Chronic disease incidence explained by stepwise models and co-occurrence among them

Area of Science:

• Epidemiology
• Biostatistics
• Gerontology

Background:

• Multimorbidity (MM) involves the co-occurrence of multiple chronic diseases, posing significant challenges in healthcare.
• Understanding the complexity and progression of MM is crucial for effective patient management and resource allocation.

Purpose of the Study:

• To develop and validate a dynamic, multistep incidence-age model to represent the complexity of multimorbidity.
• To analyze the interaction network and risk stratification of diseases within the Charlson Comorbidity Index (CCI).

Main Methods:

• Construction of a multistep incidence-age model for patients with MM (2014-2021) in the Basque Health System.
• Modeling of 19 diseases comprising the Charlson Comorbidity Index (CCI) to analyze their interaction network.
• Hierarchical clustering of incidence-age profiles to categorize CCI diseases by mortality risk.

Main Results:

• The multistep model, with 8 steps for males and 9 for females, provided a well-fitting representation of MM.
• CCI diseases formed a complex interaction network, clustered into low- and high-risk of dying pathologies.
• Central nervous system diseases exhibited the highest number of steps, indicating greater complexity in the model.

Conclusions:

• A dynamic, multistep incidence-age model effectively captures the complexity of multimorbidity.
• Disease progression and mortality risk can be stratified using incidence-age profiles and clustering techniques.
• Central nervous system, kidney, and heart diseases represent more complex trajectories within multimorbidity patterns.