Disease Control and Prevention, Harbin Medical University, Harbin City, Heilongjiang Province, China.
Hexuan DongInstitute for Kashin-Beck Disease Control and Prevention, Center for Endemic Disease Control, Chinese Center for Disease Control and Prevention, Harbin Medical University, Harbin City, Heilongjiang Province, China.
Chenxi WangInstitute for Kashin-Beck Disease Control and Prevention, Center for Endemic Disease Control, Chinese Center for Disease Control and Prevention, Harbin Medical University, Harbin City, Heilongjiang Province, China.
Jiaxin LiInstitute for Kashin-Beck Disease Control and Prevention, Center for Endemic Disease Control, Chinese Center for Disease Control and Prevention, Harbin Medical University, Harbin City, Heilongjiang Province, China.
Yue ZhaoInstitute for Kashin-Beck Disease Control and Prevention, Center for Endemic Disease Control, Chinese Center for Disease Control and Prevention, Harbin Medical University, Harbin City, Heilongjiang Province, China.
Jiayuan LiInstitute for Kashin-Beck Disease Control and Prevention, Center for Endemic Disease Control, Chinese Center for Disease Control and Prevention, Harbin Medical University, Harbin City, Heilongjiang Province, China.
Jun YuInstitute for Kashin-Beck Disease Control and Prevention, Center for Endemic Disease Control, Chinese Center for Disease Control and Prevention, Harbin Medical University, Harbin City, Heilongjiang Province, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: We analyzed the spatial distribution of years lived with disability (YLDs) among patients with Kashin-Beck disease (KBD) at the county level across the country, identified hotspot regions and the primary areas of disease burden. This provides a foundation for the prevention and control of KBD and the rational allocation of healthcare resources to regions with high disease burden. Methods: The data were obtained from the National KBD Surveillance System. Spatial autocorrelation analysis was conducted to assess spatial clustering and to identify hotspots of YLDs in patients with KBD. Geographically weighted regression (GWR) models were used to identify counties with limited economic and healthcare resources and a high burden of health losses. Results: Spatial aggregation of YLDs among patients with KBD was observed nationwide, with hotspots concentrated in diseased counties in western China, including Shaanxi, Gansu, and Sichuan, and in the northern regions of Heilongjiang and Inner Mongolia. Among the variables, the number of health technicians was negatively correlated with the YLD rate of patients with KBD across 2 years ( Conclusions: YLDs among patients with KBD at the county level in China demonstrated spatial clustering, with hotspots primarily in the western regions. Strengthening the recruitment and training of health professionals in high-burden, underserved areas may help improve the quality of life of patients.

Indexed as

geographically weighted regression modelKashin–Beck diseasespatial autocorrelation analysisyears lived with disability

Identifiers

PMID41255597
PMCPMC12620681

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