Population-health analysis of the progress of chronic disease burden over a 10-year period in a regional cohort of 5.5 million adults living in Catalonia

Multimorbidity, a major driver of healthcare demand and clinical complexity, is often addressed in a disease-centric manner and remains insufficiently understood in its population-level dynamics. Using data from a 10-year population-based cohort of 5.5 million adults in Catalonia, Spain, we quantified multimorbidity burden using the Adjusted Morbidity Groups (AMG) index to predict progression from low/moderate (< percentile 80) to high/very high (≥ P80) burden. Machine learning and statistical models (including random forest, neural networks, and gradient boosting) were used to assess predictive factors, while network analyses explored co-occurrence patterns among chronic conditions. During follow-up, 39.2% of individuals transitioned to high/very high burden. Baseline AMG score was the strongest predictor of progression, surpassing models relying solely on individual diagnoses. The most prevalent conditions were nutritional and endocrine disorders, anxiety, and hypertension, with notable sequential links between mental and physical disorders. Findings emphasize the need for integrated, patient-centred care strategies and population-based prevention approaches to mitigate multimorbidity progression. Clinical trial number : Not applicable.