Evidence map›Paper›PMID 32475837›Full record

ArticleBMJ open diabetes research & care2020

'Turning the tide' on hyperglycemia in pregnancy: insights from multiscale dynamic simulation modeling.

Louise Freebairn, Jo-An Atkinson, Yang Qin, Christopher J Nolan, Alison L Kent, Paul M Kelly, Luke Penza, Ante Prodan, Anahita Safarishahrbijari, Weicheng Qian and 6 more

Open access · goldAbstract read
In one paragraph

Article in BMJ open diabetes research & care, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 1 pooled it
1.0field-weighted citation impact, top 19% 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

7 citing papers in PubMed, 1 synthesis or guideline pooled it, 10 citations in OpenAlex.

  1. Pooled it
  2. Review
  3. Article
  4. Article
  5. Article
  6. Review
  7. 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

16 authors at 6 institutions in 3 countries.

Louise FreebairnThe Australian Prevention Partnership Centre, Sax Institute, Haymarket, New South Wales, Australia louise.freebairn@act.gov.au.ORCID 0000-0002-7434-870X
Jo-An AtkinsonThe Australian Prevention Partnership Centre, Sax Institute, Haymarket, New South Wales, Australia.ORCID 0000-0002-2380-1092
Yang QinComputational Epidemiology and Public Health Informatics Laboratory, University of Saskatchewan, Saskatoon, Saskatchewan, Canada.
Christopher J NolanEndocrinology and Diabetes, ACT Health, Woden, Australian Capital Territory, Australia.ORCID 0000-0002-6964-3819
Alison L KentMedical School, College of Health and Medicine, Australian National University, Canberra, Australian Capital Territory, Australia.
Paul M KellyPopulation Health, ACT Health, Woden, Australian Capital Territory, Australia.
Luke PenzaSchool of Computer, Data and Mathematical Sciences, Western Sydney University, Penrith, New South Wales, Australia.
Ante ProdanSchool of Computer, Data and Mathematical Sciences, Western Sydney University, Penrith, New South Wales, Australia.ORCID 0000-0001-9684-7967
Anahita SafarishahrbijariComputational Epidemiology and Public Health Informatics Laboratory, University of Saskatchewan, Saskatoon, Saskatchewan, Canada.
Weicheng QianComputational Epidemiology and Public Health Informatics Laboratory, University of Saskatchewan, Saskatoon, Saskatchewan, Canada.
Louise Maple-BrownWellbeing and Preventable Chronic Diseases Division, Menzies School of Health Research, Charles Darwin University, Casuarina, Northern Territory, Australia.
Roland DyckDepartment of Medicine, University of Saskatchewan College of Medicine, Saskatoon, Saskatchewan, Canada.
Allen McLeanComputational Epidemiology and Public Health Informatics Laboratory, University of Saskatchewan, Saskatoon, Saskatchewan, Canada.ORCID 0000-0002-1344-3949
Geoff McDonnellThe Australian Prevention Partnership Centre, Sax Institute, Haymarket, New South Wales, Australia.
Nathaniel D OsgoodComputational Epidemiology and Public Health Informatics Laboratory, University of Saskatchewan, Saskatoon, Saskatchewan, Canada.
Diabetes in Pregnancy Modelling Consortium
University of Saskatchewan · CAAustralian National University · AUSax Institute · AUWestern Sydney University · AUCharles Darwin University · AUThe University of Sydney · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionHyperglycemia in pregnancy (HIP, including gestational diabetes and pre-existing type 1 and type 2 diabetes) is increasing, with associated risks to the health of women and their babies. Strategies to manage and prevent this condition are contested. Dynamic simulation models (DSM) can test policy and program scenarios before implementation in the real world. This paper reports the development and use of an advanced DSM exploring the impact of maternal weight status interventions on incidence of HIP.

methodsA consortium of experts collaboratively developed a hybrid DSM of HIP, comprising system dynamics, agent-based and discrete event model components. The structure and parameterization drew on a range of evidence and data sources. Scenarios comparing population-level and targeted prevention interventions were simulated from 2018 to identify the intervention combination that would deliver the greatest impact.

resultsPopulation interventions promoting weight loss in early adulthood were found to be effective, reducing the population incidence of HIP by 17.3% by 2030 (baseline ('business as usual' scenario)=16.1%, 95% CI 15.8 to 16.4; population intervention=13.3%, 95% CI 13.0 to 13.6), more than targeted prepregnancy (5.2% reduction; incidence=15.3%, 95% CI 15.0 to 15.6) and interpregnancy (4.2% reduction; incidence=15.5%, 95% CI 15.2 to 15.8) interventions. Combining targeted interventions for high-risk groups with population interventions promoting healthy weight was most effective in reducing HIP incidence (28.8% reduction by 2030; incidence=11.5, 95% CI 11.2 to 11.8). Scenarios exploring the effect of childhood weight status on entry to adulthood demonstrated significant impact in the selected outcome measure for glycemic regulation, insulin sensitivity in the short term and HIP in the long term. DISCUSSION: Population-level weight reduction interventions will be necessary to 'turn the tide' on HIP. Weight reduction interventions targeting high-risk individuals, while beneficial for those individuals, did not significantly impact forecasted HIP incidence rates. The importance of maintaining interventions promoting healthy weight in childhood was demonstrated.

Indexed as

Diabetes, GestationalDiabetes Mellitus, Type 2HyperglycemiaInsulin ResistanceAdultBody WeightFemaleHumansPregnancycausal modelinggestational diabetes mellitusmodelingpopulation health

Identifiers

PMID32475837
PMCPMC7265040
OpenAlexW3028953962

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.