Orienting Policy Towards Inequality Minimising Actions (OPTIMA)
A systems science approach to 20-minute neighbourhood policy and evaluation, is an NIHR Public Health Research (PHR) Programme-funded project.
About the Project
The OPTIMA project aims to investigate the true impact of 20-minute neighbourhoods, their benefits and how they may also contribute to greater health inequalities across our communities.
How the Project Works
The OPTIMA project recruits the use of an agent-based model (ABM) to help simulate how various policy changes may impact behaviour and interaction in neighbourhoods
Get in Touch
For more information, ways to collaborate or for any questions you may have about the OPTIMA project, please don't hestitate to contact us via email
What is a 20-minute neighbourhood?
In a 20-minute neighbourhood (20MN), local communities can easily get to daily services like stores offering healthy produce, schools, healthcare, and parks within a short walk or bike ride from their homes (20-minute return trip). There is an ambition to implement these across Scotland, and other parts of the UK and internationally.
There are claims that these will reduce health inequalities, but evidence to support this claim is lacking. There is also a real chance that health inequalities might get worse – for example, if prices increase and the poorest are left behind, or there is increased access to unhealthy products or services, like those providing alcohol or gambling.
Helping Reduce Health Inequalities
We are going to provide evidence to make better decisions possible to reduce health inequalities, before transformational costs are fully committed. We will:
- Look at what is already known about how bringing services closer to people affects health and inequalities and co-produce evidence with the community and with decision-makers.
-
Put together data on where services currently are in Scotland and who lives there.
-
Put all this knowledge and data together in a computer model of how things might change over time.
-
Use real data and changes that have already happened to plan more tests and further improve the evidence.