Leader: Vera Djordjilovic (UNIVE); Other collaborator(s):
This task aims to study healthy ageing in Italy observing trends in morbidity compression, comparing these with other countries; studying the possible mechanism of reduction of chronic diseases, particularly in relationship with the social determinants of health.
Brief description of the activities and of the intermediate results
Nov 23 Mar 24 the team has worked to propose deeper analyses on morbidity compression. First results have been presented in a meeting in Milan (Boccon - Feb 26th) and submitted to an international journal (JRSS-A (under review).
Main policy, industrial and scientific implications
First evidence confirms the relevance of policies for active aging favouring a morbidity compression. This seems to be in action but still major difference among population subgroups are present and gaps seems to be constant over the years
Brief description of the activities and of the intermediate results
The team has been working on analyzing trends in morbidity compression, with a focus on the social determinants of health and their varying impacts across different regions of Italy. Additionally, a web application that reports the estimated prevalence of chronic health conditions for various subgroups of the population in different regions has been partially developed (though it is not yet published). The results of this research, along with the web app concept, were presented at the “Age-It General Meeting” in Venice and at the National Statistical Conference (SIS 2024). Furthermore, a critical discussion on the ongoing research using PASSI data took place during the workshop "Quattro PASSI per Ca' Foscari," with participation from the PASSI Technical Coordination Group.
Main policy, industrial and scientific implications
Our findings confirm the importance of policies promoting active aging, which support morbidity compression. Notably, improvements in the social conditions of the population appear to play a more significant role than general technological and environmental advancements. However, substantial differences persist across various population subgroups (both geographical and economic), and these disparities may even seem to be increasing over time.
Brief description of the activities and of the intermediate results
The team has been worked on reviewing the initial results on morbidity compression, compiling a revison for the work submitted to JRSSA. Results of this research, along with the web app concept, were presented and discussed at the international statistical meetings “ISBA 2024” and “Greek Stochastic 2024”.
Main policy, industrial and scientific implications
Our findings confirm the importance of policies promoting active aging, which support morbidity compression. Notably, improvements in the social conditions of the population appear to play a more significant role than general technological and environmental advancements. However, substantial differences persist across various population subgroups (both geographical and economic), and these disparities may even seem to be increasing over time.
Brief description of the activities and of the intermediate results
The team has been working on finalizing a web application that reports the estimated prevalence of chronic health conditions across various population subgroups in different regions. The web app is now publicly accessible at this link. The concept of the web app was presented at the conference "Lorenzo Bernardi e la statistica sociale, tra formazione, società e istituzioni", where it was awarded Best Poster.
Additionally, the team has been developing a scientific paper focused on effectively communicating complex statistical results in the public health domain.
Main policy, industrial and scientific implications
By integrating prevalence rates—predicted using complex statistical models—with interactive visualizations, the web application enhances the accessibility and usability of statistical findings. This makes the information more actionable for policymakers and healthcare professionals, supporting data-driven decision-making in public health.