Evidence map›Paper›PMID 38866837›Full record

ArticleScientific reports2024

Trends in maternal and child health in China and its urban and rural areas from 1991 to 2020: a joinpoint regression model.

Xin-Yue Wang, Bei-Bei Zhang, Yuan-Yi Cao, Qian Xue, Qin Ye, Yuan-Sheng Li, Shu-Yuan Wang, Yuan-Wei Ma, Yan-Que Sun, Jun-Hui Zhang

Abstract read
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Xin-Yue Wang *Department of Epidemiology and Health Statistics, School of Public Health, Southwest Medical University, No.1, Section 1, Xianglin Road, Longmatan District, Luzhou, Sichuan, People's Republic of China.
Bei-Bei Zhang *Department of Epidemiology and Health Statistics, School of Public Health, Southwest Medical University, No.1, Section 1, Xianglin Road, Longmatan District, Luzhou, Sichuan, People's Republic of China.
Yuan-Yi Cao *Department of Epidemiology and Health Statistics, School of Public Health, Southwest Medical University, No.1, Section 1, Xianglin Road, Longmatan District, Luzhou, Sichuan, People's Republic of China.
Qian XueDepartment of Epidemiology and Health Statistics, School of Public Health, Southwest Medical University, No.1, Section 1, Xianglin Road, Longmatan District, Luzhou, Sichuan, People's Republic of China.
Qin YeDepartment of Epidemiology and Health Statistics, School of Public Health, Southwest Medical University, No.1, Section 1, Xianglin Road, Longmatan District, Luzhou, Sichuan, People's Republic of China.
Yuan-Sheng LiSchool of Public Health, Southern Medical University, Guangzhou, 510515, Guangdong, People's Republic of China.
Shu-Yuan WangDepartment of Epidemiology and Health Statistics, School of Public Health, Southwest Medical University, No.1, Section 1, Xianglin Road, Longmatan District, Luzhou, Sichuan, People's Republic of China.
Yuan-Wei MaDepartment of Epidemiology and Health Statistics, School of Public Health, Southwest Medical University, No.1, Section 1, Xianglin Road, Longmatan District, Luzhou, Sichuan, People's Republic of China.
Yan-Que SunDepartment of Epidemiology and Health Statistics, School of Public Health, Southwest Medical University, No.1, Section 1, Xianglin Road, Longmatan District, Luzhou, Sichuan, People's Republic of China.
Jun-Hui ZhangDepartment of Epidemiology and Health Statistics, School of Public Health, Southwest Medical University, No.1, Section 1, Xianglin Road, Longmatan District, Luzhou, Sichuan, People's Republic of China. zjh960500@swmu.edu.cn.

Funding

The Innovation and Entrepreneurship Training Program for University Student in Southwest Medical University 2022-147The National Statistical Science Research Projection of China 2021LZ31The Scientific Research Project of Southwest Medical University 2021ZKMS002
6 · The paper itself

Abstract

The long-term trends in maternal and child health (MCH) in China and the national-level factors that may be associated with these changes have been poorly explored. This study aimed to assess trends in MCH indicators nationally and separately in urban and rural areas and the impact of public policies over a 30‒year period. An ecological study was conducted using data on neonatal mortality rate (NMR), infant mortality rate (IMR), under-five mortality rate (U5MR), and maternal mortality ratio (MMR) nationally and separately in urban and rural areas in China from 1991 to 2020. Joinpoint regression models were used to estimate the annual percentage changes (APC), average annual percentage changes (AAPC) with 95% confidence intervals (CIs), and mortality differences between urban and rural areas. From 1991 to 2020, maternal and child mortalities in China gradually declined (national AAPC [95% CI]: NMRs - 7.7% [- 8.6%, - 6.8%], IMRs - 7.5% [- 8.4%, - 6.6%], U5MRs - 7.5% [- 8.5%, - 6.5%], MMRs - 5.0% [- 5.7%, - 4.4%]). However, the rate of decline nationally in child mortality slowed after 2005, and in maternal mortality after 2013. For all indicators, the decline in mortality was greater in rural areas than in urban areas. The AAPCs in rate differences between rural and urban areas were - 8.5% for NMRs, - 8.6% for IMRs, - 7.7% for U5MRs, and - 9.6% for MMRs. The AAPCs in rate ratios (rural vs. urban) were - 1.2 for NMRs, - 2.1 for IMRs, - 1.7 for U5MRs, and - 1.9 for MMRs. After 2010, urban‒rural disparity in MMR did not diminish and in NMR, IMR, and U5MR, it gradually narrowed but persisted. MCH indicators have declined at the national level as well as separately in urban and rural areas but may have reached a plateau. Urban‒rural disparities in MCH indicators have narrowed but still exist. Regular analyses of temporal trends in MCH are necessary to assess the effectiveness of measures for timely adjustments.

Indexed as

Child HealthChild MortalityInfant MortalityMaternal HealthMaternal MortalityRural PopulationUrban PopulationChildChild, PreschoolChinaFemaleHumansInfantInfant, NewbornMaleInfant mortality rateJoinpoint regression modelMaternal and child healthMaternal mortality ratioNeonatal mortality rateUnder-five mortality rate

Identifiers

PMID38866837
PMCPMC11169526

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