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

基于县级数据的克山病热点识别及空间回归分析——中国2019年和2023年

阅读:1

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 (P<0.05). Significant geographical differences were found in the spatial distribution of YLDs, with key disease burden areas in 85 northern counties, including Heilongjiang, Jilin, and Inner Mongolia, and 145 western counties, including Shaanxi, Shanxi, and other provincial-level administrative divisions. 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.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。