Summary
Objectives: Evolution of multiple chronic conditions (MCC) follows a complex stochastic process,
influenced by several factors including the inter-relationship of existing conditions,
and patient-level risk factors. Nearly 20% of citizens aged 18 years and older are
burdened with two or more (multiple) chronic conditions (MCC). Treatment for people
living with MCC currently accounts for an estimated 66% of the Nation’s healthcare
costs. However, it is still not known precisely how MCC emerge and accumulate among
individuals or in the general population. This study investigates major patterns of
MCC transitions in a diverse population of patients and identifies the risk factors
affecting the transition process.
Methods: A Latent regression Markov clustering (LRMCL) algorithm is proposed to identify major
transitions of four MCC that include hypertension (HTN), depression, Post- Traumatic
Stress Disorder (PTSD), and back pain. A cohort of 601,805 individuals randomly selected
from the population of Iraq and Afghanistan war Veterans (IAVs) who received VA care
during three or more years between 2002-2015, is used for training the proposed LRMCL
algorithm.
Results: Two major clusters of MCC transition patterns with 78% and 22% probability of membership
respectively were identified. The primary cluster demonstrated the possibility of
improvement when the number of MCC is small and an increase in probability of MCC
accumulation as the number of co- morbidities increased. The second cluster showed
stability (no change) of MCC overtime as the major pattern. Age was the most significant
risk factor associated with the most probable cluster for each IAV.
Conclusions: These findings suggest that our proposed LRMCL algorithm can be used to describe
and understand MCC transitions, which may ultimately allow healthcare systems to support
optimal clinical decision- making. This method will be used to describe a broader
range of MCC transitions in this and non-VA populations, and will add treatment information
to see if models including treatments and MCC emergence can be used to support clinical
decision-making in patient care.
Keywords
Multiple chronic conditions - Markov clustering - latent regression model - Markov
mixture model
