Analysis of multimorbidity compression using a latent variable in a mixed mixture model

Multimorbidity, i.e., the co-presence of multiple diseases in an individual, is an increasing concern, particularly as the population ages. Addressing it is critical to improving health status and optimizing healthcare resources. Particularly relevant in this scenario is the concept of multimorbidity compression, i.e., the onset of chronic diseases is delayed more rapidly than the increase in life expectancy. According to this theory, the duration individuals spend in poor health should be shortened. Existing studies have started examining multimorbidity trends, yet often overlook the cumulative burden of multiple diseases.

We define the multimorbidity concept as a latent variable estimated with the disease burden described by the disability weights from the Global Burden of Diseases (GBD) project. Using a mixed-mixture model, we analyze the nonlinear relationship between multimorbidity and socioeconomic traits, accounting for zero inflation and spatial variability in Italy. We use twelve years of the surveillance system PASSI data to investigate the multimorbidity compression concept.

Our findings suggest multimorbidity compression is acting in Italy: severe multimorbidities are increasingly concentrated later in life, indicating a positive impact of healthcare improvements on the quality of life. The phenomenon is observed in both socially advantaged and disadvantaged subpopulations.