Simulation modelling as a tool for knowledge mobilisation in health policy settings: a case study protocol

Abstract

BackgroundEvidence-informed decision-making is essential to ensure that health programs and services are effective and offer value for money; however, barriers to the use of evidence persist. Emerging systems science approaches and advances in technology are providing new methods and tools to facilitate evidence-based decision-making. Simulation modelling offers a unique tool for synthesising and leveraging existing evidence, data and expert local knowledge to examine, in a robust, low risk and low cost way, the likely impact of alternative policy and service provision scenarios. This case study will evaluate participatory simulation modelling to inform the prevention and management of gestational diabetes mellitus (GDM). The risks associated with GDM are well recognised; however, debate remains regarding diagnostic thresholds and whether screening and treatment to reduce maternal glucose levels reduce the associated risks. A diagnosis of GDM may provide a leverage point for multidisciplinary lifestyle modification interventions. This research will apply and evaluate a simulation modelling approach to understand the complex interrelation of factors that drive GDM rates, test options for screening and interventions, and optimise the use of evidence to inform policy and program decision-making.Methods/DesignThe study design will use mixed methods to achieve the objectives. Policy, clinical practice and research experts will work collaboratively to develop, test and validate a simulation model of GDM in the Australian Capital Territory (ACT). The model will be applied to support evidence-informed policy dialogues with diverse stakeholders for the management of GDM in the ACT. Qualitative methods will be used to evaluate simulation modelling as an evidence synthesis tool to support evidence-based decision-making. Interviews and analysis of workshop recordings will focus on the participants’ engagement in the modelling process; perceived value of the participatory process, perceived commitment, influence and confidence of stakeholders in implementing policy and program decisions identified in the modelling process; and the impact of the process in terms of policy and program change.DiscussionThe study will generate empirical evidence on the feasibility and potential value of simulation modelling to support knowledge mobilisation and consensus building in health settings.

Partnering to prevent chronic disease: reflections and achievements from The Australian Prevention Partnership Centre

“…We have developed six models in collaboration with policy makers to examine the impacts of prevention programs addressing alcohol harms, childhood overweight and obesity, tobacco control regulations, chronic obstructive pulmonary disorder and gestational diabetes. [8][9][10][11][12][13] We undertook the first systematic application of liveability indicators to identify which built environments optimise health and wellbeing, and produced the first baseline measure of liveability in Australia's state and territory capitals. We are currently developing a national liveability indicator platform for use by our stakeholders.…”

Impacts of licensed premises trading hour policies on alcohol‐related harms

“…However, the primary purpose of the model was to provide decision support capability by estimating the overall comparative impacts of different policy scenarios over time against the baseline rather than providing highly precise estimates of outcome indicators. A strength of the study, and a key innovation in the application of simulation models to public health settings, is the explicit engagement of key stakeholders in the design and development of the simulation modelling tool [16,[19][20][21]. The participatory approach assisted with transparent negotiation and consensus building around the most acceptable policy options in the context of previous, and sometimes contentious, empirical evidence.…”

“…Computer simulation models are valuable tools for conducting virtual policy experiments before solutions are implemented in the real world [14,15]. They provide a low-risk way of testing assumptions, managing uncertainty, and better understanding the quantitative trade-offs of different policy options to support decision making and knowledge translation [16]. They can be used to conduct experiments not possible in the real world with the aim of identifying the most acceptable and effective policy responses in particular contexts, and importantly, provide a mechanism for estimating the impacts, both beneficial and adverse, of a policy option into the future [17].…”

Knowledge translation facilitating co-creation of evidence in public health

“…While there are many KT approaches and techniques available, the research on evaluation of their use is still in its infancy 45. Rigorous assessment of the value and use of these methods and tools is required prior to them being more generally adopted to support for evidence-based public health decision-making 45.…”

“…While there are many KT approaches and techniques available, the research on evaluation of their use is still in its infancy 45. Rigorous assessment of the value and use of these methods and tools is required prior to them being more generally adopted to support for evidence-based public health decision-making 45. This is particularly necessary given that KT approaches have not demonstrated positive changes48 and their potential failure may be due to the use of a narrow definition of evidence as research evidence only in them 49…”