DrConnor Gascoigne

Connor's research sits at the intersection of Bayesian statistical methodology, causal inference, and public health policy evaluation. His current work is focused on quantifying the population health consequences of climate-related policies, with two active lines of research:

• Clean air policy and population health: Conducting the statistical evaluation of the health impacts of London's Ultra Low Emission Zone (ULEZ), combining Bayesian spatiotemporal modelling with state-of-the-art epidemiological exposure modelling to quantify the benefits of air pollution reduction policies.
• Climate policy and the temperature–mortality relationship: Supervising and collaborating on research examining how climate policies and changing climatic conditions modify the relationship between temperature extremes and mortality.

More broadly, Connor's research programme spans:

• Bayesian spatiotemporal modelling for disease mapping and policy evaluation.
• Causal inference methods for evaluating the health effects of government interventions.
• Mental health and welfare policy: Evaluating the population-level mental health impacts of UK government policies, including welfare reform, immigration law, and national suicide prevention strategies.
• Health economics: Extending policy evaluation frameworks to incorporate full health economic assessments of major interventions.
• Global child health: Bayesian methods for estimating under-five mortality in Low- and Middle-Income Countries.

Connor works within a cross-disciplinary network including collaborators at Imperial College London, University College London, the London School of Economics, and the University of Turin.