2000―2030年江西省各地级市5岁以下儿童死亡率估计和趋势分析

张兴超; 陈三仟; 马婧杰; 邓雨婷; 周思雨; 丁静; 胡松波

doi:10.16462/j.cnki.zhjbkz.2024.11.014

2000―2030年江西省各地级市5岁以下儿童死亡率估计和趋势分析

doi: 10.16462/j.cnki.zhjbkz.2024.11.014

张兴超^{1, 2},
陈三仟^{1, 2},
马婧杰^{1, 2},
邓雨婷^{1, 2},
周思雨^{1, 2},
丁静^{1, 2},
胡松波^{1, 2, ,}

1.
南昌大学公共卫生学院流行病与卫生统计学系，南昌 330006
2.
疾病预防与公共卫生江西省重点实验室，南昌 330006

基金项目:

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

详细信息

通讯作者:
胡松波，E-mail: husbo0910@ncu.edu.cn

中图分类号: R179
计量
- 文章访问数: 78
- HTML全文浏览量: 27
- PDF下载量: 7
- 被引次数: 0
出版历程
- 收稿日期: 2024-01-08
- 修回日期: 2024-06-06
- 网络出版日期: 2024-12-23
- 刊出日期: 2024-11-10

Estimates and trends analysis of under 5 mortality rates by prefecture-level cities in Jiangxi, 2000-2030

ZHANG Xingchao^{1, 2},
CHEN Sanqian^{1, 2},
MA Jingjie^{1, 2},
DENG Yuting^{1, 2},
ZHOU Siyu^{1, 2},
DING Jing^{1, 2},
HU Songbo^{1, 2
, ,}

1.
Department of Epidemiology and Health Statistics, School of Public Health, Jiangxi Medical College, Nanchang University, Nanchang 330006, China
2.
Jiangxi Provincial Key Laboratory of Disease Prevention and Public Health, Nanchang University, Nanchang 330006, China

Funds:

Jiangxi Provincial Natural Science Foundation 20224BAB206094

Jiangxi Provincial Health Commission Science and Technology Program 202211345

National Natural Science Foundation of China 81960618

More Information

Corresponding author: HU Songbo, E-mail: husbo0910@ncu.edu.cn

摘要

摘要: 目的分析2000―2030年江西省各地级市5岁以下儿童死亡率(under 5 mortality rate, U5MR)的趋势和地区差异。方法采用2000―2012年江西省县(区)U5MR、出生人口计算各地级市U5MR，构建线性混合效应模型估计2013―2022年江西省各地级市的U5MR。用一阶单变量灰色模型[grey model with one variable and one-order differential equation, GM(1, 1)]或等维递补GM(1, 1)模型对2023―2030年江西省各地级市U5MR进行预测。用集中指数和集中曲线衡量江西省5岁以下儿童的健康不平等。结果 2000年江西省U5MR为48.842‰，最低和最高的地级市分别为南昌市(36.616‰)、赣州市(58.729‰)。2030年江西省U5MR预计为3.472‰，最低和最高地级市预计分别为南昌市(1.980‰)、宜春市(4.987‰)。2000―2030年江西省各地级市的U5MR均呈快速下降趋势，地区差异随U5MR的下降在逐渐缩小。2000、2005、2010、2015、2020年江西省5岁以下儿童死亡率的集中指数分别为-0.051、-0.065、-0.090、-0.096、-0.101。结论 2000―2030年江西省U5MR整体呈下降趋势，地区差异随U5MR的下降在逐渐缩小，与社会经济水平相关的5岁以下儿童健康不平等呈扩大趋势。
- 5岁以下儿童死亡率 /
- 线性混合效应模型 /
- GM(1, 1)模型 /
- 集中指数
Abstract: Objective To analyze the trend and regional differences in the under 5 mortality rate (U5MR) at the level of each prefecture-level cities in Jiangxi from 2000 to 2030 U5MR over this period. Methods The U5MR of each prefecture-level city in Jiangxi was calculated based on the U5MR of each district and county in Jiangxi and the birth population from 2000 to 2012, and then based on this data, a linear mixed-effects model was constructed to estimate the U5MR of prefecture-level cities in Jiangxi from 2013 to 2022. GM(1, 1) or equal-dimensional recursive GM(1, 1) model was used to forecast U5MR in Jiangxi prefecture-level cities from 2023 to 2030. Health inequalities among children under 5 years of age in Jiangxi were measured using the concentration index and concentration curve. Results In 2000, the U5MR in Jiangxi was 48.842 ‰, with the lowest and highest prefecture-level cities being Nanchang (36.616‰) and Ganzhou (58.729‰), respectively. In 2030, the U5MR in Jiangxi might be 3.472‰, with the lowest and highest prefecture-level cities projected to be Nanchang (1.980‰) and Yichun (4.987‰), respectively. The U5MR of each prefecture-level city in Jiangxi got a rapid decline from 2000 to 2030, and the regional differences were gradually narrowing with the decline of U5MR. The concentration indices of U5MR in Jiangxi in 2000, 2005, 2010, 2015 and 2020 were -0.051, -0.065, -0.090, -0.096 and -0.101, respectively. Conclusions From 2000 to 2030, the overall U5MR in Jiangxi shows a decreasing trend, and the regional differences gradually narrows with the decrease of U5MR, and the inequality in the health of children under 5 years of age related to the socio-economic level shows a widening trend.
- Under 5 mortality rate /
- Linear mixed-effects model /
- GM(1, 1) model /
- Concentration index

HTML全文

图 1 2005、2025年江西省各地级市5岁以下儿童死亡率及排序(从高到低)

Figure 1. Under 5 mortality rate and ranking (from highest to lowest) by prefecture-level cities in Jiangxi, 2005 and 2025

下载: 全尺寸图片幻灯片

图 2 2000―2030年江西省11个地级市的5岁以下儿童死亡率(‰)

Figure 2. Box plots of under 5 mortality rate (‰) in 11 prefecture-level cities in Jiangxi, 2000-2030

下载: 全尺寸图片幻灯片

图 3 2000―2020年江西省5岁以下儿童死亡率的集中曲线

Figure 3. Concentration curves of under 5 mortality rate in Jiangxi, 2000-2020

下载: 全尺寸图片幻灯片

表 1 2000―2030年每隔3年的江西省各地级市5岁以下儿童死亡率(‰)

Table 1. Under 5 mortality rate (‰) in Jiangxi at 3-year intervals, 2000-2030, by prefecture-level cities

地区 Region	年份 Year
地区 Region	2000	2003	2006	2009	2012	2015	2018	2021	2024	2027	2030
南昌市 Nanchang	36.616^①	26.074^①	18.913^①	14.254^①	12.523^①	8.365^①	6.241^①	4.690^①	3.525^①	2.644^①	1.980^①
景德镇市 Jingdezhen	58.357	34.873	25.278	18.996	14.039	9.823	7.233	5.309	3.914	2.883	2.124
萍乡市 Pingxiang	49.165	36.415	27.444	21.755	15.359	12.035	9.072	6.859	5.174	3.906	2.948
九江市 Jiujiang	38.011	26.490	19.081	17.978	14.680	10.859	8.447	6.605	5.155	4.027	3.146
新余市 Xinyu	52.192	38.496	30.714	23.527	18.282	13.703	10.381	7.871	5.964	4.508	3.407
鹰潭市 Yingtan	40.660	37.709	30.067	20.624	15.967	11.859	9.151	7.073	5.469	4.231	3.273
赣州市 Ganzhou	58.729^①	47.981^①	36.716^①	27.351^①	22.453^①	16.614^①	12.845^①	9.958	7.709	5.968	4.618
吉安市 Ji′an	44.843	36.432	28.685	21.002	15.784	12.060	9.131	6.912	5.232	3.962	3.000
宜春市 Yichun	47.303	41.238	33.671	25.996	19.902	16.410	12.796	10.141^①	7.996^①	6.315^①	4.987^①
抚州市 Fuzhou	58.245	39.034	27.257	19.967	16.266	10.706	7.870	5.758	4.218	3.086	2.254
上饶市 Shangrao	47.599	36.316	26.323	23.582	21.083	15.351	12.151	9.689	7.724	6.162	4.916
江西省 Jiangxi	48.842	37.767	28.359	22.162	18.070	13.292	10.177	7.794	5.963	4.553	3.472
注：①代表对应年份各地区5岁以下儿童死亡率的最大值和最小值。 Note: ① Represents the maximum and minimum under five mortality rates by region for the corresponding year.

下载: 导出CSV

表 2 2000―2030年江西省各地级市5岁以下儿童死亡率的变化幅度百分比及年均变化率

Table 2. Percentage change and average annual rate of change of under 5 mortality rate in prefecture-level cities in Jiangxi, 2000-2030

地区 Region	变化幅度百分比 Change magnitude percentage/%			年均变化率 Annual average rate of change/%
地区 Region	2000―2012年year	2013―2022年year	2023―2030年year	2000―2012年year	2013―2022年year	2023―2030年year
南昌市 Nanchang	-65.799	-58.029	-48.903	-8.553	-9.196	-9.146
景德镇市 Jingdezhen	-75.943^①	-60.543	-50.992	-11.195^①	-9.817	-9.687
萍乡市 Pingxiang	-68.760	-57.067	-48.126	-9.240	-8.967	-8.950
九江市 Jiujiang	-61.380	-52.618	-43.791	-7.622	-7.964	-7.900
新余市 Xinyu	-64.972	-56.513	-47.921	-8.371	-8.837	-8.899
鹰潭市 Yingtan	-60.730	-53.927	-45.065	-7.494	-8.250	-8.202
赣州市 Ganzhou	-61.768	-53.844	-45.017	-7.700	-8.232	-8.190
吉安市 Ji′an	-64.802	-56.778	-47.735	-8.334	-8.899	-8.853
宜春市 Yichun	-57.927	-51.198	-42.347	-6.961	-7.662	-7.566
抚州市 Fuzhou	-72.073	-60.893^①	-51.899^①	-10.084	-9.906^①	-9.927^①
上饶市 Shangrao	-55.707^①	-49.838^①	-40.977^①	-6.561^①	-7.379^①	-7.255^①
江西省 Jiangxi	-63.003	-55.116	-46.765	-7.952	-8.516	-8.613
注：①表示对应时期各地区变化幅度及年均变化率绝对值的最大值和最小值。 Note: ①The black bold font in the table indicates the maximum and minimum values of the absolute value of the magnitude of change and the absolute value the annual average rate of change for each region in the corresponding period.

下载: 导出CSV

表 3 江西省各地级市5岁以下儿童死亡率最佳类型灰色模型的误差评估

Table 3. Error assessment of the best grey model for under 5 mortality rate in prefecture-level cities in Jiangxi

地区 Region	最佳类型的灰色模型 Optimal type of grey model	MAE	MAPE	RMSE
南昌市 Nanchang	6维等维递补GM(1, 1)模型 6-dimensional equal-dimensional recursive GM(1, 1) model	0.000 16	2.653 85	0.000 21
景德镇市 Jingdezhen	8维GM(1, 1)模型 8-dimensional GM(1, 1) model	0.000 07	0.988 52	0.000 09
萍乡市 Pingxiang	5维GM(1, 1)模型 5-dimensional GM(1, 1) model	0.000 15	1.918 85	0.000 16
九江市 Jiujiang	7维GM(1, 1)模型 7-dimensional GM(1, 1) model	0.000 11	1.217 55	0.000 13
新余市 Xinyu	5维等维递补GM(1, 1)模型 5-dimensional equal-dimensional recursive GM(1, 1) model	0.000 06	0.588 77	0.000 07
鹰潭市 Yingtan	5维GM(1, 1)模型 5-dimensional GM(1, 1) model	0.000 03	0.330 60	0.000 04
赣州市 Ganzhou	6维等维递补GM(1, 1)模型 6-dimensional equal-dimensional recursive GM(1, 1) model	0.000 12	0.803 06	0.000 18
吉安市 Ji′an	7维GM(1, 1)模型 7-dimensional GM(1, 1) model	0.000 02	0.201 63	0.000 02
宜春市 Yichun	7维GM(1, 1)模型 7-dimensional GM(1, 1) model	0.000 19	1.651 59	0.000 21
抚州市 Fuzhou	7维等维递补GM(1, 1)模型 7-dimensional equal-dimensional recursive GM(1, 1) model	0.000 20	2.234 19	0.000 29
上饶市 Shangrao	6维GM(1, 1)模型 6-dimensional GM(1, 1) model	0.000 13	1.108 36	0.000 15
江西省 Jiangxi	6维等维递补GM(1, 1)模型 6-dimensional equal-dimensional recursive GM(1, 1) model	0.000 10	1.015 22	0.000 12
注：MAE，平均绝对误差；MAPE，平均绝对百分比误差；RMSE，均方根误差；GM(1, 1), 一阶单变量灰色模型。 Note: MAE, mean absolute error; MAPE, mean absolute percentage error; RMSE, root mean square error; GM(1, 1), grey model with one variable and one-order differential equation.

下载: 导出CSV

参考文献(14)

[1]	United Nations. United nations sustainable development goals [EB/OL]. (2017-05-30)[2024-01-05]. https://www.un.org/sustainabledevelopment/health/.
[2]	中华人民共和国中央人民政府. 国务院关于印发中国妇女发展纲要和中国儿童发展纲要的通知[EB/OL]. (2021-10-20)[2024-01-01]. https://www.gov.cn/gongbao/content/2021/content_5643262.htm.
[3]	江西省卫生健康委员会. 中共江西省委江西省人民政府关于印发《"健康江西2030"规划纲要》的通知[EB/OL]. (2019-12-25)[2024-01-01]. http://hc.jiangxi.gov.cn/art/2019/12/25/art_38264_2417692.html.
[4]	江西省人民政府. 江西省人民政府办公厅关于印发江西省"十四五"卫生健康发展规划的通知[EB/OL]. (2021-09-30)[2024-01-01]. http://www.jiangxi.gov.cn/art/2021/10/20/art_4968_3692594.html.
[5]	王雪, 任正洪, 安琳. 利用妇幼卫生监测资料评价妇幼卫生年报儿童与孕产妇死亡数据[J]. 中国妇幼保健, 2011, 26(27): 4165-4168. Wang X, Ren ZH, An L. Evaluation of the data of children and maternal death from the national annual report system of MCH using the data based on the surveillance system of MCH [J]. Maternal and Child Health Care of China, 2011, 26(27): 4165-4168.
[6]	李成, 米红. 中国1982年后人口普查和抽样调查中死亡漏报的估计: 基于Bayesian分层回归模型[J]. 人口研究, 2022, 46(1): 19-36. Li C, Mi H. Estimation of death underreporting in population censuses and sample surveys of China since 1982 [J]. Population Research, 2022, 46(1): 19-36.
[7]	Wang YP, Li XH, Zhou MG, et al. Under-5 mortality in 2851 Chinese Counties, 1996-2012: a subnational assessment of achieving MDG 4 goals in China [J]. Lancet, 2016, 387(10015): 273-283. DOI: 10.1016/S0140-6736(15)00554-1.
[8]	GBD 2019 Under-Mortality Collaborators. Global, regional, and national progress towards Sustainable Development Goal 3.2 for neonatal and child health: all-cause and cause-specific mortality findings from the Global Burden of Disease Study 2019 [J]. Lancet, 2021, 398(10303): 870-905. DOI: 10.1016/S0140-6736(21)01207-1.
[9]	李永浮, 鲁奇, 周成虎. 2010年北京市流动人口预测[J]. 地理研究, 2006, 25(1): 131-140. DOI: 10.3321/j.issn:1000-0585.2006.01.016. Li YF, Lu Q, Zhou CH. The prediction of Beijing floating population in 2010 [J]. Geographical Research, 2006, 25(1): 131-140. DOI: 10.3321/j.issn:1000-0585.2006.01.016.
[10]	O′Donnell O, O′Neill S, Van Ourti T, et al. Conindex: estimation of concentration indices [J]. Stata J, 2016, 16(1): 112-138. DOI: 10.1177/1536867X1601600112.
[11]	中华人民共和国国家卫生健康委员会. 2022年我国卫生健康事业发展统计公报[EB/OL]. (2023-10-12)[2024-01-01]. http://www.nhc.gov.cn/cms-search/xxgk/getManuscriptXxgk.htm?id=6707c48f2a2b420fbfb739c393fcca92.
[12]	江西省统计局. 2022年江西省儿童发展纲要统计监测报告[EB/OL]. (2023-10-09)[2024-01-01]. http://www.jiangxi.gov.cn/art/2023/10/19/art_422_4635184.html.
[13]	李敏, 王宝珠. 陕西省2010—2020年5岁以下儿童死亡监测结果分析[J]. 中华疾病控制杂志, 2023, 27(9): 1019-1024. DOI: 10.16462/j.cnki.zhjbkz.2023.09.006. Li M, Wang BZ. Analysis of mortality monitoring results of children under 5 years old in Shaanxi Province from 2010 to 2020 [J]. Chin J Dis Control Prev, 2023, 27(9): 1019-1024. DOI: 10.16462/j.cnki.zhjbkz.2023.09.006.
[14]	Schleiff M, Kumapley R, Freeman PA, et al. Comprehensive review of the evidence regarding the effectiveness of community-based primary health care in improving maternal, neonatal and child health: 5. equity effects for neonates and children [J]. J Glob Health, 2017, 7(1): 010905. DOI: 10.7189/jogh.07.010905.