Speaker
Description
Multimorbidity—the coexistence of multiple chronic diseases in the same individual—is one of the major challenges for modern public health, considering the ageing of population. Understanding how chronic conditions develop, accumulate and interact over the life course requires moving beyond traditional epidemiological analyses based on single outcomes.
This lecture will illustrate how increasingly complex epidemiological questions naturally lead to increasingly sophisticated statistical models. Starting from population-based cohort studies, we will discuss the challenges posed by multiple correlated outcomes, competing events and disease trajectories over time. These problems motivate the use of methods such as competing-risk models and multistate models, which provide a more realistic description of disease progression.
The second part of the lecture will focus on causal inference. In particular, we will discuss how causal mediation analysis can be used to investigate the mechanisms linking socioeconomic disadvantage to multimorbidity, helping to distinguish association from causation and to identify potential intervention targets.
Rather than providing a technical review of statistical methods, the lecture aims to show how mathematical and statistical modelling can address fundamental questions in epidemiology and contribute to understanding the complex pathways leading to chronic disease.