Research lines
Physical Frailty
Physical frailty is a clinical syndrome characterized by reduced physiological reserves and a diminished ability to cope with stressors, resulting from the cumulative decline of multiple biological systems. Our research focuses on developing frailty measures based on administrative health data, identifying risk factors, and investigating the complex relationship between frailty and multimorbidity.
Healthy Ageing
Healthy ageing is a multidimensional process that extends beyond the absence of disease, encompassing physical, psychological, and social well-being throughout later life. Our research is grounded in close interdisciplinary collaboration with geriatricians, psychologists, public health experts, and other specialists to better understand the determinants of healthy ageing and to promote evidence-based interventions.
Partially Ordered Set (POSET) Theory
Partially Ordered Set (POSET) theory provides a powerful framework for ranking individuals or units based on multiple ordinal and binary variables without relying on arbitrary weighting schemes. Our research has extended POSET methodology to a variety of applications, including multidimensional ranking, treatment balancing, and methodological developments for complex health data.
Multimorbidity
Multimorbidity, defined as the coexistence of two or more chronic diseases in the same individual, is one of the greatest challenges posed by population ageing. Our research investigates multimorbidity patterns, their evolution over time, and the mechanisms through which multiple chronic conditions contribute to the development of frailty using advanced statistical and graphical modelling approaches.
Disease clusters
The identification of geographical clusters of disease and mortality is a key challenge in public health, supporting the detection of spatial inequalities and the planning of targeted interventions. Our research develops innovative statistical methods for identifying disease and mortality clusters while accounting for the complex relationships among different groups of diseases.
Causal inference
Understanding the determinants of health requires moving beyond association towards causal interpretation. Since our research is largely based on observational population data, we develop and apply modern causal inference methods to evaluate the effects of social, environmental, and healthcare factors on health outcomes, frailty, and healthy ageing.