武冈市农村地区心脑血管住院病例的时间序列预测分析
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摘要
目的建立武冈市农村地区心脑血管疾病(cardio-cerebrovascular disease,CVD)住院病例的预测模型,并对CVD住院病例的变化趋势进行预测分析,为医院合理配置CVD科室医疗资源提供参考依据。方法利用Stata 14. 0软件对武冈市2013年1月～2016年12月农村地区CVD住院人次月度数据构建季节性自回归移动平均混合模型(seasonal autoregressive integrated moving average model,SARIMA),并对2017年武冈市农村地区CVD住院病例进行预测分析。结果通过模型构建最终拟合的CVD住院病例预测模型为SARIMA(2,1,1) x(0,1,0)_(12)。Ljung-Box Q检验结果显示残差序列为白噪音序列(Q=11. 12,P=0. 680),说明所建模型拟合度较好,且2017年的预测结果与观测结果基本一致,总体相对误差在-1. 2%左右。预测结果显示,夏季为每年CVD住院高峰期。结论 SARIMA模型可以对武冈市CVD住院病例进行较准确的短期预测,医院可以根据不同月份CVD就医需求合理配置院内CVD科室医疗资源。
Objective To establish a predictive model for inpatients of cardio-cerebrovascular disease in rural areas of Wugang through time series analysis,and predict the changing trend of cardio-cerebrovascular disease,so as to offer guidance for the health care resources allocation and prevention and control of cardio-cerebrovascular disease. Methods The seasonal autoregressive integrated moving average model( SARIMA) was constructed based on the monthly number of cases of cardio-cerebrovascular disease in rural areas from January 2013 to December 2016 by Stata 14. 0 software,and the predictive effect of the model was verified with the monthly number of inpatients of cardio-cerebrovascular disease in2017. Results The final fitting model of inpatients of cardio-cerebrovascular disease was SARIMA( 2,1,1) ×( 0,1,0)12. The residual sequence of the model was diagnosed. Results of Ljung-Box Q test showed that the residual sequence was white noise sequence( Q = 11. 12,P = 0. 68). In addition,the2017 forecast was basically consistent with the observations,the overall relative error was around-1. 2%.The results showed that the summer was the peak period of cardiovascular and cerebrovascular hospitalization. Conclusion SARIMA model can accurately predict the number of inpatients of cardio-cerebrovascular disease in Wugang,which can provide data support for the hospital administrator to rationally allocate medical resources in the cardiovascular according to the needs of cardio-cerebrovascular treatment in different months.
Objective To establish a predictive model for inpatients of cardio-cerebrovascular disease in rural areas of Wugang through time series analysis,and predict the changing trend of cardio-cerebrovascular disease,so as to offer guidance for the health care resources allocation and prevention and control of cardio-cerebrovascular disease. Methods The seasonal autoregressive integrated moving average model( SARIMA) was constructed based on the monthly number of cases of cardio-cerebrovascular disease in rural areas from January 2013 to December 2016 by Stata 14. 0 software,and the predictive effect of the model was verified with the monthly number of inpatients of cardio-cerebrovascular disease in2017. Results The final fitting model of inpatients of cardio-cerebrovascular disease was SARIMA( 2,1,1) ×( 0,1,0)12. The residual sequence of the model was diagnosed. Results of Ljung-Box Q test showed that the residual sequence was white noise sequence( Q = 11. 12,P = 0. 68). In addition,the2017 forecast was basically consistent with the observations,the overall relative error was around-1. 2%.The results showed that the summer was the peak period of cardiovascular and cerebrovascular hospitalization. Conclusion SARIMA model can accurately predict the number of inpatients of cardio-cerebrovascular disease in Wugang,which can provide data support for the hospital administrator to rationally allocate medical resources in the cardiovascular according to the needs of cardio-cerebrovascular treatment in different months.
引文
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[11]常倩,叶云杰,汪庆庆,等.南京市大气污染物与居民心脑血管疾病死亡的相关性[J].环境与职业医学,2017,34(12):1041-1045.DOI:10.13213/j.cnki.jeom.2017.17433.Chang Q,Ye YJ,Wang QQ,et al.Correlation between air pollutants and cardio-cerebrovascular mortality in Nanjing[J].Journal of Environmental&Occupational Medine,2017,34(12):1041-1045.DOI:10.13213/j.cnki.jeom.2017.17433.
[12]许安阳,张丽娟,李觉,等.上海市温度和大气污染对居民心血管疾病门急诊人数的影响[J].同济大学学报(医学版),2017,38(01):114-118,123.DOI:10.16118/j.1008-0392.2017.01.024.Xu AY,Zhang LJ,Li j,et al.Effects of temperature and air pollution on outpatient and emergency visits for cardiovascular diseases in Shanghai[J].Journal of Tongji University(Medical Science),2017,38(01):114-118,123.DOI:10.16118/j.1008-0392.2017.01.024.
[13]Yang J,Zhou M,Ou CQ,et al.Seasonal variations of temperature-related mortality burden from cardiovascular disease and myocardial infarction in China[J].Environ Pollut,2017,5(224):400-406.DOI:10.1016/j.envpol.2017.02.020.
[14]顾恺,何红.高血压患者夏季和冬季动态血压监测值的差异[J].中华高血压杂志,2016,24(6):578-580.DOI:10.16439/j.cnki.1673-7245.2016.06.019.Gu K,He H.Differences between summer and winter ambulatory blood pressure monitoring values in patients with hypertension[J].Chinese Journal of Hypertension,2016,24(6):578-580.DOI:10.16439/j.cnki.1673-7245.2016.06.019.
[15]吕阳,王志盟,陈滨.室内空气环境与高龄者心脑血管疾病关联性研究进展[J].建筑科学,2018,34(2):124-130.DOI:10.13614/j.cnki.11-1962/tu.2018.02.21.Lv Y,Wang ZM,Chen B.A review of the Relationship between Indoor Air Environment and Cardiovascular and Cerebrovascular Disease in the Elderly[J].Building Science,2018,34(2):124-130.DOI:10.13614/j.cnki.11-1962/tu.2018.02.21.
[16]廉恒丽,俞剑琴,傅映晖.ARIMA与SARIMA模型在医院门诊人次与出院人次预测中的应用[J].中国医院统计,2017,24(02):81-84.DOI:10.3969/j.issn.1006-5253.2017.02.001.Liang HL,Yu JQ,Fu YH.Application of ARIMA and SARIMAmodel in predicting hospital outpatients and discharges[J].Chinese Journal of Hospital Statistics,2017,24(02):81-84.DOI:10.3969/j.issn.1006-5253.2017.02.001.
[2]World Health Organization.World health statistics 2018[EB/OL].(2018-3-28)[2018-12-15]http://www.who.int/iris/bistream/handle/10665/272596/9789241565585eng.pdf?ua=1.
[3]陈伟伟,高润霖,刘力生,等.《中国心血管病报告2017》概要[J].中国循环杂志,2018,33(1):1-8.DOI:10.3969/j.issn.1000-3614.2018.01.001.Chen WW,Gao RL,Liu LS,et al.Summary of China Cardiovascular Disease Report 2017[J].Chin Circul J,2018,33(01):1-8.DOI:10.3969/j.issn.1000-3614.2018.01.001.
[4]李小升,马春柳,雷海科,等.SARIMA模型在医院门诊量预测中的应用[J].中国病案,2013,14(3):37-40.DOI:10.3969/j.issn.1672-2566.2013.03.019.Li XS,Ma CL,Lei HK,et al.Applications of SARIMA Model on Predicting Outpatients Quantity[J].Chinese Medical Record,2013,14(3):37-40.DOI:10.3969/j.issn.1672-2566.2013.03.019.
[5]马翠荣,杨婕,余小金.江苏省2006-2014年城乡未成年人跌倒病例的时间序列预测分析[J].中华疾病控制杂志,2018,22(2):122-125,137.DOI:10.16462/j.cnki.zhjbkz.2018.02.005.Ma CR,Yang J,Yu XJ,et al.The fall injury cases of urban and rural areas for minors in Jiangsu Province:a time-series prediction and analysis,2006-2014[J].Chin J Dis Control Prev,2018,22(2):122-125,137.DOI:10.16462/j.cnki.zhjbkz.2018.02.005.
[6]易静,杜昌廷,王润华,等.自回归求和移动平均季节乘积模型在结核病发病率预测中的应用[J].中华预防医学杂志,2007,41(2):118-121.DOI:10.3760/j.issn:0253-9624.2007.02.009.Yi J,Du CT,Wang RH,et al.Applications of multiple seasonal autoregressive integrated moving average(ARIMA)model on predictive incidence of tuberculosis[J].Chin J Prev Med,2007,41(02):118-121.DOI:10.3760/j.issn:0253-9624.2007.02.009.
[7]王芳,柴宗举,刘雯,等.出血性脑卒中发病趋势及时间序列分析[J].中国老年学杂志,2013,33(17):4128-4130.DOI:10.3969/j.issn.1005-9202.2013.17.012.Wang F,Chai ZJ,Liu W,et al.Incidence trend and time series analysis of hemorrhagic stroke[J].Chin J Ger,2013,33(17):4128-4130.DOI:10.3969/j.issn.1005-9202.2013.17.012.
[8]谷少华,陆蓓蓓,边国林,等.大气可吸入颗粒物对心血管疾病急救人次的短期影响[J].环境与职业医学,2016,33(10):965-969.DOI:10.13213/j.cnki.jeom.2016.16140.Gu SH,Lu BB,Bian GL,et al.Short-Term Effect of Inhalable Particulate Matters on Emergency Ambulance Dispatches for Cardiovascular Diseases[J].Journal of Environmental&Occupational Medine,2016,33(10):965-969.DOI:10.13213/j.cnki.jeom.2016.16140.
[9]张霞,刘起勇.高温热浪对心脑血管病影响研究进展[J].中国公共卫生,2014,30(2):242-243.DOI:10.11847/zgggws2014-30-02-38.Zhang X,Liu QY.Research on the effects of high temperature heat waves on cardiovascular and cerebrovascular diseases[J].Chinese Journal of Public Health,2014,30(2):242-243.DOI:10.11847/zgggws2014-30-02-38.
[10]刘方,张金良,陆晨.北京市气温与脑卒中发病关系的时间序列研究[J].中华流行病学杂志,2004(11):48-52.DOI:10.3760/j.issn:0254-6450.2004.11.011.Liu F,Zhang JL,Lu C.The relationship of temperature and stroke incidence in Beijing:a time-series study[J].Chin J Epidemiol,2004(11):48-52.DOI:10.3760/j.issn:0254-6450.2004.11.011.
[11]常倩,叶云杰,汪庆庆,等.南京市大气污染物与居民心脑血管疾病死亡的相关性[J].环境与职业医学,2017,34(12):1041-1045.DOI:10.13213/j.cnki.jeom.2017.17433.Chang Q,Ye YJ,Wang QQ,et al.Correlation between air pollutants and cardio-cerebrovascular mortality in Nanjing[J].Journal of Environmental&Occupational Medine,2017,34(12):1041-1045.DOI:10.13213/j.cnki.jeom.2017.17433.
[12]许安阳,张丽娟,李觉,等.上海市温度和大气污染对居民心血管疾病门急诊人数的影响[J].同济大学学报(医学版),2017,38(01):114-118,123.DOI:10.16118/j.1008-0392.2017.01.024.Xu AY,Zhang LJ,Li j,et al.Effects of temperature and air pollution on outpatient and emergency visits for cardiovascular diseases in Shanghai[J].Journal of Tongji University(Medical Science),2017,38(01):114-118,123.DOI:10.16118/j.1008-0392.2017.01.024.
[13]Yang J,Zhou M,Ou CQ,et al.Seasonal variations of temperature-related mortality burden from cardiovascular disease and myocardial infarction in China[J].Environ Pollut,2017,5(224):400-406.DOI:10.1016/j.envpol.2017.02.020.
[14]顾恺,何红.高血压患者夏季和冬季动态血压监测值的差异[J].中华高血压杂志,2016,24(6):578-580.DOI:10.16439/j.cnki.1673-7245.2016.06.019.Gu K,He H.Differences between summer and winter ambulatory blood pressure monitoring values in patients with hypertension[J].Chinese Journal of Hypertension,2016,24(6):578-580.DOI:10.16439/j.cnki.1673-7245.2016.06.019.
[15]吕阳,王志盟,陈滨.室内空气环境与高龄者心脑血管疾病关联性研究进展[J].建筑科学,2018,34(2):124-130.DOI:10.13614/j.cnki.11-1962/tu.2018.02.21.Lv Y,Wang ZM,Chen B.A review of the Relationship between Indoor Air Environment and Cardiovascular and Cerebrovascular Disease in the Elderly[J].Building Science,2018,34(2):124-130.DOI:10.13614/j.cnki.11-1962/tu.2018.02.21.
[16]廉恒丽,俞剑琴,傅映晖.ARIMA与SARIMA模型在医院门诊人次与出院人次预测中的应用[J].中国医院统计,2017,24(02):81-84.DOI:10.3969/j.issn.1006-5253.2017.02.001.Liang HL,Yu JQ,Fu YH.Application of ARIMA and SARIMAmodel in predicting hospital outpatients and discharges[J].Chinese Journal of Hospital Statistics,2017,24(02):81-84.DOI:10.3969/j.issn.1006-5253.2017.02.001.