Case study: Health and socio-economic modelling using population density

Resource planning for public services, including the NHS, is typically based on per-capita models (relating to each person in a population). The underlying rationale is that larger cities require more resources, whereas smaller cities, towns and rural communities need fewer resources due to their lower population density. This is fundamentally a biased assumption that some researchers believe needs reform.

Instead, imagine basing your resource planning on a far more sophisticated statistical model - one that combines around 150 different indicators, including crime, mortality, ethnicity, age, attainment, economic activity, road accidents, property transaction values, and disabilities – in a single platform that is updated over time.

This is the thinking behind a University of Derby health and socio-economic modelling research project, which will soon provide a new service planning resource for researchers - including clinical researchers - in the East Midlands.

In addition to providing a valuable data resource that combines this publicly available data, this project aims to prompt a wider debate on the most effective methods for planning services to meet the specific needs of an increasing and diverse population in the future.

This asset will be available for approved clinical researchers with access to the East Midlands SDE (Secure Data Environment). The East Midlands SDE is part of a national network of digital platforms designed to provide approved researchers with a single point of access to a wide range of research data, including anonymised healthcare data.

Although the data project was developed in Derby and researchers there – with the support from project collaborators in Brazil - making the data sets available via the East Midlands SDE ensures broader accessibility for other researchers who can benefit from its insights.

What’s the situation with public service resource planning?

The University of Derby’s Dr Jack Sutton, the leading researcher for this SDE project, says that analysis of multiple data sources as part of the project has proven to be highly informative in developing a more nuanced understanding of how to allocate resources for public services.

“With our modelling and dataset, we can drill down to highlight inequalities that might otherwise be hidden and provide insight into the reasons behind them,” Dr Sutton explains.

Unpacking the differences between regions

A considerable number of spatial-temporal datasets from Dr Sutton’s research have revealed that the differences between communities are often more complex than those suggested by simpler - yet well-established - divides like the ‘North-South divide.’

“This research highlights fundamental differences between rural and urban regions, with some indicators increasing in urban areas while others decline. It demonstrates the per-capita models are fundamentally biased and can lead to underestimations of the resources needed to serve a community,” said Dr Sutton, Lecturer in Statistics and Data Science at the University of Derby’s College of Science and Engineering.

“For example, most mortality show a decline in urban regions relative to population density and expectations.

“Much of this can be explained by age demographics, as cities tend to attract younger people, whereas older people often move to rural and coastal areas. I don’t believe this aspect is fully appreciated when it comes to resource planning and allocation,” added Dr Sutton.

“Factors such as ethnicity and attainment are also underappreciated when considering how to properly resource services for a population, which is why our project aims to inform a wider debate on the modelling and data required.”

What does this new research resource offer?

This project is thought to be the largest of its type, featuring a huge data set consisting of middle layer super output areas (MSOAs) for both England and Wales. It combines data from approximately 7,000 MSOAs and over 100 indicators of health, crime and population characteristics, covering the 2015-2022 timeframe.

The data source will help researchers looking for a deeper dive into the factors that cause inequalities in a particular region, providing a narrative about their causes.

Dr Sutton said: “This research can give us a clearer picture of inequality. We’ve been able to identify not just a binary North-South divide, but several distinct clusters.

“Understanding the differing characteristics that define these clusters is crucial.”

He added: “If a region is affected by a certain type of crime, it may also exhibit a high number of other related indicators. For example, we’ve shown that crime is strongly associated with a younger demographic, while most mortalities are closely associated with an older demographic.

“There are also other interconnections we have found that are less obvious. One example is the set of ‘preventable deaths,’ such as lung cancer and liver cancer, which are highly associated with a younger demographic.” This is just one of many examples.

Bringing all the data sources together in one place – rather than researchers searching for the same information on a piecemeal basis – offers a powerful new tool for future investigators. This research and tool will be particularly helpful if we aim to reform the NHS, serving as an excellent starting point.