Evidence map›Paper›PMID 28583101›Full record

ArticleBMC pregnancy and childbirth2017

Healthcare interventions for the prevention and control of gestational diabetes mellitus in China: a scoping review.

Tingting Xu, Yasheng He, Livia Dainelli, Kai Yu, Patrick Detzel, Irma Silva-Zolezzi, Sheri Volger, Hai Fang

Open access · goldAbstract readScoping Review
In one paragraph

Article in BMC pregnancy and childbirth, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
25citing papers in PubMed, 2 pooled it
4.5field-weighted citation impact, top 5% 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

25 citing papers in PubMed, 2 syntheses or guidelines pooled it, 47 citations in OpenAlex.

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  15. Polish Women Have Moderate Knowledge of Gestational Diabetes Mellitus and Breastfeeding Benefits.International journal of environmental research and public health · 2021
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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

8 authors at 4 institutions in 3 countries.

Tingting XuChina Center for Health Development Studies, Peking University, Haidian District, PO Box 505, Beijing, 100191, China.
Yasheng HeChina Center for Health Development Studies, Peking University, Haidian District, PO Box 505, Beijing, 100191, China.
Livia DainelliNestlé Research Center, Lausanne, Switzerland.
Kai YuNestlé Research Center, Beijing, China.
Patrick DetzelNestlé Research Center, Lausanne, Switzerland.
Irma Silva-ZolezziNestlé Research Center, Lausanne, Switzerland.
Sheri VolgerNestlé Nutrition, King of Prussia, PA, USA.
Hai FangChina Center for Health Development Studies, Peking University, Haidian District, PO Box 505, Beijing, 100191, China. hfang@hsc.pku.edu.cn.
Nestlé (Switzerland) · CHPeking University · CNNestlé (China) · CNNestlé (United States) · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundGestational Diabetes Mellitus (GDM) is a type of diabetes which occurs during pregnancy. Women with GDM are at greater risk of complications during pregnancy and delivery, while babies born from mothers with GDM are at greater risk of post-natal complications. Using the most updated diagnosis criteria, the GDM prevalence is estimated at 9.3-25.5% worldwide and 9.3-18.9% in China. Our objective was to identify healthcare interventions aimed at GDM prevention and control in China.

methodsA best-evidence synthesis was performed based on a systematic search of literature published between 1997 and October 2015 in PubMed, Web of Science, China National Knowledge Infrastructure (CNKI), and Wan-fang databases using keywords "Gestational Diabetes Mellitus", "GDM", "Intervention" "Medical Intervention" "Early Medical Intervention", "Dietary Intervention", "Exercise Intervention", "Lifestyle Intervention", "Therapy", "Treatment" and "China". Inclusion criteria were studies conducted in China, reporting GDM healthcare interventions, and published in either Chinese or English. Two reviewers independently assessed eligibility and quality of the studies and extracted the data. Treatment efficacy was examined with weighted pooled odds ratio (OR) meta-analyses.

resultsThe search resulted in 5961 articles (published in 276 different Chinese language journals and 6 English language journals), of which 802 were included in this synthesis. While 39.4% (n = 316) failed to report the GDM diagnostic criteria used, the remaining studies classified GDM with various international (n = 5) or Chinese (n = 7) diagnostic standards. Treatment interventions were categorized into 6 types: dietary (18.6%), exercise (1.6%), medication (20.7%), health education (9.0%), psychological (2.6%) and combination (47.4%). No interventions aimed at GDM prevention were identified. Meta-analyses demonstrated a statistically significant overall benefit of GDM treatment strategies in reducing the odds of maternal and infant adverse outcomes (ORs range 0.20-0.34, 95% CI 0.17-0.49, P < 0.05 for all). Dietary, western medication, and combined interventions were the most effective inteventions.

conclusionsAn increasing number of healthcare interventions were found in China aimed at controlling GDM while no interventions were intended for GDM prevention. Well-designed clinical trials are needed to determine the comparative and cost effectiveness of GDM prevention and treatment strategies.

Indexed as

DietExerciseHealth EducationChinaCombined Modality TherapyDiabetes, GestationalFemaleHealth BehaviorHumansHypoglycemic AgentsPregnancyPregnancy ComplicationsHypoglycemic AgentsChinaGestational diabetes mellitus (GDM)Glycemic controlHealthcareInterventionsPrevention

Identifiers

PMID28583101
PMCPMC5460547
OpenAlexW2622438067

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.