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‘Turning the tide’ on hyperglycemia in pregnancy: insights from multiscale dynamic simulation modeling
  1. Louise Freebairn1,2,3,
  2. Jo-an Atkinson1,4,
  3. Yang Qin5,
  4. Christopher J Nolan6,7,
  5. Alison L Kent7,8,
  6. Paul M Kelly3,7,
  7. Luke Penza9,
  8. Ante Prodan9,
  9. Anahita Safarishahrbijari5,
  10. Weicheng Qian5,
  11. Louise Maple-Brown10,11,
  12. Roland Dyck12,
  13. Allen McLean5,
  14. Geoff McDonnell1,
  15. Nathaniel D Osgood5
  16. Diabetes in Pregnancy Modelling Consortium
    1. 1The Australian Prevention Partnership Centre, Sax Institute, Haymarket, New South Wales, Australia
    2. 2School of Medicine, The University of Notre Dame Australia, Darlinghurst, New South Wales, Australia
    3. 3Population Health, ACT Health, Woden, Australian Capital Territory, Australia
    4. 4Brain and Mind Centre, University of Sydney, Sydney, New South Wales, Australia
    5. 5Computational Epidemiology and Public Health Informatics Laboratory, University of Saskatchewan, Saskatoon, Saskatchewan, Canada
    6. 6Endocrinology and Diabetes, ACT Health, Woden, Australian Capital Territory, Australia
    7. 7Medical School, College of Health and Medicine, Australian National University, Canberra, Australian Capital Territory, Australia
    8. 8Golisano Children’s Hospital at URMC, University of Rochester, Rochester, New York, USA
    9. 9School of Computer, Data and Mathematical Sciences, Western Sydney University, Penrith, New South Wales, Australia
    10. 10Wellbeing and Preventable Chronic Diseases Division, Menzies School of Health Research, Charles Darwin University, Casuarina, Northern Territory, Australia
    11. 11Endocrinology Department, Royal Darwin Hospital, Casuarina, Northern Territory, Australia
    12. 12Department of Medicine, University of Saskatchewan College of Medicine, Saskatoon, Saskatchewan, Canada
    1. Correspondence to Dr Louise Freebairn; louise.freebairn{at}


    Introduction Hyperglycemia 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.

    Methods A 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.

    Results Population 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.

    • gestational diabetes mellitus
    • modeling
    • causal modeling
    • population health

    This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made. See:

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    • Collaborators Diabetes in Pregnancy Modelling Consortium includes the authors of this paper and Dr Tracey Baker, Ms Lynelle Boisseau, Ms Jacqui Davison, Assoc. Prof. Jeff Flack, Assoc. Prof. Alison Hayes, Ms Eloise O’Donnell, Prof. Michael Peek, Mr Nick Roberts, Prof. David Simmons, Dr Jana Sisnowski, and Ms Christine Whittall.

    • Contributors Study conceptualization and planning: LF, JA, GM, NDO, CJN, ALK and PMK. Model and scenario programming: NDO, WQ, YQ, AS and AM. Supervision of model programming: NDO. Expert contribution to model development: CJN, ALK, LM-B, RD and members of the Diabetes in Pregnancy Modelling Consortium. Model outputs: YQ. Data processing and analysis: LP, LF and AP. Conceptualization of manuscript and writing the first drafts: LF. Writing and editing multiple draft revisions: all named authors.

    • Funding This research was supported by The Australian Prevention Partnership Centre through the NHMRC partnership center grant scheme (grant ID: GNT9100001) with the Australian Government Department of Health, NSW Ministry of Health, ACT Health, HCF, and the HCF Research Foundation. The research was also supported by the Australian Government’s Medical Research Future Fund (MRFF). The MRFF provides funding to support health and medical research and innovation, with the objective of improving the health and well-being of Australians. MRFF funding has been provided to The Australian Prevention Partnership Centre under the MRFF Boosting Preventive Health Research Program. Further information on the MRFF is available at The content of this paper is solely the responsibility of the individual authors and does not reflect the views of the NHMRC or funding partners. LM-B was supported by an NHMRC Practitioner Fellowship (#1078477) to collaborate on this project. The University of Notre Dame has provided the following financial support for this case study: Australian Postgraduate Award scholarship and CRN top-up scholarship for supervision travel expenses.

    • Competing interests PMK, LF, CJN and ALK were employees of ACT Health at the time of this study.

    • Patient consent for publication Not required.

    • Provenance and peer review Not commissioned; externally peer reviewed.

    • Data availability statement All data relevant to the study are included in the article or uploaded as supplementary information.

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