Constructing the Trajectories of Multimorbidity Patterns of Chronic Diseases Leading to Death at Older Ages

Hoang Khanh Linh Dang, Alma Mater Studiorum - Università di Bologna
Nicola Caranci, Direction of Health and Welfare, Emilia-Romagna Region
Giulia Roli, University of Bologna
Rosella Rettaroli, Università di Bologna
Rossella Miglio, Università di Bologna

One key factor to construct sound measures to prevent adverse health outcomes and to allocate healthcare resources for sustainable aging populations is the possibility of identifying precise multimorbidity patterns and seizing their trajectories in time. In both developed and developing countries, understanding the structure of multimorbidity, and most ideally across time, is an urging challenge, so that groups who share the same degree of vulnerability and needs can receive assistance and intervention in a timely manner. Compared to traditional approach like factorial and clustering analysis that have been of standard practice in the literature, combining the probabilistic approach of graphical model and the intuitive visibility of network analysis is emerging quickly as powerful tool in recent years to not only efficiently explore the richness of administrative health data, but also to provide a framework with predictability. By applying these methods on reliable longitudinal data of individuals aged 50 and above residing Emilia-Romagna region (northern Italy) in 2011 and followed up to 2019 (N = 1,010,610), we study the multimorbidity patterns at older ages and their changes across time. Using hidden Markov model based on the estimated multimorbidity patterns, we construct the trajectories of multimorbidity leading to death at older ages.

Keywords: Mortality and Longevity, Longitudinal studies , Health and Morbidity, Population Ageing

See extended abstract.