Cross-Cutting Platform 2: Data, Linkages, and Casual Inference
Despite a large evidence base of observational studies indicating strong associations between a range of social determinants with mental health, evaluating the effectiveness of population-level interventions to prevent mental ill-health has proven difficult, as complex interventions designed to address the social determinants of mental health are rarely amenable to randomised controlled trials.
The objective of this cross-cutting platform is to leverage novel large-scale and linked data, and refine state-of-the-art natural experiment and causal inference methods, to evaluate policy, public health or the impact of other large-scale ‘shocks’ on population mental health inequalities.
The UK is a world leader in longitudinal cohorts and repeated cross-sectional surveys with embedded mental health outcomes in large, nationally representative population-based samples. Various candidate data sources will be explored and used, and we will establish the potential for cross-country comparative analyses across challenge area topics.
Collaborators
Professor Ann John (Swansea University), Dr Alex Dregan and Professor Jayati Das-Munshi (King's College London), Professor Gerard Leavey (University of Ulster), THRIVE-LDN Real Time Surveillance System, Greater London Authority City Intelligence Unit
Investigators
Reader in Epidemiology
Professor of Psychiatric and Social Epidemiology, UCL
Senior Research Fellow, UCL
Professor of Health Economics, University of Manchester