Understanding the Comorbidity of Multiple Chronic Diseases Using a Network Approach

Chronic diseases and associated conditions are the leading causes of death in most of the countries worldwide. Due to this, governments all over the world are concerned about the burden of chronic diseases. These diseases often pose severe health risks to the patients when they suffer from more than one chronic disease at the same time (also named as comorbidity of chronic disease). Several prediction approaches utilizing routinely collected administrative healthcare data have been developed for the prevention and better management of comorbidity. Most studies to date have focused on understanding the progression of single chronic disease rather than multiple chronic diseases. In this study, a research framework is proposed using social network analysis and graph theory using administrative healthcare data to understand the comorbidity of two chronic diseases (i.e., type 2 diabetes (T2D) leading to the development of cardiovascular disease). The results show that diseases related to blood (e.g., high blood pressure or high cholesterol) and kidney disease occurred frequently during the progression of cardiovascular disease for the T2D patients. The proposed framework could be useful for stakeholders including governments and health insurers to adopt appropriate prevention or management program for the patients at high risk of developing multiple chronic diseases.