Advance Search
Volume 37 Issue 12
Dec.  2021
Turn off MathJax
Article Contents
SHI Wei-wei, XUN Lu-ning, CAO Ya-jing, . Prevalence characteristics of cerebral infarction among residents in Hebei province, 2015 – 2018: a time series analysis[J]. Chinese Journal of Public Health, 2021, 37(12): 1800-1804. doi: 10.11847/zgggws1131022
Citation: SHI Wei-wei, XUN Lu-ning, CAO Ya-jing, . Prevalence characteristics of cerebral infarction among residents in Hebei province, 2015 – 2018: a time series analysis[J]. Chinese Journal of Public Health, 2021, 37(12): 1800-1804. doi: 10.11847/zgggws1131022

Prevalence characteristics of cerebral infarction among residents in Hebei province, 2015 – 2018: a time series analysis

doi: 10.11847/zgggws1131022
  • Received Date: 2020-06-07
    Available Online: 2021-08-11
  • Publish Date: 2021-12-13
  •   Objective   To examine prevalence characteristics of cerebral infarction (CI) among residents in Hebei province from 2015 to 2018, and to explore the application of time series model in the prediction of CI incidence for the prevention and control of the disease.   Methods   From National Chronic Non-Communicable Disease Surveillance System, we collected the data on registered cerebral infarction incidents in 18 surveillance points in Hebei province from January 1, 2015 through December 31, 2018. The prevalence characteristics of CI and its changing trend were analyzed and a time series model was established to predict annual and monthly incidence rate of CI in 2019.  Results  During the 4-year period in the province, the average annual CI incidence rate were 311.73/100 000, with the yearly incidence rate of 283.22, 296.36, 322.93, and 342.55 per 100 000 for the year from 2015 to 2018 and a significantly increasing trend (χ2 = 624.353, P < 0.001). The CI incidence rate in the male residents were significantly higher than that in the female residents (348.81/100 000 vs. 273.60/100 000, χ2 = 1 680.960; P < 0.05). The age-specific CI incidence rate was significantly different among various age groups, with the rate of 2.58, 48.96, 263.21, 650.61, 1 641.77, 2 083.59, and 3 000.88 per 100 000 for the residents aged < 35, 35 – 44, 45 – 54, 55 – 64, 65 – 74, 75 – 84, and ≥ 85 years, respectively (χ2 = 381 051.465, P < 0.001). The established best fitted model was ARIMA (autoregressive integrated moving average) (0, 1, 1)(2, 1, 0)12; the model's residual sequence was white noise sequence (P > 0.05); and the parameters for the established model were as following: Akaike information criterion (AIC) = 214.480, Bayesian information criterion (BIC) = 220.698, root mean square error (RMSE) = 2.97, mean absolute error (MAE) =1.62, and mean absolute percentage error (MAPE) = 7.55%. Based on the model, the predicted yearly CI incidence rate was 362.46/100 000 for the residents in 2019, which was higher than that in previous years; the predicted monthly CI incidence rates ranged between 23.62/100 000 and 33.33/100 000 and the variation of the rates for the year of 2019 was similar to that in previous years.   Conclusion   The incidence rate of cerebral infarction was at a high level and increased yearly during 2015 – 2018 among residents in Hebei province; the incidence rate was relatively higher in the male and the elderly residents. Time series model can be used to predicate the incidence rate for the prevention and control of cerebral infarction.
  • loading
  • [1]
    Mozaffarian D, Benjamin EJ, Go AS, et al. Heart disease and stroke statistics – 2016 update: a report from the American Heart Association[J]. Circulation, 2016, 133: e38 – e360.
    [2]
    王陇德. 中国脑卒中防治报告2017[M]. 北京: 人民卫生出版社, 2017.
    [3]
    Wen LH, Zhang S, Wan KZ, et al. Risk factors of haemorrhagic transformation for acute ischaemic stroke in Chinese patients receiving intravenous thrombolysis: a meta-analysis[J]. Medicine (Baltimore), 2020, 99(7): e18995. doi: 10.1097/MD.0000000000018995
    [4]
    王陇德, 刘建民, 杨弋, 等. 我国脑卒中防治仍面临巨大挑战 —— 《中国脑卒中防治报告2018》概要[J]. 中国循环杂志, 2019, 34(2): 105 – 119.
    [5]
    寻鲁宁, 张帆, 孙纪新, 等. 基于求和自回归滑动平均模型的道路交通伤害死亡趋势预测分析[J]. 中华疾病控制杂志, 2020, 24(4): 467 – 472.
    [6]
    李峰, 陈国清, 徐士林, 等. 三种时间序列模型在盐城市肾综合征出血热发病预测应用中的比较研究[J]. 中国卫生统计, 2020, 37(2): 249 – 252.
    [7]
    杨晓明, 沈冰, 王妍敏, 等. 上海市静安区居民意外跌落死亡率时间序列分析与预测[J]. 中国公共卫生, 2015, 31(11): 1450 – 1452. doi: 10.11847/zgggws2015-31-11-25
    [8]
    World Health Organization. International statistical classification of diseases and related health problems 10th revision[EB/OL]. (2017 – 05 – 17)[2020 – 08 – 17]. https://icd.who.int/browse10/2010/en.
    [9]
    彭荣荣, 刘芸男, 杨小丽, 等. 基于时间序列分析的单采血小板临床需求预测模型研究[J]. 首都医科大学学报, 2020, 41(2): 231 – 236. doi: 10.3969/j.issn.1006-7795.2020.02.014
    [10]
    吴玉攀, 韦柳意, 王双, 等. 武冈市农村地区心脑血管住院病例的时间序列预测分析[J]. 中华疾病控制杂志, 2019, 23(2): 222 – 226.
    [11]
    谢渊, 刘淑清, 董国英, 等. 2004 — 2018年我国狂犬病疫情时间序列分析[J]. 中国人兽共患病学报, 2019, 35(11): 1041 – 1046.
    [12]
    朱佳佳, 胡登利, 洪秀琴, 等. 基于时空大数据的甲型肝炎发病率分布特征分析及预测模型[J]. 中华疾病控制杂志, 2018, 22(11): 1144 – 1147.
    [13]
    Institute for Health Metrics and Evaluation. Global Health Data Exchange. GBD results tool[DB/OL]. [2020 – 08 – 17]. http://ghdx.healthdata.Org/gbd-results-tool.
    [14]
    丁贤彬, 焦艳, 毛德强, 等. 2012 — 2018年重庆市脑卒中发病和死亡趋势分析[J]. 中国慢性病预防与控制, 2020, 28(6): 428 – 431.
    [15]
    沈卓之, 丁贤彬, 毛德强, 等. 2015年重庆市常住人口脑卒中发病与死亡情况[J]. 公共卫生与预防医学, 2016, 27(5): 48 – 51.
    [16]
    薛晓丹, 王德征, 张颖, 等. 2010 — 2016年天津市居民脑梗死发病特征及趋势分析[J]. 疾病监测, 2019, 34(4): 354 – 358. doi: 10.3784/j.issn.1003-9961.2019.04.016
    [17]
    孙文慧, 阎秀芳, 王松强, 等. 2014 — 2016年郑州市居民心脑血管疾病监测资料分析[J]. 现代预防医学, 2019, 46(4): 708 – 710, 722.
    [18]
    《中国脑卒中防治报告》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.
    [19]
    徐颖, 陈远银, 于绍轶, 等. 2012 — 2015年烟台市脑卒中流行病学特征分析[J]. 现代预防医学, 2017, 44(2): 200 – 204.
    [20]
    顾锋, 罗颖芝. 2012 — 2015年宁波市北仑区脑卒中发病状况流行特征分析[J]. 中国慢性病预防与控制, 2017, 25(7): 527 – 529.
    [21]
    冯光坤, 牛建花, 朱海英, 等. 中青年与老年脑梗死患者的病因及危险因素研究[J]. 中国全科医学, 2012, 15(17): 1940 – 1942.
    [22]
    张静, 刘志东, 劳家辉, 等. 基于时间序列分解法预测肾综合征出血热发病趋势和季节性[J]. 中国公共卫生, 2018, 34(7): 1038 – 1041.
    [23]
    林亚楠, 郭岩, 杨西, 等. 大连市气象因素与急性脑梗死发病的相关性分析[J]. 中国脑血管病杂志, 2018, 15(3): 113 – 118. doi: 10.3969/j.issn.1672-5921.2018.03.001
    [24]
    河北省气象局. 河北省气候公报2016年[EB/OL]. (2017 – 01 – 13)[2020 – 08 – 26]. http://he.cma.gov.cn/qxfw/qhfx/qhgb/201904/t20190403_272044.html.
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(4)  / Tables(2)

    Article views (197) PDF downloads(24) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return