Evidence map›Paper›PMID 33731060›Full record

ArticleBMC pregnancy and childbirth2021

Association between migration status and caesarean section delivery based on a modified Robson classification in China.

Ming Liu, Mengqi Xue, Qing Yang, Wenchong Du, Xiaoling Yan, Jing Tan, Tao Duan, Jing Hua

Open access · goldAbstract read
In one paragraph

Article in BMC pregnancy and childbirth, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.5field-weighted citation impact, top 34% 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

2 citing papers in PubMed, 5 citations in OpenAlex.

  1. Article
  2. 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

8 authors at 3 institutions in 2 countries.

Ming Liu *The Women and Children's Health Care Department, Shanghai First Maternity and Infant Hospital, Tongji University School of Medicine, Shanghai, 200000, China.
Mengqi Xue *The Women and Children's Health Care Department, Shanghai First Maternity and Infant Hospital, Tongji University School of Medicine, Shanghai, 200000, China.
Qing YangSongjiang Maternity & Child Health Hospital of Shanghai, Shanghai, 200042, China.
Wenchong DuDepartment of Psychology, Nottingham Trent University, Nottingham, UK.
Xiaoling YanThe Obstetrical Department, Shanghai First Maternity and Infant Hospital, Tongji University School of Medicine, P.O. 536 Changle Road, Shanghai, 200042, China.
Jing TanThe Obstetrical Department, Shanghai First Maternity and Infant Hospital, Tongji University School of Medicine, P.O. 536 Changle Road, Shanghai, 200042, China.
Tao Duan *The Obstetrical Department, Shanghai First Maternity and Infant Hospital, Tongji University School of Medicine, P.O. 536 Changle Road, Shanghai, 200042, China.
Jing HuaThe Women and Children's Health Care Department, Shanghai First Maternity and Infant Hospital, Tongji University School of Medicine, Shanghai, 200000, China. szhuaj@hotmail.com.
Shanghai First Maternity and Infant Hospital · CNNottingham Trent University · GBShanghai Changning Maternity and Infant Health Hospital · CN

Funding

National Natural Science Foundation of China 81673179Shanghai municipal medical and health discipline construction projects under Grant 2017ZZ02015the Science and Technology Commission of Shanghai Municipality 18140903100the Shenkang Hospital Development Center under Grant SHDC12016239
6 · The paper itself

Abstract

backgroundChina has one of the highest caesarean section (C-Section) rates in the world. In recent years, China has been experiencing a massive flow of migration due to rapid urbanization. In this study, we aimed to differentiate the rates of C-Section between migrants and residents, and explore any possible factors which may moderate the association between migrant status and C-Section rates.

methodsWe conducted a retrospective cohort study in Shanghai, China. All deliveries were classified using the modified Robson Classification. The association between women's migrant status and C-Section rates was assessed using the Poisson regression of sandwich estimation, after adjusting for possible factors.

resultsOf the 40,621 women included in the study, 66.9% were residents and 33.1% were internal migrants. The rate of C-Section in migrants was lower than that of residents in all subjects (39.9 and 47.7%) and in group 1 subjects (based on the Robson Classification) using a modified Robson Classification. There was an association between migrant status and caesarean delivery on maternal request that was statistically significant (RR = 0.664, p < 0.001), but the association was weakened after adjusting for such factors as maternal age at delivery (aRR = 0.774, p = 0.02), ethnicity (aRR = 0.753, p < 0.001), health insurance (aRR = 0.755, p < 0.001), and occupation (aRR = 0.747, p = 0.004), but had no significant changes when adjusting for health conditions (aRR = 0.668, p = 0.001) and all considering variables (aRR = 0.697, p = 0.002). In group 1 subjects, the effect of migrant status on maternal requested intrapartum C-Section was also statistically significant (RR = 0.742, p = 0.004).

conclusionC-Section rates are lower among migrant women than residents, especially on maternal request. The medical practitioners should further reinforce the management of elective C-Section in resident women.

Indexed as

Elective Surgical ProceduresResidence CharacteristicsAdultCesarean SectionChinaEthnicityFemaleHumansPatient PreferencePoisson DistributionPregnancyRetrospective StudiesTransients and MigrantsCaesarean section rateChinaModified Robson classificationResidents and internal migrants

Identifiers

PMID33731060
PMCPMC7971954
OpenAlexW3137903371

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.