Prediction of maternal and child health care indicators in China based on GM(1, 1) model
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摘要:
目的 研究GM(1,1)模型在我国妇幼保健指标中的预测效果,并对未来妇幼保健指标进行短期预测,为我国妇幼保健服务水平的逐步完善提供科学依据。 方法 收集我国2008-2017年的孕产妇死亡率(maternal mortality rate,MMR)、新生儿死亡率(neonatal mortality rate,NMR)、婴儿死亡率(infant mortality rate,IMR)和5岁以下儿童死亡率(under-five mortality rate,U5MR),建立模型,应用MATLAB 2018b软件进行预测分析。 结果 我国MMR、NMR、IMR和U5MR的预测模型分别为:${\rm{\hat x}}\left( {{\rm{k}} + 1} \right) = - 476.08{{\rm{e}}^{ - 0.09{\rm{k}}}} + 510.28({C_1} = 0.165,{P_1} = 1.000)$,${\rm{\hat x}}\left( {{\rm{k}} + 1} \right) = - 108.43{{\rm{e}}^{{\rm{ - 0}}{\rm{.09k}}}} + 118.63({C_2} = 0.043,{P_2} = 1.000)$,${\rm{\hat x}}\left( {{\rm{k}} + 1} \right) = - 160.60{{\rm{e}}^{ - 0.09{\rm{k}}}} + 175.50({C_3} = 0.085,{P_3} = 1.000)$,${\rm{\hat x}}\left( {{\rm{k}} + 1} \right) = - 224.37{{\rm{e}}^{ - 0.08{\rm{k}}}} + 242.87({C_4} = 0.124,{P_4} = {\rm{ }}1.000)$,平均相对误差分别为:3.46%、0.67%、1.75%和2.36%。 结论 GM(1,1)模型适用于对我国妇幼保健指标的预测,拟合精度均较高;预测未来三年各指标将继续逐年下降,相关部门应有针对性的加强管理工作。 -
关键词:
- GM (1, 1)模型 /
- 妇幼保健 /
- 预测分析
Abstract:Objective To study the predictive effect of model[GM(1, 1)] in China's maternal and child health indicators, and to predict the future maternal and child health indicators in a short-term, and provide a scientific basis for the gradual improvement of maternal and child health care services in China. Methods The maternal mortality rate (MMR), neonatal mortality rate (NMR), infant mortality rate (IMR) and under-five mortality rate (U5MR) were collected from 2008 to 2017 in China. Models were established and MATLAB 2018b software was used for predictive analysis. Results The prediction models of maternal mortality rate, neonatal mortality rate, infant mortality rate and under-five mortality rate were as follows: ${\rm{\hat x}}\left( {{\rm{k}} + 1} \right) = - 476.08{{\rm{e}}^{ - 0.09{\rm{k}}}} + 510.28({C_1} = 0.165,{P_1} = 1.000)$, ${\rm{\hat x}}\left( {{\rm{k}} + 1} \right) = - 108.43{{\rm{e}}^{{\rm{ - 0}}{\rm{.09k}}}} + 118.63({C_2} = 0.043,{P_2} = 1.000)$, ${\rm{\hat x}}\left( {{\rm{k}} + 1} \right) = - 160.60{{\rm{e}}^{ - 0.09{\rm{k}}}} + 175.50({C_3} = 0.085,{P_3} = 1.000)$, ${\rm{\hat x}}\left( {{\rm{k}} + 1} \right) = - 224.37{{\rm{e}}^{ - 0.08{\rm{k}}}} + 242.87({C_4} = 0.124,{P_4} = {\rm{ }}1.000)$, the average relative errors were as follows: 3.46%, 0.67%, 1.75% and 2.36%。 Conclusions The GM (1, 1) is suitable for the prediction of maternal and child health indicators in China, and the fitting accuracy is high. It is predicted that the indicators will continue to decline year by year in the next three years, and relevant departments should strengthen the management work in a targeted manner. -
Key words:
- GM(1, 1) model /
- Maternal and child health care /
- Predictive analysis
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P值 C值 拟合精度等级 >0.95 <0.35 好 >0.80 <0.50 合格 >0.70 <0.65 勉强合格 ≤0.70 ≥0.65 不合格 表 2 我国各年份妇幼保健指标的GM(1, 1)模型建立及检验[8]
Table 2. Establishment and testing of GM(1, 1) model for maternal and child health indicators in various years in China[8]
时间 MMR(1/10万) NMR(‰) IMR(‰) U5MR(‰) 实际值 预测值 相对误差(%) 实际值 预测值 相对误差(%) 实际值 预测值 相对误差(%) 实际值 预测值 相对误差(%) 2008 34.20 - 0.00 10.20 - 0.00 14.90 - 0.00 18.50 - 0.00 2009 31.90 31.08 2.57 9.00 9.06 0.67 13.80 14.03 1.67 17.20 17.46 1.51 2010 30.00 29.05 3.17 8.30 8.30 0.00 13.10 12.80 2.29 16.40 16.10 1.83 2011 26.10 27.15 4.02 7.80 7.61 2.44 12.10 11.68 3.47 15.60 14.85 4.81 2012 24.50 25.38 3.59 6.90 6.97 1.01 10.30 10.66 3.50 13.20 13.69 3.71 2013 23.20 23.73 2.28 6.30 6.39 0.00 9.50 9.73 2.42 12.00 12.63 5.25 2014 21.70 22.18 2.21 5.90 5.86 0.68 8.90 8.88 0.22 11.70 11.64 0.51 2015 20.10 20.73 3.13 5.40 5.37 0.56 8.10 8.11 0.12 10.70 10.74 0.37 2016 19.90 19.38 2.61 4.90 4.92 0.41 7.50 7.40 1.33 10.20 9.90 2.94 2017 19.60 18.12 7.55 4.50 4.51 0.22 6.80 6.75 0.74 9.10 9.13 0.33 C值 0.165 0.043 0.085 0.124 P值 1.000 1.000 1.000 1.000 平均误差(%) 3.46 0.67 1.75 2.36 注:符合率=预测值/实际值*100%,相对误差=1-符合率。 表 3 2018-2020年妇幼保健指标的预测情况
Table 3. Forecast of maternal and child health indicators from 2018 to 2020
时间 MMR预测值(1/10万) NMR预测值(‰) IMR预测值(‰) U5MR预测值(‰) 2018 16.93 4.13 6.16 8.42 2019 15.83 3.79 5.62 7.77 2020 14.80 3.47 5.13 7.16 -
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