Overall aim: To study health inequalities associated with stroke using an intersectionality framework and multimodal data
Stroke is a leading cause of death (7 million deaths/year) and disability (>160 million disability-adjusted life-years) worldwide. In the UK, annually ~100,000 people have a stroke causing 38,000 deaths (fourth leading cause of death), with 1.4 million stroke survivors living in the country in 2024. Projections estimate 21.4 million stroke cases, 159.3 million survivors, 12.1 million deaths, and 224.9 million disability‐adjusted life years in 2050. Stroke is a condition of significant and persisting inequalities disproportionately affecting ethnic minority and socioeconomically disadvantaged groups, from risk factors, incidence, access to appropriate healthcare, and post-stroke outcomes including recovery, long-term disability and survival. However, despite this substantial evidence, studies often examine health inequalities in isolation overlooking intersectionality. The conventional ‘single-axis’ approaches focusing on unitary dimensions of inequity (and thereby masking heterogeneity within and across groups) has been found to be limiting in reducing health inequalities. Intersectionality in health policy has been advocated as the most viable approach to tackling health inequity.
This project will use multimodal data (multiple sources and types) to comprehensively study health inequalities associated with stroke (from risk factors and access to healthcare to outcomes and survival – i.e., across the entire continuum). Methods include state-of-the-art developed specifically for inequalities research like the Multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA) and will consider key factors like age, gender, ethnicity, family status and multiple indicators of socioeconomic position. Further, for the first time in the UK, this project will examine stroke related inequalities in sexual and gender minority groups. This project will combine traditional and advanced epidemiological methods and statistical modelling with new technological advances, AI, and using multiple sources and types of data (like stroke registers, electronic health records from general practice, and research studies like the UK Biobank and Our Future Health, and national health surveys).