# Ten-year population-based assessment of multimorbidity burden progression in a regional cohort of 5.5 million adults

## TL;DR

This study analyzed how multimorbidity, or having multiple chronic conditions, progresses in a large population over 10 years, highlighting the need for better healthcare strategies.

## Contribution

The study introduces a population-based assessment of multimorbidity progression using the AMG index and machine learning to identify predictive factors.

## Key findings

- 39.2% of individuals transitioned to high/very high clinical complexity over the 10-year period.
- Baseline AMG score was the strongest predictor of progression compared to models using individual diagnoses.
- Mental and physical disorders showed notable sequential links in multimorbidity progression.

## Abstract

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-associated clinical complexity using the Adjusted Morbidity Groups (AMG) index to predict progression from low/moderate ( < P80) to high/very high ( ≥ P80) complexity. Machine learning models identified predictive factors, while network analyses explored co-occurrence patterns among chronic conditions. During follow-up, 39.2% of the individuals who remained alive throughout the analysis period transitioned to high/very high complexity. 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.