Bayesian Comorbidity Network and Cost Analysis for Asthma.

Abstract

The evolving disease spectrum poses significant challenges to the asthma management, thus worsening health quality and increased financial burden on patients. However, potential dependency pattern in comorbidity spectrum remains unclear. We built comorbidity networks based on Bayesian networks utilizing 19604 asthma-patient hospitalization data to investigate dependency patterns among asthma comorbidities. We analyze static properties and trajectory behaviors of gender- and age-stratified asthmatic comorbidity networks. Results suggest that chronic obstructive pulmonary disease, respiratory failure, hypertension, atherosclerosis, and gastritis and duodenitis are the hubs of the asthma comorbidity network. They have a strong dependency pattern, while most of the associations among other comorbidities are sparse and weak. The strength of association between comorbidities is higher in female asthmatics than in males. Although the comorbidity network in children with asthma is simple and stable, the onset of common comorbidities as they age will enhance the association between comorbidities and thus increase the risk of developing other comorbidities. Furthermore, the more attributes of comorbidities, the stronger association with each other, and the greater risk of causing high treatment costs. Our study will help to dissect the asthma co-morbidity network and provide a basis for improving asthma management and cost control.