Identifying actionable sociodemographic inequities associated with the burden of stroke

Despite significant progress in prevention and treatment, stroke remains a leading cause of death and disability globally. Particularly, the number of strokes is increasing among young people (under 50 years) in Australia and around the world. Many young people who have strokes do not have the usual risk factors, such as high blood pressure, diabetes, or high cholesterol. Therefore, there is still a lot we do not know about why strokes happen in this young population. Little is known about how biological (e.g., ethnicity), socio-demographic (e.g., race, education, marital status, income, remoteness, access to healthcare) and environmental determinants of health (e.g., air pollution, atmospheric temperatures, neighbourhood, built environment) influence the risk and outcomes of young-onset stroke. To fill this important evidence gap, this project will use linked national and international data and machine learning methods to understand how disparities in biological, social, and interactions between health equity and social-demographic determinants of health of stroke. The project sits within the Big Data, Epidemiology, and Prevention Division within the Stroke and Ageing Research (STAR) group, located at the Victorian Heart Hospital, Monash University Main Campus, and is broadly divided into the following phases: Phase 1: Conduct a systematic review of associations between health inequities and biological, social and environmental determinants of health and the risk of young-onset stroke. Phase 2: Analyse linked national data (including health information, demographic, social factors) to comprehensively understand the role of biological, socio-demographic and environmental determinants of health in predicting young-onset stroke in Australia. The student will develop skills and expertise in:  Life-course approach in social epidemiology - longitudinal analysis of health and socio-demographic data to unearth novel risk factors for stroke.  Advanced statistical and machine learning methods - using sophisticated analytical methods to untangle the complex interactions between biological, social and demographic determinants of health.  Health equity and disease prevention: Identify equity issues relevant to disease prevention to inform policy and practice.