| 林静静,龚巍巍,陆凤,周晓燕,方乐,徐春晓,潘劲,陈向宇,戴品远,钟节鸣.浙江省2015-2020年基于空间面板模型的卒中死亡空间自相关及相关因素研究[J].Chinese journal of Epidemiology,2023,44(10):1616-1621 |
| 浙江省2015-2020年基于空间面板模型的卒中死亡空间自相关及相关因素研究 |
| Spatial autocorrelation and related factors of stroke mortality in Zhejiang Province based on spatial panel model in 2015-2020 |
| Received:March 16, 2023 |
| DOI：10.3760/cma.j.cn112338-20230316-00154 |
| KeyWord: 卒中 空间自相关 空间面板模型 |
| English Key Word: Stroke Spatial autocorrelation Spatial panel model |
| FundProject:浙江省软科学研究计划（2022C35013）；浙江省医药卫生科技计划（2020RC049）；浙江省公益技术应用研究项目（LGF22H260018） |
| Author Name | Affiliation | E-mail | | Lin Jingjing | Department of Chronic and Non-communicable Disease Prevention and Control, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou 310051, China | | | Gong Weiwei | Department of Chronic and Non-communicable Disease Prevention and Control, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou 310051, China | | | Lu Feng | Department of Chronic and Non-communicable Disease Prevention and Control, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou 310051, China | | | Zhou Xiaoyan | Department of Chronic and Non-communicable Disease Prevention and Control, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou 310051, China | | | Fang Le | Department of Chronic and Non-communicable Disease Prevention and Control, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou 310051, China | | | Xu Chunxiao | Department of Chronic and Non-communicable Disease Prevention and Control, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou 310051, China | | | Pan Jin | Department of Chronic and Non-communicable Disease Prevention and Control, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou 310051, China | | | Chen Xiangyu | Department of Chronic and Non-communicable Disease Prevention and Control, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou 310051, China | | | Dai Pinyuan | Department of Chronic and Non-communicable Disease Prevention and Control, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou 310051, China | | | Zhong Jieming | Department of Chronic and Non-communicable Disease Prevention and Control, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou 310051, China | jmzhong@cdc.zj.cn |
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| Abstract: |
| 目的 探讨2015-2020年浙江省卒中死亡的空间自相关分布及其宏观影响因素，为制定卒中防控策略提供科学依据。方法 从浙江省慢性病监测信息管理系统获取卒中死亡数据，通过绘制地图、空间自相关分析探索卒中死亡的空间分布特征，采用空间面板模型分析卒中死亡与社会经济、卫生保健因素的相关性。结果 2015-2020年浙江省卒中平均标化死亡率为68.38/10万，在空间分布上呈东部地区高于西部，南部地区高于北部的趋势，具有空间正相关性（Moran's I值为0.274~0.390，P<0.001）。卒中标化死亡率与人均国内生产总值（GDP）（β=-0.370，P<0.001）、人均医疗卫生财政支出（β=-0.116，P=0.021）、每千人口床位数（β=-0.161，P=0.030）呈负相关。缺血性卒中标化死亡率与人均GDP（β=-0.310，P=0.002）和高血压规范管理率（β=-0.462，P=0.011）呈负相关，出血性卒中标化死亡率与人均GDP（β=-0.481，P<0.001）、人均医疗卫生财政支出（β=-0.184，P=0.001）、每千人口床位数（β=-0.288，P=0.001）和高血压规范管理率（β=-0.336，P=0.029）呈负相关。结论 2015-2020年浙江省卒中死亡水平存在空间正相关性，对经济相对落后的地区开展重点防治、加大政府医疗卫生资金投入、优化医疗资源配置、提升高血压患者规范管理率是降低卒中死亡率的重要举措。 |
| English Abstract: |
| Objective To explore the spatial autocorrelation and macro influencing factors of stroke mortality in Zhejiang Province in 2015-2020 and provide a scientific basis for stroke prevention and control strategy. Methods The data on stroke death were obtained from Zhejiang Chronic Disease Surveillance System. The spatial distribution of stroke mortality was explored by mapping and spatial autocorrelation analysis. The spatial panel model analyzed the correlation between stroke mortality and socioeconomic and healthcare factors. Results From 2015 to 2020, the average stroke mortality was 68.38/100 thousand. The standard mortality of stroke was high in the areas of east and low in the west, high in the south and low in the north. Moreover, positive spatial autocorrelation was observed (Moran's I=0.274-0.390, P<0.001). Standard mortality of stroke was negatively associated with per capita gross domestic product (GDP) (β=-0.370, P<0.001), per capita health expenditure (β=-0.116, P=0.021), number of beds per thousand population (β=-0.161, P=0.030). Standard mortality of ischemic stroke was negatively associated with per capita GDP (β=-0.310, P=0.002) and standard management rate of hypertension (β=-0.462, P=0.011). Standard mortality of hemorrhagic stroke was negatively associated with per capita GDP (β=-0.481, P<0.001), per capita health expenditure (β=-0.184, P=0.001), number of beds per thousand population (β=-0.288, P=0.001) and standard management rate of hypertension (β=-0.336, P=0.029). Conclusions A positive spatial correlation existed between stroke mortality in Zhejiang Province in 2015-2020. We must focus more on preventing and controlling strokes in relatively backward economic areas. Moreover, to reduce the mortality of stroke, increasing the investment of government medical and health funds, optimizing the allocation of medical resources, and improving the standard management rate of hypertension are important measures. |
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