Exploring the impact of multimorbidity onset and treatment timing on work absence: causal analysis using longitudinal data
Supervisors:
Dr Atul Anand, Centre for Cardiovascular Science (University of Edinburgh)
Dr Clare MacRae, Usher Institute (University of Edinburgh)
Prof Nazir Lone, Usher Institute (University of Edinburgh)
Summary:
The rise of long-term conditions and multimorbidity in working-age populations represents a growing public health and economic concern. Sickness absence and unemployment have long-term consequences not only for income but also for future health. Yet, we do not clearly understand how multimorbidity is associated with time lost from work, nor how the timing of diagnosis, or specialist treatment shapes that impact. It is likely that this burden is not equally distributed across the UK population, and understanding the relationship between health and work, including the intersectionality of these impacts, is essential for designing interventions that promote health equity and workforce participation.
Using rich, linked health and administrative data from NHS Lothian (Scotland) and Bradford (England), the student will develop data science methods to identify untreated and/or active disease and apply advanced causal inference techniques to model the impact on employment outcomes. A key focus will be understanding how this burden varies by long-term condition, sex, age, income, and other demographic factors - helping identify opportunities for targeted policy intervention. The student will gain experience working with complex, longitudinal datasets, and develop advanced skills in population health research including causal inference.