Non-Communicable Disease Epidemiology

The Non-communicable Disease Epidemiology Research Group is led by Sarah Wild and Caroline Jackson. Their research covers the use of quantitative methods to investigate the causes and consequences of non-infectious diseases such as heart disease and mental illness.

Research in a Nutshell

The groups multidisciplinary research primarily uses linked electronic health data and large cohort studies to describe the causes and consequences of long-term conditions, the interplay between health conditions and the role of health inequalities. Whilst the group’s research interests cover a range of conditions, they have a particular focus on diabetes, cardiovascular disease and the interplay between mental illness and physical health. Their research projects employ a range of epidemiological methods and study designs. The group work closely with a wide range of professional fields, including the following:

Epidemiologists
Statisticians
Data scientists
Social scientists
Various health professionals
Policymakers

Key People

Name	Role
Sarah Wild	Chronic Disease Epidemiology Research Group Co-Lead \| Professor of Epidemiology
Caroline Jackson	Chronic Disease Epidemiology Research Group Co-Lead \| Senior Lecturer
Kelly Fleetwood	Statistician
Regina Prigge	Lecturer in Epidemiology
Evropi Theeodoratou	Professor of Cancer Epidemiology and Global Health
Nazir Lone	Professor of Critical Care and Epidemiology
Dorien Kimenai	Research Fellow

Themes and Keywords

Scientific Themes

Cardiovascular Disease; Diabetes; Health Inequalities; Mental Illness; Metabolic Disease

Methodology Keywords

Cohort Studies; Quantitative; Record Linkage; Routine Data

Projects

The Hub for Metabolic Psychiatry is one of six new research hubs forming the basis of the UKRI mental health research platform, established to accelerate progress towards novel and more effective treatments for SMI. The Hub is comprised of a network of universities with a long-standing interest in the interface between mental and physical health.

Metabolic Psychiatry Hub

Metabolic Psychiatry Hub - Health Informatics and Data Science

This project aims to develop a set of tools for optimising health datasets and supporting AI development in ensuring equity. Central to the solution is a novel measurement tool for quantifying health inequalities: deterioration-allocation area under curve. This framework assess the fairness by checking whether the AI allocate the same level of resources for people with the same health needs across different groups. Specifically, this project will conduct three lines of work:

Analyse the embedded racial bias in three heath datasets so AI developers can make informed decisions and selections on how to characterise patients and what to predict
Systematically review and analyse risk prediction models, particularly those widely used in clinical settings, for COVID-19 and type 2 diabetes
Develop a novel method called multi-objective ensemble to bring insights from complementary datasets (avoiding actual data transfer) for mitigating inequality caused by too little data for certain groups.

QMIA Grant Summary