Abstract
BackgroundSystems science methods such as dynamic simulation modelling are well suited to address questions about public health policy as they consider the complexity, context and dynamic nature of system-wide behaviours. Advances in technology have led to increased accessibility and interest in systems methods to address complex health policy issues. However, the involvement of policy decision makers in health-related simulation model development has been lacking. Where end-users have been included, there has been limited examination of their experience of the participatory modelling process and their views about the utility of the findings. This paper reports the experience of end-user decision makers, including senior public health policy makers and health service providers, who participated in three participatory simulation modelling for health policy case studies (alcohol related harm, childhood obesity prevention, diabetes in pregnancy), and their perceptions of the value and efficacy of this method in an applied health sector context.MethodsSemi-structured interviews were conducted with end-user participants from three participatory simulation modelling case studies in Australian real-world policy settings. Interviewees were employees of government agencies with jurisdiction over policy and program decisions and were purposively selected to include perspectives at different stages of model development.ResultsThe ‘co-production’ aspect of the participatory approach was highly valued. It was reported as an essential component of building understanding of the modelling process, and thus trust in the model and its outputs as a decision-support tool. The unique benefits of simulation modelling included its capacity to explore interactions of risk factors and combined interventions, and the impact of scaling up interventions. Participants also valued simulating new interventions prior to implementation in the real world, and the comprehensive mapping of evidence and its gaps to prioritise future research. The participatory aspect of simulation modelling was time and resource intensive and therefore most suited to high priority complex topics with contested options for intervening.ConclusionThese findings highlight the value of a participatory approach to dynamic simulation modelling to support its utility in applied health policy settings.
Highlights
Systems science methods such as dynamic simulation modelling are well suited to address questions about public health policy as they consider the complexity, context and dynamic nature of system-wide behaviours
Pre-modelling perceptions of evidence use in decision making Prior to the commencement of the participatory modelling process, respondents consistently emphasised the importance of evidence-informed decision making they identified challenges relating to the availability, applicability, persuasiveness, timeliness and accessibility of evidence to inform decision making
The unique benefits of the models included the ability to explore the interaction of risk factors and causal mechanisms; the interaction and combination of public health interventions; the impact of scaling up the reach and effectiveness of existing programs and the impact of new and untested interventions in simulation before they were implemented in the real world
Summary
This paper reports the experience of end-user decision makers, including senior public health policy makers and health service providers, who participated in three participatory simulation modelling for health policy case studies (alcohol related harm, childhood obesity prevention, diabetes in pregnancy), and their perceptions of the value and efficacy of this method in an applied health sector context. Policy and program decision making processes are frequently non-linear and iterative. They are influenced by a range of factors, that compete with research evidence, such as the political environment, budget and resource constraints, and public perceptions of the value of policy options being considered, [1,2,3,4,5,6]. Policy makers require synthesised and contextualised evidence that establishes the need for a policy response, compares and prioritises policy options, and demonstrates cost-effectiveness of interventions [4, 9]
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