您的位置：山东大学 -> 科技期刊社 -> 《山东大学学报（医学版）》

山东大学学报 (医学版) ›› 2022, Vol. 60 ›› Issue (2): 81-88.doi: 10.6040/j.issn.1671-7554.0.2021.0758

• 公共卫生与管理学 • 上一篇    下一篇

基于镇街尺度的淄博市2019年脑卒中时空分布

房启迪1*,杨淑霞2*,齐畅1,程传龙1,韩闯1,刘盈1,崔峰2,李秀君1   

  1. 1. 山东大学齐鲁医学院公共卫生学院生物统计学系, 山东 济南 250012;2. 淄博市疾病预防控制中心, 山东 淄博 255026
  • 发布日期:2022-01-25
  • 通讯作者: 李秀君. E-mail:xjli@sdu.edu.cn崔峰. E-mail:cuifeng@126.com*共同第一作者
  • 基金资助:
    国家自然科学基金(81673238);国家重点研发计划(2019YFC1200500,2019YFC1200502)

Spatio-temporal distribution of stroke in Zibo City in 2019 based on township scale

FANG Qidi1*, YANG Shuxia2*, QI Chang1, CHENG Chuanlong1, HAN Chuang1, LIU Ying1,CUI Feng2, LI Xiujun1   

  1. 1. Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan 250012, Shandong, China;
    2. Zibo Center for Disease Control and Prevention, Zibo 255026, Shandong, China
  • Published:2022-01-25

摘要： 目的 探索2019年淄博市各镇(街道)脑卒中报告病例的时空分布特征,为脑卒中防控策略提供参考。 方法 收集淄博市2019年脑卒中报告病例数据,应用空间自相关分析、圆分布法和扫描统计量探索脑卒中的时空分布特征。 结果 淄博市2019年脑卒中报告病例共18 469例,粗发病率为407.65/10万,其中,男性粗发病率(464.90/10万)高于女性(350.03/10万);随着年龄增长,脑卒中粗发病率呈增长趋势;发病类型以缺血性脑卒中(84.06%)为主;空间上,病例主要分布在沂源县、张店区和淄川区;时间上,病例主要分布在1月至5月初和8月末至11月。 结论 脑卒中在空间和时间上的分布不同,各疾病预防控制部门可因地制宜,有针对性地加强对脑卒中的预防和干预,以期有效地降低脑卒中的发病率。

关键词: 脑卒中, 空间自相关, 圆分布, 扫描统计量

Abstract: Objective To explore the spatio-temporal distribution characteristics of stroke in various towns(subdistricts)in Zibo City in 2019, so as to provide references for the preventive and control strategies. Methods The data of stroke cases in Zibo City in 2019 were collected. The spatial autocorrelation analysis, circular distribution method and scan statistics were used to explore the spatio-temporal distribution characteristics of stroke. Results The total number of stroke cases was 18,469, with a crude incidence rate of 407.65/100,000. The crude incidence rate of men(464.90/100,000)was higher than that of women(350.03/100,000), and showed an increasing trend with the increase of age. The main type of disease was ischemic stroke(84.06%). In terms of space, cases were mainly distributed in Yiyuan County, Zhangdian District and Zichuan District. In terms of time, cases mainly occurred from January to early May and from the end of August to November. Conclusion The distribution of stroke cases is different in space and time. Various disease prevention and control departments can strengthen the prevention and intervention of stroke according to local conditions, so as to effectively reduce the incidence of stroke.

Key words: Stroke, Spatial autocorrelation, Circular distribution method, Scan statistics

中图分类号: 

  • R18
[1] Institute for Health Metrics and Evaluation. GBD Compare[DB/OL].(2020-10-15)[2021-06-08]. https://vizhub.healthdata.org/gbd-compare.
[2] 《中国脑卒中防治报告2019》编写组.《中国脑卒中防治报告2019》概要[J]. 中国脑血管病杂志, 2020, 17(5): 272-281. Report on stroke prevention and treatment in China Writing Group. Brief report on stroke prevention and treatment in China, 2019[J]. Chinese Journal of Cerebrovascular Diseases, 2020, 17(5): 272-281.
[3] 曹新西, 徐晨婕, 侯亚冰, 等. 1990~2025年我国高发慢性病的流行趋势及预测[J]. 中国慢性病预防与控制, 2020, 28(1): 14-19. CAO Xinxi, XU Chenjie, HOU Yabing, et al. The epidemic trend and prediction of chronic diseases with high incidence in China from 1990 to 2025 [J]. Chinese Journal of Prevention and Control of Chronic Diseases, 2020, 28(1): 14-19.
[4] Li Z, Jiang Y, Li H, et al. Chinas response to the rising stroke burden[J]. BMJ, 2019, 364: l879.doi: 10.1136/bmj.l879.
[5] 佘凯丽, 张丹丹, 齐畅, 等. 安徽省新型冠状病毒肺炎流行病学特征及其潜伏期估计[J]. 山东大学学报(医学版), 2020, 58(10): 44-52. SHE Kaili, ZHANG Dandan, QI Chang, et al. Epidemiological characteristics and incubation period of coronavirus disease 2019 in Anhui Province[J]. Journal of Shandong University(Health Sciences), 2020, 58(10): 44-52.
[6] 张永树, 杨振凯, 訾璐, 等. 中国艾滋病空间格局和时空演化分析[J]. 地球信息科学学报, 2020, 22(2): 198-206. ZHANG Yongshu, YANG Zhenkai, ZI Lu, et al. Spatio-temporal Evolution of the AIDS Pattern in China [J]. Journal of Geo-Information Science, 2020, 22(2): 198-206.
[7] 贾艳, 李春雨, 刘利利, 等. 浙江省新型冠状病毒肺炎的流行特征与空间分析[J]. 山东大学学报(医学版), 2020, 58(10): 66-73. JIA Yan, LI Chunyu, LIU Lili, et al. Epidemic characteristics and spatial analysis of COVID-19 in Zhejiang Province [J]. Journal of Shandong University(Health Sciences), 2020, 58(10): 66-73.
[8] 王珮竹, 郑兆磊, 许勤勤, 等. 山东省麻疹消除目标年前后时空分布特征比较分析[J]. 中国病原生物学杂志, 2018, 13(4): 359-363. WANG Peizhu, ZHENG Zhaolei, XU Qinqin, et al. Comparison of the spatiotemporal characteristics of measles in Shandong Province before and after the target year for measles elimination [J]. Journal of Pathogen Biology, 2018, 13(4): 359-363.
[9] Moran PAP. The interpretation of statistical maps [J]. J R Stat Soc Series B Stat Methodol, 1948, 10(2): 243-251.
[10] Anselin L. Local indicators of spatial association-LISA [J]. Geogr Anal, 1995, 27(2): 93-115.
[11] 黄仁发, 吴磊, 朱清仙, 等. 脑卒中的发病季节和时间规律分析[J]. 中国老年学杂志, 2012, 32(6): 1117-1118. HUANG Renfa, WU Lei, ZHU Qingxian, et al. Analysis on the season and time rule of stroke incidence [J]. Chinese Journal of Gerontology, 2012, 32(6): 1117-1118.
[12] 洪冰, 周富友, 金素萍. 杭州市脑卒中急诊就诊时间和季节的分布特征[J]. 中国卫生统计, 2003(5): 48-49.
[13] 金晓胜, 叶侃, 张豪, 等. 2786例院前脑卒中患者发病时间及分析[J]. 医院管理论坛, 2017, 34(7): 38-40,72. JIN Xiaosheng, YE Kan, ZHANG Hao, et al. Analysis on Symptom Onset Time of 2786 Patients with Prehospital Stroke [J]. Hospital Management Forum, 2017, 34(7): 38-40,72.
[14] 潘东霞, 陈玲琍, 谢开婿, 等. 应用圆形分布法探讨脑卒中的发病季节和时间规律[J].疾病监测, 2016, 31(1): 58-62. PAN Dongxia, CHEN Lingli, XIE Kaixu, et al. Season specific incidence pattern of stroke indicated with circular distribution method [J]. Disease Surveillance, 2016, 31(1): 58-62.
[15] 金丕焕. 医用统计方法[M]. 2版. 上海:复旦大学出版社, 2003.
[16] 刘廷轩, 齐畅, 佘凯丽, 等. 河北省新型冠状病毒肺炎流行特征与时空聚集性分析[J]. 山东大学学报(医学版), 2020, 58(10): 74-81. LIU Tingxuan, QI Chang, SHE Kaili, et al. Analysis on the epidemiolohical characteristics and spatial-temporal clustering of COVID-19 in Hebei province[J]. Journal of Shandong University(Health Sciences), 2020, 58(10): 74-81.
[17] 刘利利, 贾艳, 齐畅, 等. 基于时空统计方法分析温州市2020年1~3月新型冠状病毒肺炎的聚集性分布[J]. 山东大学学报(医学版), 2020, 58(10): 82-88. LIU Lili, JIA Yan, QI Chang, et al. The clustering distribution of COVID-19 in Wenzhou from January to March 2020 based on spatiotemporal analysis [J]. Journal of Shandong University(Health Sciences), 2020, 58(10): 82-88.
[18] 唐咸艳, 李峤, 仇小强, 等. 扫描统计量中最大空间扫描窗口的尺度选择[J]. 中华疾病控制杂志, 2015, 19(3): 316-317, 320. TANG Xianyan, LI Qiao, QIU Xiaoqiang, et al. An exploratory study on maximum spatial cluster size of scan statistics [J]. Chinese Journal Disease Control & Prevention, 2015, 19(3): 316-317, 320.
[19] Tango T, Takahashi K. A flexibly shaped spatial scan statistic for detecting clusters [J]. Int Health Geogr, 2005, 4: 11. doi: 10.1186/1476-072X-4-11.
[20] Kulldorff M. SaTScan User Guide v9.7 [EB/OL].(2021-01)[2021-06-08] https://www.satscan.org/techdoc.html.
[21] 周盛年, 孙晓晗, 周雪颖, 等. 山东省脑卒中流行病学及其危险因素分析[J]. 中华神经科杂志, 2019, 52(9): 716-723. ZHOU Shengnian, SUN Xiaohan, ZHOU Xueying, et al. Epidemiology of stroke and its risk factors in Shandong province, China [J]. Chinese Journal of Neurology, 2019, 52(9): 716-723.
[22] Suadicani P, Andersen LL, Holtermann A, et al. Perceived psychological pressure at work, social class, and risk of stroke: a 30-year follow-up in Copenhagen male study [J]. J Occup Environ Med, 2011, 53(12): 1388-1395.
[23] Shah ASV, Lee KK, McAllister DA, et al. Short term exposure to air pollution and stroke: systematic review and meta-analysis[J]. BMJ, 2015, 350: h1295. doi: 10.1136/bmj.h1295.
[24] Huang K, Liang F, Yang X, et al. Long term exposure to ambient fine particulate matter and incidence of stroke: prospective cohort study from the China-PAR project [J]. BMJ, 2019, 367: l6720. doi: 10.1136/bmj.l6720.
[25] Béjot Y, Reis J, Giroud M, et al. A review of epidemiological research on stroke and dementia and exposure to air pollution [J]. Int J Stroke, 2018, 13(7): 687-695.
[26] Tian Y, Liu H, Si Y, et al. Association between temperature variability and daily hospital admissions for cause-specific cardiovascular disease in urban China: A national time-series study [J]. PLoS Med, 2019, 16(1): e1002738. doi: 10.1371/journal.pmed.1002738.
[27] Chen R, Yin P, Wang L, et al. Association between ambient temperature and mortality risk and burden: time series study in 272 main Chinese cities[J]. BMJ, 2018, 363: k4306. doi: 10.1136/bmj.k4306.
[1] 逄锦宏,苏萍,乔俊鹏,陈巧巧,陈学禹,赵颖颖,施婕,孙晓茹,李秋春,何蕊言,范轶欧,迟蔚蔚. 老年人群可改变心血管危险因素聚集模式与脑卒中的关联[J]. 山东大学学报 (医学版), 2025, 63(9): 11-19.
[2] 申路佳,逯天威,巩伟明,赵岩松,王淑康,袁中尚. 代谢风险评分在2型糖尿病人群心血管结局预测中的应用[J]. 山东大学学报 (医学版), 2025, 63(8): 69-78.
[3] 陈莹莹,王鲁,胡锡峰,朱高培,薛付忠. 基于贝叶斯网络的2型糖尿病患者并发脑卒中风险预测[J]. 山东大学学报 (医学版), 2025, 63(8): 94-102.
[4] 宋思豪,程传龙,李树芬,席睿,梁珂梦,倪志松,崔峰,李秀君. 大气污染对淄博市缺血性脑卒中患者寿命损失年的短期影响及极端温度事件修饰效应[J]. 山东大学学报 (医学版), 2025, 63(2): 84-94.
[5] 王立童,王战,吴静仪,吴艳盈,李永娜,唐洪. 耳穴电针联合高频重复经颅磁刺激对卒中后抑郁的疗效[J]. 山东大学学报 (医学版), 2025, 63(11): 53-60.
[6] 刘淋,王晓楠,杨雅溪,王江腾,李旭,周新丽,管庆波,张栩. 甘油三酯-葡萄糖指数与颅内动脉粥样硬化性狭窄的相关性[J]. 山东大学学报 (医学版), 2024, 62(8): 93-100.
[7] 梁珂梦,李树芬,倪志松,宋思豪,席睿,程传龙,左慧,段雨琪,刘昆,白尧,李秀君. 基于MGWR模型的西安手足口病发病影响因素[J]. 山东大学学报 (医学版), 2024, 62(6): 96-101.
[8] 李晨淑,王瑞华,陆信武. 不同腔内技术治疗非A非B型夹层的神经系统并发症研究进展[J]. 山东大学学报 (医学版), 2024, 62(11): 1-7.
[9] 张伯韬,仉率杰,孙爽爽,袁莹,胡锡峰,贾晓峰,于媛媛,薛付忠. 基于贝叶斯网络的缺血性脑卒中筛查模型构建[J]. 山东大学学报 (医学版), 2024, 62(11): 73-84.
[10] 李希,王秉翔,李娜,曹丽娜,李爱华,冠潇,张志勉. 下肢外骨骼机器人康复训练对脑卒中偏瘫患者下肢运动的影响[J]. 山东大学学报 (医学版), 2023, 61(3): 121-126.
[11] 程传龙,杨淑霞,佘凯丽,房启迪,韩闯,刘盈,崔峰,李秀君. 淄博市2018年恶性肿瘤的流行特征及影响因素[J]. 山东大学学报 (医学版), 2022, 60(2): 102-108.
[12] 赵璇,李晓鹏,李剑,田彬,王广君. 磁珠耳穴贴压联合重复经颅磁刺激对脑卒中后抑郁的疗效[J]. 山东大学学报 (医学版), 2022, 60(1): 65-70.
[13] 贾艳,李春雨,刘利利,佘凯丽,刘廷轩,朱雨辰,齐畅,张丹丹,王旭,陈恩富,李秀君. 浙江省新型冠状病毒肺炎的流行特征与空间分析[J]. 山东大学学报 (医学版), 2020, 58(10): 66-73.
[14] 陈希,刘新宇,范立霞,田永昊,原所茂. 老年脊柱疾病应用颈动脉超声评估缺血性脑卒中风险的临床价值[J]. 山东大学学报 (医学版), 2019, 57(5): 48-55.
[15] 郝小蕊,赵昌盛. 肠内和肠外营养支持对早期重症脑卒中患者血清ALT、SCr浓度变化及并发症的影响[J]. 山东大学学报 (医学版), 2019, 57(3): 80-84.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
版权所有 © 2018 《山东大学学报(医学版)》编辑部　
地址:山东大学科技期刊社（济南市历城区山大南路27号山东大学中心校区明德楼B座721室） 电话: 0531-88366918 E-mail: xbyxb@sdu.edu.cn
本系统由北京玛格泰克科技发展有限公司设计开发