Spreading of diseases through comorbidity networks across life and gender

Chmiel, A., Klimek, P., & Thurner, S. (2014). Spreading of diseases through comorbidity networks across life and gender. New Journal of Physics 16 p. 115013. 10.1088/1367-2630/16/11/115013.

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The state of health of patients is typically not characterized by a single disease alone but by multiple (comorbid) medical conditions. These comorbidities may depend strongly on age and gender. We propose a specific phenomenological comorbidity network of human diseases that is based on medical claims data of the entire population of Austria. The network is constructed from a two-layer multiplex network, where in one layer the links represent the conditional probability for a comorbidity, and in the other the links contain the respective statistical significance. We show that the network undergoes dramatic structural changes across the lifetime of patients. Disease networks for children consist of a single, strongly interconnected cluster. During adolescence and adulthood further disease clusters emerge that are related to specific classes of diseases, such as circulatory, mental, or genitourinary disorders. For people over 65 these clusters start to merge, and highly connected hubs dominate the network. These hubs are related to hypertension, chronic ischemic heart diseases, and chronic obstructive pulmonary diseases. We introduce a simple diffusion model to understand the spreading of diseases on the disease network at the population level. For the first time we are able to show that patients predominantly develop diseases that are in close network proximity to disorders that they already suffer. The model explain more than 85% of the variance of all disease incidents in the population. The presented methodology could be of importance for anticipating age-dependent disease profiles for entire populations, and for design and validation of prevention strategies.

Item Type: Article
Uncontrolled Keywords: network medicine; disease dynamics; multilayer statistics; medical claims data
Research Programs: Advanced Systems Analysis (ASA)
Bibliographic Reference: New Journal of Physics; 16:115013 (November 2014)
Depositing User: IIASA Import
Date Deposited: 15 Jan 2016 08:50
Last Modified: 27 Aug 2021 17:39
URI: https://pure.iiasa.ac.at/10794

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