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.
Brief description of the activities and of the intermediate results
Building on the work of previous months, part of the team has submitted the paper “Analysis of Multimorbidity Compression Using a Latent Variable in a Mixed Mixture Model” to the international journal Population Health Metrics (currently under review).
The team has finalized the research paper “Communicating Complex Statistical Models to a Public Health Audience: Translating Science into Action with the FARSI Approach”, which proposes and applies a novel framework aimed at improving the communication of complex statistical findings within the public health domain. The FARSI approach provides a structured method for developing user-friendly tools that enhance the accessibility of advanced statistical outputs. The paper includes a practical application: the translation of previous work on a Bayesian hierarchical model for multi-morbidity into the user-friendly web application accessible at this link. This work was also presented at the internal Age-It Workshop held at Ca’ Foscari University of Venice on February 27th.
Additionally, the team has begun exploring American morbidity data to lay the groundwork for future cross-national comparisons with the Italian context.
Main policy, industrial and scientific implications
The FARSI framework offers a systematic approach to improving how complex statistical models are communicated and understood. By making these models more accessible and interpretable, the framework supports evidence-based decision-making in public health and enhances the societal relevance and impact of scientific research.
Brief description of the activities and of the intermediate results
The team has submitted the paper “Communicating Complex Statistical Models to a Public Health Audience: Translating Science into Action with the FARSI Approach” to BMC Public Health. The FARSI framework was also presented at the outreach event “Lessi una tesi…” held at the Department of Statistical Sciences, University of Padova.
Progress has been made in the analysis of U.S. morbidity data, with a focus on social determinants of health and their regional variations. This work aims to support cross-national comparisons with Italy and to examine whether morbidity compression is stalling in the United States, particularly regarding cardiovascular disease and diabetes.
Additionally, part of the team presented the methodological contribution “Directional replicability: when is the factor of two necessary?” at the High-Dimensional Statistical Inference Workshop in Venice. This study addresses foundational issues in evaluating the consistency of findings across multiple studies addressing the same phenomenon. In particular, it studies conditions necessary to establish the replicability of findings derived from independent investigations.
Main policy, industrial and scientific implications
The FARSI framework, in addition to supporting evidence-based decision-making, also shows that it can be a valuable tool for improving the communication and public understanding of complex statistical models—helping to bridge the gap between technical research and policy-making.
Preliminary findings from the U.S. suggest a possible reversal of morbidity compression, particularly among cohorts born after the 1940s. Cardiovascular health appears to have worsened in the aftermath of the COVID-19 pandemic, potentially erasing a decade of progress. Meanwhile, diabetes prevalence continues to rise, likely driven by the ongoing obesity epidemic, especially among more recent cohorts. These trends highlight the urgent need for preventive public health interventions targeting metabolic and cardiovascular risk factors. Policies focused on prevention and health promotion may offer a useful complement—or alternative—to conventional clinical programs, with implications that are increasingly relevant for the Italian healthcare system as well.
Finally, the team’s research on directional replicability provides important methodological tools for validating findings across independent studies. This is particularly crucial in morbidity research, where confirming the robustness of results strengthens the reliability of evidence used to inform public health policy.