Gestational diabetes through a systems science lens
Findings Brief: Summary of the project's key findings
This project is now finished. A summary of the project’s key findings are in the Findings Brief below.
Project title: Simulation modelling to support decision making in gestational diabetes care
Start date: April 2015
End date: July 2019
What is the issue?
This PhD project tackled the growing problem of diabetes in pregnancy against the backdrop of increasing interest in systems science methods to examine complex problems.
Health systems are under continual pressure to provide accessible and effective health services with limited budgets. In this context, decisions regarding the best investment of health funds need to be well informed, reviewed regularly and aimed at achieving the greatest health gain for the investment.
The development and testing of methods and tools to inform decision-making processes is critical. The application of systems science and simulation modelling to the decision-making process is an innovative area with great potential value for those responsible for allocating resources.
Diabetes in pregnancy includes diabetes that develops, or is first diagnosed, during pregnancy and pre-existing type 1 and type 2 diabetes. Babies born to women who have diabetes during pregnancy are at short-term risk of high birthweight, birth complications and hypoglycaemia and at long-term risk of sustained impaired glucose tolerance. Women who have gestational diabetes are at higher risk of developing type 2 diabetes later in life.
Diabetes in pregnancy is increasing, and this is challenging the capacity of diabetes services. The increase is associated with an increase in risk factors such as overweight and obesity, older maternal age and increasing numbers of women from high-risk groups. New guidelines introduced in 2015 recommend that women who are high risk of developing diabetes in pregnancy should be screened in the first trimester. Consequently, these women are diagnosed with diabetes earlier in their pregnancy and need services for longer. Also, women are more frequently presenting with a combination of risk factors resulting in more complex care needs.
This all means that diabetes in pregnancy is having a significant impact on health service demand and resources, and the need to “do things differently”.
How did the project address the issue?
This project applied systems science and simulation modelling to the problem of diabetes in pregnancy in the ACT as a case study.
Systems science is emerging as an effective way to examine both complex health problems and their context. It can be used to synthesise evidence, examine and compare the potential outcomes of interventions and guide the best use of limited resources through methods such as simulation modelling.
The project used case study methodology to illustrate the strengths and weaknesses of simulation modelling as a tool to inform policy and program decision making.
Simulation modelling is being used to explore strategies for gestational diabetes diagnosis, early intervention and management. The modelling includes interaction between risk factors, the short-and long-term outcomes for mother and baby, and potential modes and timing of intervention.
Involving key decision-makers and experts in the model development and validation process increases the validity of the model for the local context. The model is therefore more likely to be useful to inform decisions about priority interventions and policies.
Relevance for practice
The project evaluated the effectiveness of participatory dynamic simulation modelling to inform program and policy decision-making. It produced a simulation model for diabetes in pregnancy that will inform investments for intervention in diabetes in pregnancy. The model considers the short-, intermediate- and long-term implications of the increasing prevalence of risk factors for diabetes in pregnancy.
What were the expected outcomes?
- Produced a model that will be a functioning simulation tool to explore possible scenarios and the likely impact over time of each scenario on health outcomes for the mother and baby as well as service impacts for the health system
- Engaged with the stakeholder group to use the model to inform decision-making
- Evaluated the use of participatory dynamic simulation modelling as a decision-support tool.
Louise Freebairn, PhD Candidate, Prevention Centre, ACT Health
- Associate Professor Jo-An Atkinson, Prevention Centre
- Professor Roland Dyck, University of Saskatchewan
- Dr Paul Kelly, ACT Health
- Professor Alison Kent, ANU Medical School, ACT Health
- Dr Geoff McDonnell, Prevention Centre
- Professor Christopher Nolan, ANU Medical School, ACT Health
- Professor Nate Osgood, University of Saskatchewan
- Professor Lucie Rychetnik, Sax Institute and Prevention Centre
This project was funded by the NHMRC, Australian Government Department of Health, NSW Ministry of Health, ACT Health and the HCF Research Foundation.
- Internationally recognised experts in neonatology, diabetes, obesity, population health interventions, health economics and simulation modelling participated in a group model building process that started in May 2016
- A series of workshops was held. Participants’ expert knowledge was used to map the complex problem of diabetes in pregnancy, to identify relevant literature and data sources, and to help quantify and verify the model
- The model was finalised in collaboration with sub-groups of participants focusing on evidence review and scenario development
- A key achievement for this project was the engagement of stakeholders. Almost all the experts invited to participate in the model development process agreed to do so and remain actively engaged.
- The model developed as part of this project represents a technologically advanced dynamic simulation model that includes interventions spanning the spectrum from clinical care to population health.
- Turning the tide on hyperglycemia in pregnancy | July 2020 | Findings from Dr Louise Freebairn’s Prevention Centre-funded University of Notre Dame PhD project Simulation modelling to support decision making in gestational diabetes care.
- Freebairn L, Atkinson JA, Qin Y, Nolan C, Kent A L, et al. ‘Turning the tide’ on hyperglycemia in pregnancy: Insights from multi-scale dynamic simulation modelling. BMJ Open Diabetes Research & Care.
- Freebairn L, Atkinson JA, Osgood ND, Kelly P, McDonnell G, Rychetnik L. Turning conceptual systems maps into dynamic simulation models: An Australian case study for diabetes in pregnancy. PLOS One. Online. 2019. doi: 10.1371/journal.pone.0218875
- Freebairn L, Atkinson JA, Kelly P, McDonnell G, Rychetnik L. Decision makers’ experience of participatory dynamic simulation modelling: methods for public health policy. BMC Med Inform Decis. 2018; doi.org/10.1186/s12911-018-0707-6
- Atkinson J, O’Donnell E, Wiggers J, McDonnell G, Mitchell J, Freebairn L, Indig D, Rychetnik L. Dynamic simulation modelling of policy responses to reduce alcohol-related harms: rationale and procedure for a participatory approach. Public Health Res Pract. 2017;27(1):e2711707.
- Freebairn L, Atkinson J, Kelly P, McDonnell G, Rychetnik L. Simulation modelling as a tool for knowledge mobilisation in health policy settings: a case study protocol Health Res Policy Syst 2016;14:71 doi: 10.1186/s12961-016-0143-y
- Freebairn L, Rychetnik L, Atkinson J, Kelly P, McDonnell G, Roberts N, Whittall C, Redman S. Knowledge mobilisation for policy development: implementing systems approaches through participatory dynamic simulation modelling. Health Res Policy Syst. 2017;15:83 doi: 10.1186/s12961-017-0245-1
- O’Donnell E, Atkinson JA, Freebairn L, Rychetnik L. Participatory simulation modelling to inform public health policy and practice: Rethinking the evidence hierarchies. J Public Health Policy. 2017; May;38(2):203–215. doi: 10.1057/s41271-016-0061-9.
- Freebairn L. Simulation Modelling to Inform Decision Making for Diabetes in Pregnancy in the ACT. International Congress on Modelling and Simulation, Hobart, December, 2017.
- Freebairn L. Simulation modelling as a tool for knowledge translation in health policy settings: a case study protocol. School of Medicine, University of Notre Dame, Research Conference 2016. 23 March 2016. Sydney.
- Freebairn L. Simulation modelling: A systems approach to optimising the use of evidence to inform decision making in gestational diabetes care MODISM – 21st International Congress on Modelling and Simulation. 29 November 2015. Gold Coast.
- Freebairn L. Gestational diabetes through a systems science lens. Centre for Health Stewardship Research Group. Australian National University. 19 November 2015. Canberra.
- Prevention Centre news, June 2016: Simulation modelling helps to unpick causes of gestational diabetes
- Prevention Centre news, May 2017: Project expanded to tackle all forms of diabetes in pregnancy
- Freebairn L and Kelly P. Harnessing new technologies to inform health decision making: Dynamic simulation modelling as a decision support tool for diabetes in pregnancy. ACT Population Health Bulletin. Volume 6, Issue 2, May 2017, p, 31-32.