Evidence map›Paper›PMID 32413025›Full record

ArticlePLoS medicine2020

Spatiotemporal trends and ecological determinants in maternal mortality ratios in 2,205 Chinese counties, 2010-2013: A Bayesian modelling analysis.

Junming Li, Juan Liang, Jinfeng Wang, Zhoupeng Ren, Dian Yang, Yanping Wang, Yi Mu, Xiaohong Li, Mingrong Li, Yuming Guo and 1 more

Abstract read
In one paragraph

Article in PLoS medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing 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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

13 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
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

11 authors.

Junming LiSchool of Statistics, Shanxi University of Finance and Economics, Taiyuan, Shanxi, China.ORCID 0000-0002-9008-4767
Juan LiangNational Office for Maternal and Child Health Surveillance of China, Department of Obstetrics, West China Second University Hospital, Sichuan University, Chengdu, Sichuan, China.ORCID 0000-0002-3774-9301
Jinfeng WangState Key Laboratory of Resources and Environmental Information System (LREIS), Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China.
Zhoupeng RenState Key Laboratory of Resources and Environmental Information System (LREIS), Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China.
Dian YangState Key Laboratory of Resources and Environmental Information System (LREIS), Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China.
Yanping WangNational Office for Maternal and Child Health Surveillance of China, Department of Obstetrics, West China Second University Hospital, Sichuan University, Chengdu, Sichuan, China.
Yi MuNational Office for Maternal and Child Health Surveillance of China, Department of Obstetrics, West China Second University Hospital, Sichuan University, Chengdu, Sichuan, China.
Xiaohong LiNational Office for Maternal and Child Health Surveillance of China, Department of Obstetrics, West China Second University Hospital, Sichuan University, Chengdu, Sichuan, China.
Mingrong LiNational Office for Maternal and Child Health Surveillance of China, Department of Obstetrics, West China Second University Hospital, Sichuan University, Chengdu, Sichuan, China.
Yuming GuoSchool of Public Health and Preventive Medicine, Monash University, Melbourne, Australia.ORCID 0000-0002-1766-6592
Jun ZhuNational Office for Maternal and Child Health Surveillance of China, Department of Obstetrics, West China Second University Hospital, Sichuan University, Chengdu, Sichuan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAs one of its Millennium Development Goals (MDGs), China has achieved a dramatic reduction in the maternal mortality ratio (MMR), although a distinct spatial heterogeneity still persists. Evidence of the quantitative effects of determinants on MMR in China is limited. A better understanding of the spatiotemporal heterogeneity and quantifying determinants of the MMR would support evidence-based policymaking to sustainably reduce the MMR in China and other developing areas worldwide. METHODS AND

findingsWe used data on MMR collected by the National Maternal and Child Health Surveillance System (NMCHSS) at the county level in China from 2010 to 2013. We employed a Bayesian space-time model to investigate the spatiotemporal trends in the MMR from 2010 to 2013. We used Bayesian multivariable regression and GeoDetector models to address 3 main ecological determinants of the MMR, including per capita income (PCI), the proportion of pregnant women who delivered in hospitals (PPWDH), and the proportion of pregnant women who had at least 5 check-ups (PPWFC). Among the 2,205 counties, there were 925 (42.0%) hotspot counties, located mostly in China's western and southwestern regions, with a higher MMR, and 764 (34.6%) coldspot counties with a lower MMR than the national level. China's westernmost regions, including Tibet and western Xinjiang, experienced a weak downward trend over the study period. Nationwide, medical intervention was the major determinant of the change in MMR. The MMR decreased by 1.787 (95% confidence interval [CI]: 1.424-2.142, p < 0.001) per 100,000 live births when PPWDH increased by 1% and decreased by 0.623 (95% CI 0.436-0.798, p < 0.001) per 100,000 live births when PPWFC increased by 1%. The major determinants for the MMR in China's western and southwestern regions were PCI and PPWFC, while that in China's eastern and southern coastlands was PCI. The MMR in western and southwestern regions decreased nonsignificantly by 1.111 (95% CI -1.485-3.655, p = 0.20) per 100,000 live births when PCI in these regions increased by 1,000 Chinese Yuan and decreased by 1.686 (95% CI 1.275-2.090, p < 0.001) when PPWFC increased by 1%. Additionally, the western and southwestern regions showed the strongest interactive effects between different factors, in which the corresponding explanatory power of any 2 interacting factors reached up to greater than 80.0% (p < 0.001) for the MMR. Limitations of this study include a relatively short study period and lack of full coverage of eastern coastlands with especially low MMR.

conclusionsAlthough China has accomplished a 75% reduction in the MMR, spatial heterogeneity still exists. In this study, we have identified 925 (hotspot) high-risk counties, mostly located in western and southwestern regions, and among which 332 counties are experiencing a slower pace of decrease than the national downward trend. Nationally, medical intervention is the major determinant. The major determinants for the MMR in western and southwestern regions, which are developing areas, are PCI and PPWFC, while that in China's developed areas is PCI. The interactive influence of any two of the three factors, PCI, PPWDH, and PPWFC, in western and southwestern regions was up to and in excess of 80% (p < 0.001).

Indexed as

Bayes TheoremChinaFemaleHumansIncomeLive BirthMaternal MortalityPercutaneous Coronary InterventionPregnancySocioeconomic FactorsYoung Adult

Identifiers

PMID32413025
PMCPMC7228041

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.