This project aims to examine the best ways in which to inform a local and national debate about how we can increase public support for the most effective and equitable dietary and active travel health-behaviour change policies and interventions. The overarching aim is to explore the development of an emerging framework to predict public acceptability / support for various policy options aimed at improving population health.
To date there is limited knowledge on what policies are acceptable to whom and why. In particular, the limited evidence base that exists does not speak to the potential differential support for different policies amongst people from different socio-economic groups, or how acceptability varies amongst vulnerable populations (incl. those with adverse mental health). Furthermore, there is limited activity to engage the population and to create a public debate about options to protect population health with the aim to build support for more health promoting and protecting policies. Even less is known on public views about utilising non-health department powers such as transport planning and transforming food systems in the population, mainly due to research in this area not focusing on health outcomes (but, rather stakeholder engagement with urban planning, regeneration programmes and similar).
Using a variety of mixed-methods (i.e., a scoping review, qualitative interviews, quantitative pilot studies to inform the creation of Discrete Choice Experiments [DCEs], and Discrete Choice Experiments [DCEs]) the project will examine different policies (e.g., taxes, bans on sales of high fat and high sugar snacks at checkouts, calorie labelling, etc.) pertaining to food and transport (in particular active travel) to model what features of the different policies the public supports, and importantly how such support may vary between members of different demographic groups. At the end of the project we will consider the learnings that arise from the different work-packages to develop an emerging framework that could in future be used to predict public acceptability / support for various policy options aimed at improving population health.