Travel-time and Geospatial Analysis to support the implementation of Community Diagnostic Centres in Manchester and Trafford

Introduction

Manchester University NHS Foundation Trust (MFT) commissioned the NHS Transformation Unit (TU) to support the implementation of community diagnostic services across Manchester and Trafford. Whilst neighbouring localities, the demographic profiles look quite different. Manchester is more densely populated and ethnically diverse, whilst Trafford is generally one of the more affluent areas in Greater Manchester but has significant variation in deprivation between the North and South. Working on behalf of system partners, our goal was not only to improve access to diagnostics, but to ensure that services were designed and located to reduce disparities in health outcomes across the region.

MFT asked us to provide strategic analysis to support the implementation, including travel-time modelling to assess site accessibility and geospatial analysis to understand how deprivation and disease prevalence intersect across local communities. The aim of this work was to ensure that diagnostic services reached the people who needed them most, particularly those in underserved or disadvantaged areas.

The Challenge

The challenge was to ensure that new community diagnostic services were accessible and actively addressing health inequalities across Manchester and Trafford. Many communities in these areas experience poor health outcomes, limited access to care, and significant structural barriers such as poor transport links, lower health literacy, and higher levels of deprivation.

To ensure that investment in diagnostic services would have the greatest possible impact, MFT needed clear insight into which populations were underserved and how potential site locations could either reinforce or reduce existing inequalities.

Our Approach

To ensure accessibility and equity were prioritised in the planning process, we began by mapping the proposed community diagnostic centre and spoke site locations within the NHS Manchester and Trafford Places.

The proposed community diagnostic centres were North Manchester CDC and Withington CDC. The proposed spoke site locations were Brownley Green Health Centre, Clayton Health Centre, Limelight Health Centre, Partington Health Centre and The Vallance Health Centre.

A map to show overall coverage between proposed CDC hub and spoke sites in a 30-minute period as calculated by travel-time analysis

Using public transport data, we modelled 30-minute travel zones for each site to identify communities at risk of exclusion due to poor connectivity. For each of the potential spoke sites, we analysed the average travel times across Manchester and Trafford during peak (shaded in blue on the above map) and off-peak times (shaded in red on the map above), with the grey shading to indicate both periods. We layered this analysis with detailed social deprivation data at LSOA (Lower-layer Super Output Areas) level, using colour-coded maps to visualise the areas of greatest need.

A map to show the overall coverage of proposed CDC hub and spoke sites compared against social deprivation.

To deepen our understanding of health inequalities in this geography, we linked Public Health England (PHE) Fingertips disease prevalence data to local GP practices. This allowed us to map how health conditions (such as respiratory disease, cardiovascular conditions, and cancer) correlate with areas of high deprivation. By plotting these data sets alongside proposed diagnostic locations, we helped MFT identify potential gaps where access should be prioritised to meet the demand in high-need communities.

Our visual outputs and findings were presented in a clear, accessible format to support transparent, evidence-based decision-making.

The Outcome

Our geospatial analysis played a critical role in shaping the location strategy for new diagnostic centres and spoke sites across Manchester and Trafford. The insights were shared with MFT to inform their site selection process and ensure that services were strategically placed to serve those communities most affected by health inequalities. Through identifying and accounting for structural barriers to access, such as travel time and public transport links, the service model aimed to shorten diagnostic pathways for those who often face the longest waits and worst outcomes.

As part of the broader programme supported by the NHS TU, our work contributed to the delivery of over 95,000 additional diagnostic tests in 2022–23. This overperformed against our activity plan by more than 25%. More importantly, these additional tests were delivered with equity in mind, reaching patients who previously might have faced delays due to where they live, their transport options, or the socio-economic challenges they face.

Next Steps

Addressing health inequalities is essential to building a fair and effective health system. Our work with MFT demonstrates the benefits of using geospatial intelligence to inform equitable service planning and data-driven decision making. Ensuring faster and fairer access to diagnostic services contributes to early intervention, better outcomes and reduced pressure on acute services.

We believe that to make the right decisions regarding healthcare services, they must be supported by the appropriate data insights. If you’re planning diagnostic services, community hubs, or place-based interventions, and want to ensure they are both accessible and equitable, we would be happy to help.  We have a range of experience across analytics and modelling, including geospatial mapping, dashboard development, health inequalities analysis, and demand modelling.