Evidence map›Paper›PMID 31689786›Full record

Observational studyMedicine2019

The national maternal near miss surveillance in China: A facility-based surveillance system covered 30 provinces.

Yi Mu, Xiaodong Wang, Xiaohong Li, Zheng Liu, Mingrong Li, Yanping Wang, Qi Li, Kui Deng, Jun Zhu, Juan Liang

Open access · goldAbstract readObservational Study
In one paragraph

Observational study in Medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed, 1 pooled it
7.3field-weighted citation impact, top 3% of its field
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

15 citing papers in PubMed, 1 synthesis or guideline pooled it, 34 citations in OpenAlex.

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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 at 2 institutions in 1 country.

Yi MuNational Office for Maternal and Child Health Surveillance of China West China Second University Hospital, Sichuan University, Chengdu, Sichuan.
Xiaodong WangDepartment of Obstetrics, West China Second University Hospital, Sichuan University, Chengdu, Sichuan.
Xiaohong LiNational Office for Maternal and Child Health Surveillance of China West China Second University Hospital, Sichuan University, Chengdu, Sichuan.
Zheng LiuNational Office for Maternal and Child Health Surveillance of China West China Second University Hospital, Sichuan University, Chengdu, Sichuan.
Mingrong LiNational Office for Maternal and Child Health Surveillance of China West China Second University Hospital, Sichuan University, Chengdu, Sichuan.
Yanping WangNational Office for Maternal and Child Health Surveillance of China West China Second University Hospital, Sichuan University, Chengdu, Sichuan.
Qi LiNational Office for Maternal and Child Health Surveillance of China West China Second University Hospital, Sichuan University, Chengdu, Sichuan.
Kui DengNational Office for Maternal and Child Health Surveillance of China West China Second University Hospital, Sichuan University, Chengdu, Sichuan.
Jun ZhuNational Office for Maternal and Child Health Surveillance of China West China Second University Hospital, Sichuan University, Chengdu, Sichuan.
Juan LiangNational Office for Maternal and Child Health Surveillance of China West China Second University Hospital, Sichuan University, Chengdu, Sichuan.
Sichuan University · CNWest China Second University Hospital of Sichuan University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

To introduce the National Maternal Near Miss Surveillance System (NMNMSS) in detail and to report the composition of maternal near miss (MNM) in China.The NMNMSS was established by the National Health Commission at the end of 2010, covered over 400 health facilities from 30 provinces in China. The NMNMSS was designed to collect individual information for every pregnant woman admitted to obstetric department in the sampled health facilities. Cross tabulations and correlations were used to describe the distribution of population and sampled facilities in the NMNMSS, and to calculate the MNM mortality ratio for different complications and organ dysfunctions.The individual survey forms of 9,051,638 pregnant women were collected in the NMNMSS between 2012 and 2017. Compared with urban areas, there are very few well-quality medical resources in rural areas. Most women with pregnancy complications in rural areas can only be treated in Level 2 and lower hospitals. MNM in women with indirect obstetric complications received treatment more frequently in Level 3 hospital. The most common maternal complications in severe maternal outcomes (including maternal near miss and maternal death) are obstetrics hemorrhage (58.7%), hypertension disorder (28.0%), and severe anemia (20.6%). The overall MNM mortality ratio is 38:1. The MNM mortality ratios are lowest in amniotic fluid embolism, HIV/AIDS, heart disease, thrombophlebitis, and sepsis. For different organ dysfunctions, the ranks of the MNM mortality ratio from low to high are renal dysfunction, respiratory dysfunction, cardiovascular dysfunction, hepatic dysfunction, neurologic dysfunction, uterine dysfunction, coagulation dysfunction.The NMNMSS is a well-established hospital-based surveillance system for maternal complications in China. It can identify the maternal complications that need to improve health care immediately in China through a powerful longitudinal real-world evidence.

Indexed as

ChinaFemaleHealth Services AccessibilityHumansNear Miss, HealthcareObstetric Labor ComplicationsPregnancyPregnancy ComplicationsPublic Health SurveillanceResidence CharacteristicsSocioeconomic Factors

Identifiers

PMID31689786
PMCPMC6946185
OpenAlexW2984117550

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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.