Evidence map›Paper›PMID 33082182›Full record

ArticleBMJ open2020

Ten-year time trends in preterm birth during a sociodemographic transition period: a retrospective cohort study in Shenzhen, China.

Rui Ma, Yali Luo, Jun Wang, Yanxia Zhou, Haiyang Sun, Xi Ren, Quan Xu, Lian Zhang, Lingyun Zou

Open access · goldAbstract read
In one paragraph

Article in BMJ open, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed, 1 pooled it
1.6field-weighted citation impact, top 15% 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

8 citing papers in PubMed, 1 synthesis or guideline pooled it, 18 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Observational
  5. Observational
  6. Combined exposure to PMFrontiers in public health · 2023
    Article
  7. Analysis of Differences in Preterm Birth Rates According to Household Occupation in Japan From 2007 to 2019.Journal of preventive medicine and public health = Yebang Uihakhoe chi · 2022
    Article
  8. 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

9 authors at 1 institution in 2 countries.

Rui MaCenter for Big Data Research in Health, Shenzhen Baoan Women's and Children's Hospital, Jinan University, Shenzhen, China.ORCID 0000-0003-0910-3164
Yali LuoDepartment of Prevention & Health Care, Shenzhen Baoan Women's and Children's Hospital, Jinan University, Shenzhen, China.
Jun WangCenter for Big Data Research in Health, Shenzhen Baoan Women's and Children's Hospital, Jinan University, Shenzhen, China.
Yanxia ZhouCenter for Big Data Research in Health, Shenzhen Baoan Women's and Children's Hospital, Jinan University, Shenzhen, China.
Haiyang SunCenter for Big Data Research in Health, Shenzhen Baoan Women's and Children's Hospital, Jinan University, Shenzhen, China.
Xi RenCenter for Big Data Research in Health, Shenzhen Baoan Women's and Children's Hospital, Jinan University, Shenzhen, China.
Quan XuDepartment of Prevention & Health Care, Shenzhen Baoan Women's and Children's Hospital, Jinan University, Shenzhen, China.
Lian ZhangDepartment of Neonatology, Shenzhen Baoan Women's and Children's Hospital, Jinan University, Shenzhen, China lingyun.zou@gmail.com zhliande@qq.com.
Lingyun ZouCenter for Big Data Research in Health, Shenzhen Baoan Women's and Children's Hospital, Jinan University, Shenzhen, China lingyun.zou@gmail.com zhliande@qq.com.ORCID 0000-0002-1109-5709
Jinan University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesTo investigate time trends of preterm birth and estimate the contributions of risk factors to the changes in preterm birth rates over a decade (2009-2018) of transitional period in Shenzhen, China.

designRetrospective cohort study between 2009 and 2018.

settingAll births in Baoan during January 2009 and December 2018 registered in the Shenzhen Birth Registry Database.

participants478 044 live births were included with sociodemographic and medical records for both women and infants. OUTCOME MEASURES: The incidence rate of preterm birth stratified by different maternal and infant characteristics. Multiple logistic regression was used to identify significant risk factors associated with preterm birth. The population attributable risk fraction of each factor was calculated to estimate its contribution to variations of preterm birth rate over the 10 years.

resultsA total of 27 829 preterm births from 478 044 (5.8%) live births were recorded and the preterm birth rate increased from 5.5% in 2009 to 6.2% in 2018. Medically induced preterm birth rate increased from 2.0% in 2009 to 3.4% in 2018 while spontaneous preterm labour rate decreased from 3.3% to 2.7% over the decade years. Risk factors including multiple pregnancy (0.28% increase) drove the rise of preterm birth rate, whereas changes in maternal educational attainment (0.22% reduction) and prenatal care utilisation (0.45% reduction) had contributed to the decline in preterm birth rate.

conclusionsAn uptrend of preterm birth rate was observed in an area under rapid sociodemographic transitions during 2009-2018 and the changes were associated with these sociodemographic transitions. Continued investments in girls' education and prenatal care have the potential of reducing preterm birth rate.

Indexed as

Obstetric Labor, PrematurePremature BirthChinaFemaleHumansInfantInfant, NewbornPregnancyPregnancy, MultipleRetrospective Studieschild protectionepidemiologymaternal medicinepublic health

Identifiers

PMID33082182
PMCPMC7577040
OpenAlexW3094061323

What OpenQuestion holds

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LicenceCC BY-NC
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Registered trials

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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.