Data Bytes 3: Health & Economy

The 3rd instalment of our data science mini-series showcasing current research projects carried out by the Data Scientist Development Programme at Leeds Institute for Data Analytics (LIDA).

This week’s features 10-minute lightning talks from three data scientists on using data analytics to AI & Automation.

Each talk will last 10mins + 10mins Q&A.

AGENDA

Talk 1 - How Arthritis Shapes Healthcare Utilisation in Patients with Multimorbidity (Emma Briggs)

Talk Summary: Musculoskeletal disorders are highly prevalent in the UK, yet their impact on multimorbidity is often overlooked. Examining this relationship, we present a project conducted in partnership with the NHS West Yorkshire Integrated Care Board. We describe the use of Latent Class Analysis to cluster commonly co-occurring diseases, before exploring emerging variations in healthcare utilisation. We discuss how the Leeds Data Model can offer insights for population health management spanning primary care, hospital services, specialist clinics, and emergency helplines. Findings could help streamline health service planning and transform care pathways to ultimately reduce inequalities for patients with multimorbidity and musculoskeletal disorders.

Biography: Emma Briggs is a data scientist specialising in the health and population domain. She holds a BSc in Maths and Computer Science and MMedSci/MSc in Health Informatics, with a PhD in AI for Early Cancer Diagnosis forthcoming. Her interests include disease detection, generative modelling, and data science for equitable futures.

Talk 2 - DIO Food: Mapping different measures of equality and assessing the fairness of food policy across communities (Molly Sargent)

Talk Summary: This project examines how measures capture social and food‑related inequalities across UK communities. As part of the DIO‑Food project, in partnership with the Institute of Grocery Distribution, I developed an interactive tool comparing Index of Multiple Deprivation and Priority Places for Food Index to show where indicators align and diverge in identifying areas facing disadvantage. This work supports the second phase using supermarket sales data from major UK retailers to assess whether impacts of HFSS legislation were equitable across deprivation levels. Collectively, these outputs provide data‑driven evidence for selecting measures to assess inequalities in food access and policy impacts.

Biography: Molly is a data scientist with a background in Sociology and Urban Data Science. She is passionate about using data to understand social inequalities and support meaningful public impact. She enjoys translating complex, messy data into accessible insights that help inform fairer policy and improve how evidence is communicated.

Talk 3 - Developing a geospatial foundation model for mobility (Emeka Enechukwu)

Talk Summary - The cost of energy consumption in the United Kingdom is influenced by the availability and flexibility of households towards energy-using activities. While the implementation of the market-wide half hourly settlement reform allows energy suppliers to apply dynamic tariffs that benefit households who can shift their energy-consuming activities to non-peak hours, understanding how this impacts the different household types would enable policymakers to ensure inclusive energy reform. This talk will focus on the analysis of the time use survey and smart meter data from the UK Data Services in identifying households that are vulnerable in the implementation of the settlement reform.

Biography - Emeka's background in chemistry provided him with the basics for quantitative techniques, which has leveraged in contributing to addressing health outcomes. Building on this through his postgraduate training in Data, Inequality, and Society ensures that he provides a unique blend of perspective to interdisciplinary and translational research.

Know what’s Happening Next — before everyone else does.

Get App

Host or Publisher Leeds Institute for Data Analytics