Article(id=1240395007379698477, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1240395006914130733, articleNumber=null, orderNo=null, doi=10.20043/j.cnki.MPM.202504008, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1743436800000, receivedDateStr=2025-04-01, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1773662815183, onlineDateStr=2026-03-16, pubDate=1753372800000, pubDateStr=2025-07-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1773662815183, onlineIssueDateStr=2026-03-16, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1773662815183, creator=13701087609, updateTime=1773662815183, updator=13701087609, issue=Issue{id=1240395006914130733, tenantId=1146029695717560320, journalId=1227665162245664772, year='2025', volume='52', issue='14', pageStart='2497', pageEnd='2688', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=0, createTime=1773662815073, creator=13701087609, updateTime=1773662858015, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1240395187109810535, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1240395006914130733, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1240395187109810536, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1240395006914130733, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=2497, endPage=2501, ext={EN=ArticleExt(id=1240395009002894129, articleId=1240395007379698477, tenantId=1146029695717560320, journalId=1227665162245664772, language=EN, title=Projections of maternal, infant and neonatal mortality rates, Jiangxi Province, 2021-2030, columnId=1228016567443718970, journalTitle=Modern Preventive Medicine, columnName=Epidemiology and Statistical Methods Advances, runingTitle=null, highlight=null, articleAbstract=
Objective

To analyze the trends of maternal, infant and neonatal mortality rates in Jiangxi Province from 2003 to 2020 in the aggregate and in urban and rural areas, and to predict the maternal mortality rate (MMR), infant mortality rate (IMR), and neonatal mortality rate (NMR) in the aggregate and in urban and rural areas in 2030, in order to provide a basis for the development and planning of the maternal and child health care industry in Jiangxi Province during the period of Healthy China 2030.

Methods

Based on the combined and urban-rural MMR, IMR, and NMR data of Jiangxi Province from 2003 to 2020, the Gray GM (1, 1), support vector machine (SVM), neural network auto-regression (NNAR), quadratic exponential smoothing (ES) models were established, and the optimal model was selected to predict the combined and urban-rural MMR, IMR, and NMR for the years 2021-2030. The already predicted aggregate mortality rates were then used to adjust the urban and rural MMR, IMR, and NMR.

Results

In Jiangxi Province, the combined MMR, IMR, and NMR in 2025 and 2030 were 3.09/100 000, 2.52‰, 1.38‰, 1.67/100 000, 1.51‰, and 0.79‰. The urban MMR, IMR, and NMR in 2025 and 2030 were 2.44/100 000, 2.28‰, 1.28‰, and 1.22/100 000, 1.44‰, 0.75‰.And rural MMR, IMR, and NMR in 2025 and 2030 were 3.79/100 000, 2.81‰, 1.52‰,1.90/100 000, 1.63‰, and 0.89‰.

Conclusion

It is expected that Jiangxi Province will reach the targets set out in the “Healthy China 2030” Plan and other policies ahead of schedule in 2025 and 2030 in terms of MMR, IMR and NMR. Although the gap between urban and rural areas in terms of maternal, infant and neonatal mortality rates continues to narrow, the relevant indicators in rural areas are still higher than those in urban areas, an objective gap that indicates that the continued promotion of maternal and child health-care services in rural areas and the optimization of resource allocation at the grass-roots level are still the key directions of public health work.

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目的

分析江西省2003—2020年合计及城乡孕产妇、婴儿及新生儿死亡率的变化趋势,并预测2030年合计及城乡孕产妇死亡率(MMR)、婴儿死亡率(IMR)、新生儿死亡率(NMR),为健康中国2030期间江西省妇幼健康事业的发展和规划提供依据。

方法

基于江西省2003—2020年合计及城乡孕产妇死亡率(MMR)、婴儿死亡率(IMR)、新生儿死亡率(NMR)数据建立灰色GM(1,1)、支持向量机(SVM)、神经网络自回归(NNAR)、二次指数平滑(ES)模型,挑选出最优模型,对2021—2030年的合计及城乡MMR、IMR、NMR进行预测。再利用已经预测出的合计死亡率对城乡MMR、IMR、NMR进行调整。

结果

江西省2025及2030年合计MMR、IMR、NMR分别为3.09/10万、2.52‰、1.38‰、1.67/10万、1.51‰、0.79‰;2025及2030年城市MMR、IMR、NMR分别为2.44/10万、2.28‰、1.28‰、1.22/10万、1.44‰、0.75‰;2025及2030年农村MMR、IMR、NMR分别为3.79/10万、2.81‰、1.52‰、1.90/10万、1.63‰、0.89‰。

结论

预计江西省2025及2030年合计以及分城乡MMR、IMR、NMR均能提前达到《“健康中国2030”规划纲要》等政策提出的既定目标,孕产妇、婴儿与新生儿死亡率的城乡差距虽呈持续收窄态势,但农村地区相关指标仍高于城市地区,这一客观差距表明,持续推进农村妇幼卫生保健服务,优化基层资源配置仍是公共卫生工作的重点方向。

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胡松波,E-mail:
, copyrightStatement=本刊刊出的所有文章不代表中华预防医学会和本刊编委会的观点,除非特别声明。, copyrightOwner=中华预防医学会和四川大学华西公共卫生学院, extLink=null, articleAbsUrl=null, sourceXml=Vvzyq5itTsdU+e32b0irhw==, magXml=PaSzfPVLkZj1/LfHqp+tpQ==, pdfUrl=null, pdf=Sl9kMj74wu1de2a8dT56Jg==, pdfFileSize=910268, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=1Jpzon5OYR9dQD35k1UnKA==, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=f9JWnQT9Yw3NxCmJ+I7NTQ==, mapNumber=null, authorCompany=null, fund=null, authors=

周思雨(1999—),女,硕士在读,研究方向:疾病负担与生物统计

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Trends, levels and lifetime risks of maternal mortality in the world and inChina[J]. Chinese Journal of Public Health, 2019, 35(1): 53-57. (In Chinese), articleTitle=Trends, levels and lifetime risks of maternal mortality in the world and inChina, refAbstract=null), Reference(id=1240413546803164023, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240395007379698477, doi=null, pmid=null, pmcid=null, year=2010, volume=19, issue=Suppl, pageStart=69, pageEnd=94, url=null, language=null, rfNumber=[16], rfOrder=26, authorNames=Feng XL, Shi G, Wang Y, journalName=Health Economics, refType=null, unstructuredReference=Feng XL, Shi G, Wang Y, et al. An impact evaluation of the Safe Motherhood Program in China[J]. 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Analysis of thetrends and influencing factors of maternal mortality from 2005 to 2014[J]. The Chinese Health Service Management, 2018, 35(9): 710-712. (In Chinese), articleTitle=Analysis of thetrends and influencing factors of maternal mortality from 2005 to 2014, refAbstract=null), Reference(id=1240413548652852110, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240395007379698477, doi=null, pmid=null, pmcid=null, year=null, volume=null, issue=null, pageStart=null, pageEnd=null, url=null, language=null, rfNumber=[19], rfOrder=30, authorNames=中华人民共和国中央人民政府, journalName=null, refType=null, unstructuredReference=中华人民共和国中央人民政府.中共中央 国务院印发《“健康中国2030”规划纲要》[EB/OL].[2025-06-09].https://www.gov.cn/gongbao/content/2016/content_5133024.htm., articleTitle=中共中央 国务院印发《“健康中国2030”规划纲要》, refAbstract=null), Reference(id=1240413548808041361, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240395007379698477, doi=null, pmid=null, pmcid=null, year=null, volume=null, issue=null, pageStart=null, pageEnd=null, url=null, language=null, rfNumber=[19], rfOrder=31, authorNames=Central People’s Government Of the People’s Republic of China, journalName=null, refType=null, unstructuredReference=Central People’s Government Of the People’s Republic of China. The CPC Central Committee and The State Council issued the Outline of Healthy China 2030[EB/OL]. [2025-06-09]. https://www.gov.cn/gongbao/content/2016/content_5133024.htm. 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MMR, IMR, NMR Actuals, Rural-Urban Rate Ratio, and EAPC

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MMRIMRNMR
合计城乡率比合计城乡率比合计城乡率比
2005年38.460.4919.570.3113.930.32
2010年13.880.5811.430.396.470.42
2015年9.891.177.030.614.600.50
2020年5.940.644.200.912.700.86
EAPC(%)-10.99a0.08-9.65a7.27a-10.46a6.61a
t-17.480.05-54.3313.10-29.0510.21
P0.000.960.000.000.000.00
), ArticleFig(id=1240413540314575407, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240395007379698477, language=CN, label=表1, caption=

MMR、IMR、NMR实际值、城乡率比及EAPC

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MMRIMRNMR
合计城乡率比合计城乡率比合计城乡率比
2005年38.460.4919.570.3113.930.32
2010年13.880.5811.430.396.470.42
2015年9.891.177.030.614.600.50
2020年5.940.644.200.912.700.86
EAPC(%)-10.99a0.08-9.65a7.27a-10.46a6.61a
t-17.480.05-54.3313.10-29.0510.21
P0.000.960.000.000.000.00
), ArticleFig(id=1240413540436210227, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240395007379698477, language=EN, label=Table 2, caption=

Evaluation of MMR, IMR, and NMR model accuracy fitting

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模型MAPE(%)RMSE
MMRES9.550.82
IMRSVM3.060.21
NMRSVM6.760.21
), ArticleFig(id=1240413540553650748, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240395007379698477, language=CN, label=表2, caption=

MMR、IMR、NMR模型精度拟合评价

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模型MAPE(%)RMSE
MMRES9.550.82
IMRSVM3.060.21
NMRSVM6.760.21
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Projected MMR, Rural-Urban Rate Ratio and EPAC for Jiangxi Province in 2025 and 2030

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年份合计预测值
(1/10万)
城市预测值
(1/10万)
农村预测值
(1/10万)
城乡率比
2025年3.092.443.790.64
2030年1.671.221.900.64
合计EAPC(%)-11.58a--0.001a
t-1 072--2.81
P0--0.02
), ArticleFig(id=1240413540792726095, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240395007379698477, language=CN, label=表3, caption=

江西省2025及2030年MMR预测值、城乡率比和EAPC

, figureFileSmall=null, figureFileBig=null, tableContent=
年份合计预测值
(1/10万)
城市预测值
(1/10万)
农村预测值
(1/10万)
城乡率比
2025年3.092.443.790.64
2030年1.671.221.900.64
合计EAPC(%)-11.58a--0.001a
t-1 072--2.81
P0--0.02
), ArticleFig(id=1240413540897583707, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240395007379698477, language=EN, label=Table 4, caption=

Projected IMR, Rural-Urban Rate Ratio and EAPC for Jiangxi Province in 2025 and 2030

, figureFileSmall=null, figureFileBig=null, tableContent=
年份合计预测值
(‰)
城市预测值
(‰)
农村预测值
(‰)
城乡率比
2025年2.522.282.810.81
2030年1.511.441.630.88
合计EAPC(%)-9.75a--1.85a
t-713.4--20.12
P0--0
), ArticleFig(id=1240413540994052703, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240395007379698477, language=CN, label=表4, caption=

江西省2025及2030年IMR预测值、城乡率比和EAPC

, figureFileSmall=null, figureFileBig=null, tableContent=
年份合计预测值
(‰)
城市预测值
(‰)
农村预测值
(‰)
城乡率比
2025年2.522.282.810.81
2030年1.511.441.630.88
合计EAPC(%)-9.75a--1.85a
t-713.4--20.12
P0--0
), ArticleFig(id=1240413541086327400, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240395007379698477, language=EN, label=Table 5, caption=

ProjectedNMR, Rural-Urban Rate Ratio and EAPC National Targets for Jiangxi Province in 2025 and 2030

, figureFileSmall=null, figureFileBig=null, tableContent=
年份合计预测值
(‰)
城市预测值
(‰)
农村预测值
(‰)
城乡率比
2025年1.381.281.520.84
2030年0.790.750.890.84
合计EAPC(%)-10.59a--0.07
t-410.6--1.73
P0--0.12
), ArticleFig(id=1240413541203767918, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240395007379698477, language=CN, label=表5, caption=

江西省2025及2030年NMR预测值、城乡率比和EAPC

, figureFileSmall=null, figureFileBig=null, tableContent=
年份合计预测值
(‰)
城市预测值
(‰)
农村预测值
(‰)
城乡率比
2025年1.381.281.520.84
2030年0.790.750.890.84
合计EAPC(%)-10.59a--0.07
t-410.6--1.73
P0--0.12
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江西省2021—2030年孕产妇、婴儿和新生儿死亡率预测
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周思雨 1 , 徐奋飞 2 , 马婧杰 1 , 张兴超 1 , 丁静 1 , 邓雨婷 1 , 胡松波 1
现代预防医学 | 流行病与统计方法 2025,52(14): 2497-2501
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现代预防医学 | 流行病与统计方法 2025, 52(14): 2497-2501
江西省2021—2030年孕产妇、婴儿和新生儿死亡率预测
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周思雨1, 徐奋飞2, 马婧杰1, 张兴超1, 丁静1, 邓雨婷1, 胡松波1
作者信息
  • 1.南昌大学公共卫生学院/疾病预防与公共卫生江西省重点实验室,江西 南昌 330006
  • 2.江西省卫生健康事业发展中心
  • 周思雨(1999—),女,硕士在读,研究方向:疾病负担与生物统计

通讯作者:

胡松波,E-mail:
Projections of maternal, infant and neonatal mortality rates, Jiangxi Province, 2021-2030
Si-yu ZHOU1, Fen-fei XU2, Jing-jie MA1, Xing-chao ZHANG1, Jing DING1, Yu-ting DENG1, Song-bo HU1
Affiliations
  • School of Public Health, Jiangxi Medical College, Nanchang University/ Jiangxi Provincial Key Laboratory of Disease Prevention and Public Health, Nanchang University, Nanchang, Jiangxi 330006, China
出版时间: 2025-07-25 doi: 10.20043/j.cnki.MPM.202504008
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目的

分析江西省2003—2020年合计及城乡孕产妇、婴儿及新生儿死亡率的变化趋势,并预测2030年合计及城乡孕产妇死亡率(MMR)、婴儿死亡率(IMR)、新生儿死亡率(NMR),为健康中国2030期间江西省妇幼健康事业的发展和规划提供依据。

方法

基于江西省2003—2020年合计及城乡孕产妇死亡率(MMR)、婴儿死亡率(IMR)、新生儿死亡率(NMR)数据建立灰色GM(1,1)、支持向量机(SVM)、神经网络自回归(NNAR)、二次指数平滑(ES)模型,挑选出最优模型,对2021—2030年的合计及城乡MMR、IMR、NMR进行预测。再利用已经预测出的合计死亡率对城乡MMR、IMR、NMR进行调整。

结果

江西省2025及2030年合计MMR、IMR、NMR分别为3.09/10万、2.52‰、1.38‰、1.67/10万、1.51‰、0.79‰;2025及2030年城市MMR、IMR、NMR分别为2.44/10万、2.28‰、1.28‰、1.22/10万、1.44‰、0.75‰;2025及2030年农村MMR、IMR、NMR分别为3.79/10万、2.81‰、1.52‰、1.90/10万、1.63‰、0.89‰。

结论

预计江西省2025及2030年合计以及分城乡MMR、IMR、NMR均能提前达到《“健康中国2030”规划纲要》等政策提出的既定目标,孕产妇、婴儿与新生儿死亡率的城乡差距虽呈持续收窄态势,但农村地区相关指标仍高于城市地区,这一客观差距表明,持续推进农村妇幼卫生保健服务,优化基层资源配置仍是公共卫生工作的重点方向。

孕产妇死亡率  /  新生儿死亡率  /  婴儿死亡率  /  预测  /  城乡
Objective

To analyze the trends of maternal, infant and neonatal mortality rates in Jiangxi Province from 2003 to 2020 in the aggregate and in urban and rural areas, and to predict the maternal mortality rate (MMR), infant mortality rate (IMR), and neonatal mortality rate (NMR) in the aggregate and in urban and rural areas in 2030, in order to provide a basis for the development and planning of the maternal and child health care industry in Jiangxi Province during the period of Healthy China 2030.

Methods

Based on the combined and urban-rural MMR, IMR, and NMR data of Jiangxi Province from 2003 to 2020, the Gray GM (1, 1), support vector machine (SVM), neural network auto-regression (NNAR), quadratic exponential smoothing (ES) models were established, and the optimal model was selected to predict the combined and urban-rural MMR, IMR, and NMR for the years 2021-2030. The already predicted aggregate mortality rates were then used to adjust the urban and rural MMR, IMR, and NMR.

Results

In Jiangxi Province, the combined MMR, IMR, and NMR in 2025 and 2030 were 3.09/100 000, 2.52‰, 1.38‰, 1.67/100 000, 1.51‰, and 0.79‰. The urban MMR, IMR, and NMR in 2025 and 2030 were 2.44/100 000, 2.28‰, 1.28‰, and 1.22/100 000, 1.44‰, 0.75‰.And rural MMR, IMR, and NMR in 2025 and 2030 were 3.79/100 000, 2.81‰, 1.52‰,1.90/100 000, 1.63‰, and 0.89‰.

Conclusion

It is expected that Jiangxi Province will reach the targets set out in the “Healthy China 2030” Plan and other policies ahead of schedule in 2025 and 2030 in terms of MMR, IMR and NMR. Although the gap between urban and rural areas in terms of maternal, infant and neonatal mortality rates continues to narrow, the relevant indicators in rural areas are still higher than those in urban areas, an objective gap that indicates that the continued promotion of maternal and child health-care services in rural areas and the optimization of resource allocation at the grass-roots level are still the key directions of public health work.

MMR  /  NMR  /  IMR  /  Prediction  /  Urban and rural
周思雨, 徐奋飞, 马婧杰, 张兴超, 丁静, 邓雨婷, 胡松波. 江西省2021—2030年孕产妇、婴儿和新生儿死亡率预测. 现代预防医学, 2025 , 52 (14) : 2497 -2501 . DOI: 10.20043/j.cnki.MPM.202504008
Si-yu ZHOU, Fen-fei XU, Jing-jie MA, Xing-chao ZHANG, Jing DING, Yu-ting DENG, Song-bo HU. Projections of maternal, infant and neonatal mortality rates, Jiangxi Province, 2021-2030[J]. Modern Preventive Medicine, 2025 , 52 (14) : 2497 -2501 . DOI: 10.20043/j.cnki.MPM.202504008
孕产妇死亡率(maternal mortality rate, MMR) 、婴儿死亡率(infant mortality rate, IMR)与新生儿死亡率(neonatal mortality rate, NMR)是国家妇幼保健机构绩效考核的重要指标之一[1],常被用来检验一个国家或地区妇幼卫生健康、卫生工作和服务水平。也反映了一个国家、地区的经济、教育与卫生状况水平[2]。2030年是检验全球和中国妇幼健康目标是否达成的“终考年”,更是推动医疗公平和政策优化的关键窗口期。本研究通过建立预测模型来对2030年MMR、NMR、IMR进行预测,为健康中国2030期间江西省妇幼健康事业的发展和规划提供依据。
用于预测的江西省2003—2020年孕产妇死亡率、婴儿死亡率、新生儿死亡率的数据均来源于江西省妇幼健康信息报告系统,该系统监测了每年江西省孕产妇死亡率、婴儿死亡率与新生儿死亡率数据。
将个别波动较大的原始数据进行以简单移动平均法的平滑,平滑后为了保持该年合计率和城乡水平的一致,基于合计率与城乡率的关系,按照该年城乡人口权重对城乡率进行了简单的修正。
将处理后的2003—2020年合计死亡率数据按照15∶3的比例分为训练集和测试集,2003—2017年为训练集,2018—2020年为测试集。分别用GM(1,1)、SVM、NNAR、ES模型进行拟合,从中选出最优模型,利用最优模型对2021—2030年MMR、IMR、NMR进行预测。
灰色GM(1,1)模型 灰色系统理论由华中科技大学邓聚龙[3]于20世纪80年代首创,该理论主要是针对小样本、少信息、部分信息已知,而部分信息未确定的系统。而灰色GM(1,1)模型灰色系统理论中最主要的单变量预测模型,是研究小数据、信息贫乏的不确定性问题的有效途径。
SVM模型 支持向量机模型(Support Vector Machines, SVM)利用不同的核函数,通过一个非线性映射将训练数据向量映射到一个高维的特征向量空间,并在这个高维空间中进行线性回归[4]
NNAR模型 神经网络自回归模型(Neural Network Autoregression, NNAR)是基于人工神经网络和基于简单大脑数学模型的预测方法[5]
ES模型(Exponential Smoothing Models)二次指数平滑法实际上是对一次指数平滑法再做了一次指数平滑,将各滞后期的数据进行加权平均作为未来值,克服了一次指数平滑法无法对明显变化趋势的现象进行预测的缺陷[6]
EAPC(Estimated Annual Percentage Changes)年度百分比变化采用线性回归的方法描述死亡率的变化趋势[7],将不同年份的死亡率进行对数转换,Y=lg(死亡率)为因变量,以X=年份为自变量,拟合直线:Y=b+aX,得出a值。EAPC=(10a-1)×100%,并对EAPC进行假设检验,检验水准α=0.05[8]
评价指标 本文选用平均相对误差的绝对值(mean absolute percentage error,MAPE)、均方根误差(root mean square error,RMSE)作为评价标准,评价模型预测的精度与效果。MAPE、RMSE值越小代表模型预测精度越好,当MAPE<15%[10]时,提示预测精度较好。计算公式如下:
其中:N为样本数量,yi为实际值,yi’为预测值。
(1)2021—2030年城乡率的比值估计 利用2003—2020年城乡死亡率数据求出2003—2020年城乡率的率比,再将城乡率比数据拟合GM(1,1)、SVM、NNAR、ES 4种模型,对2021—2030年城乡率比进行预测。(2)城乡结构比的估计 城乡结构比数据参照联合国人口司报告的《World Urbanization Prospects》[11]中国城乡人口及预测。(3)合计率的城乡分解 利用上述两步得出的城乡率比和城乡结构比,根据合计率=城市结构比*城市率+农村结构比*农村率这个公式分解各年份的合计率为城市和农村MMR、IMR、NMR。
方法采用Excel 2016建立数据库,采用R 4.3.1为统计软件,进行模型构建、数据分析与可视化。采用MAPE、RMSE等指标对模型精确度进行评价。
2003—2020年MMR、IMR和NMR整体呈下降趋势,EAPC分别为-10.99、 -9.65、-10.46,变化趋势差异有统计学意义(均P<0.05),MMR城乡率比下降趋势无统计学意义(P=0.96),IMR、NMR城乡率比呈上升趋势(均P<0.05),见表1
MMR的最优模型为ES模型,MAPE为9.55%,IMR的最优模型为SVM模型,MAPE为3.06%,NMR的最优模型为SVM模型,MAPE为6.76%,见表2
江西省MMR 2025年以及2030年分别为3.09/10万、1.67/10万,其中城市MMR 2025年以及2030年分别为2.44/10万、1.22/10万,农村MMR 2025年以及2030年分别为3.79/10万、1.90/10万。MMR城乡率比预计2025年为0.64,2030年为0.64。2020-2030年MMR呈下降趋势,EAPC为-11.58(P<0.05),MMR城乡率比呈上升趋势,EAPC为0.001(P<0.05)。2003-2030年合计及分城乡MMR的实际值以及预测值的时间序列图如图1所示。
江西省IMR2025以及2030年分别为2.52‰、1.51‰,其中城市IMR 2025年以及2030年分别为2.28‰、1.44‰,农村IMR 2025年以及2030年分别为2.81‰、1.63‰。IMR城乡率比预计2025年为0.81,2030年为0.88。2020—2030年IMR呈下降趋势,EAPC为-9.75(P<0.05),IMR城乡率比呈上升趋势,EAPC为1.85(P<0.05)。2003年-2030年合计及分城乡IMR的实际值以及预测值的时间序列图如图2所示。
江西省NMR 2025年以及2030年分别为1.38‰、0.79‰,其中城市NMR 2025年以及2030年分别为1.28‰、0.75‰,农村NMR 2025年以及2030年分别为1.52‰、0.89‰。NMR城乡率比预计2025年为0.84,2030年为0.84。2020—2030年NMR呈下降趋势,EAPC为-10.59(P<0.05),城乡率比下降趋势无统计学意义(P=0.12)。2003—2030年合计及分城乡NMR的实际值以及预测值的时间序列图如图3所示。
本研究基于江西省2003—2020年孕产妇死亡率、婴儿死亡率以及新生儿死亡率数据分别采用灰色GM(1,1)、NNAR、SVM与ES模型对2021—2030年江西省孕产妇死亡率、婴儿死亡率和新生儿死亡率进行预测,采用MAPE对模型精度进行评价,结果均小于10%,说明本研究的预测精度较好。
江西省2003—2020年,孕产妇死亡率、婴儿死亡率以及新生儿死亡率均呈下降趋势,EAPC均为负值,且下降趋势均有统计学意义,这一结果与新疆[12]十一五至十二五时期孕产妇死亡率趋势一致。2003年始,国家政府实施新型农村合作医疗,之后又进行了医疗与保险改革[13],使江西省在妇幼卫生保健工作上也成效显著。监测数据显示,江西省农村地区的孕产妇死亡率、婴儿死亡率及新生儿死亡率一直高于城市,但其对应的城乡死亡率率比(城市死亡率/农村死亡率)在逐渐增大,该指标在小于1的区间内持续趋近于1,提示城乡差距在逐年减小,表明既往政策取得成果。另一方面也说明为实现健康中国2030战略目标,需持续强化农村地区公共卫生资源投入及健康促进策略。
本研究的预测结果表明,2025年江西省合计孕产妇死亡率、婴儿死亡率和新生儿死亡率分别为3.09/10万、2.52‰、1.38‰;2030年分别为1.67/10万、1.51‰、0.79‰,达到《江西省妇女发展纲要(2021—2030年)》[14]、《江西省儿童发展纲要(2021—2030年)》[14]与《“健康中国2030”规划纲要》提出目标。2020—2030年,孕产妇死亡率、婴儿死亡率以及新生儿死亡率的城乡死亡率率比均呈上升趋势,由于率比始终小于1,说明城市孕产妇、婴儿与新生儿死亡率仍低于农村,但两者的相对差距在逐年减小,与俞跃萍[15]的研究结果一致。提示,江西省对农村地区妇幼卫生保健工作的投入已见成效,农村地区医疗服务的改善[16],城市化可能也是导致这一结果的另一个原因[17]。江西省应该继续保持农村地区妇幼卫生保健工作[18]
本研究的局限性在于,本研究采用GM(1,1)模型、NNAR模型、SVM模型以及ES模型对孕产妇死亡率、婴儿死亡率以及新生儿死亡率进行预测,由于模型自身的局限性,不能提供预测值的95%置信区间,但是本研究提供了模型预测的误差评估值。
综上所述,江西省2021—2030年合计MMR、NMR、IMR总体呈现下降趋势,且都能达到《“健康中国2030”规划纲要》[19]等政策所提出的既定目标,孕产妇、婴儿与新生儿死亡率的城乡差距虽呈持续收窄态势,但农村地区相关指标仍高于城市地区,这一客观差距表明,持续推进农村妇幼卫生保健服务,优化基层资源配置仍是公共卫生工作的重点方向。
  • 江西省自然科学基金(20224BAB206094)
  • 江西省卫健委科技计划(202211345)
  • 国家自然科学基金(81960618)
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2025年第52卷第14期
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doi: 10.20043/j.cnki.MPM.202504008
  • 接收时间:2025-04-01
  • 首发时间:2026-03-16
  • 出版时间:2025-07-25
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  • 收稿日期:2025-04-01
基金
江西省自然科学基金(20224BAB206094)
江西省卫健委科技计划(202211345)
国家自然科学基金(81960618)
作者信息
    1.南昌大学公共卫生学院/疾病预防与公共卫生江西省重点实验室,江西 南昌 330006
    2.江西省卫生健康事业发展中心

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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
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