Life course epidemiology: From the Bradford Hill viewpoints to counterfactual comparisons
In conjunction with the Royal Statistical Society, the London School of Hygiene & Tropical Medicine is pleased to host Professor Bianca De Stavola, who will deliver the 30th Bradford Hill Memorial Lecture.
Many acute illnesses and other chronic or recurring impairing conditions appearing in later life are often shaped by developmental processes experienced in utero, childhood, adolescence or early adulthood. The primary contribution of a life course approach to investigating these mechanisms is not only the extended longitudinal perspective, but also the incorporation of physical, psychological and social factors in its investigations. Such an approach matters for informing public health policy interventions.
In this talk, Professor Bianca De Stavola will discuss how life course investigations may characterise the impact of exposures over time (e.g., continuous or time-specific) and across dimensions (e.g., via joint or distinct pathways), and how different study designs may aid them. She will also compare ‘traditional’ approaches to gather evidence for causality and potential areas of intervention with the formality and clarity of counterfactual-based estimands.
Bianca De Stavola is Professor of Medical Statistics at UCL Great Ormond Street Institute of Child Health. She joined UCL in 2017 after 23 years at the London School of Hygiene and Tropical Medicine where she was co-Director of the Centre for Statistical Methodology.
Bianca received her PhD from Imperial College London and an MSc from the London School of Economics and Political Sciences, after graduating in Statistical and Economic Sciences at the University of Padova (Italy). She is joint Editor of the Journal of the Royal Statistical Society, Series A, and has just been awarded the RSS Bradford Hill Medal for Medical Statistics. Her main research activities involve the understanding, development, and implementation of statistical methods for longitudinal studies, including linked routine data.