Spreading of diseases through comorbidity networks across life and gender

10794 9 34665 2 77073 document 34665 Spreading of diseases.pdf application/pdf 921ac6b15a43c6456d34f8a742fa66a9 MD5 1482747 2016-02-26 10:34:32 https://pure.iiasa.ac.at/id/eprint/10794/1/Spreading%20of%20diseases.pdf 10794 1 1 application/pdf text en public cc_by_nc

Spreading of diseases.pdf

published 34666 2 77076 document 34666 lightbox.jpg image/png d3a05901e0848cf9d6fab78ea989d09d MD5 13143 2016-02-26 10:35:01 https://pure.iiasa.ac.at/id/eprint/10794/2/lightbox.jpg 10794 2 2 image/png other Thumbnails conversion from text to thumbnail_lightbox en public

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http://eprints.org/relation/isVersionOf https://pure.iiasa.ac.at/id/document/34665 http://eprints.org/relation/isVolatileVersionOf https://pure.iiasa.ac.at/id/document/34665 http://eprints.org/relation/isIndexCodesVersionOf https://pure.iiasa.ac.at/id/document/34665 archive 351 disk0/00/01/07/94 2016-01-15 08:50:14 2021-08-27 17:39:37 2016-01-15 08:50:14 article show Chmiel A. Klimek P. 1924 Thurner S. 1918 Spreading of diseases through comorbidity networks across life and gender pub iis_sys prog_asa network medicine; disease dynamics; multilayer statistics; medical claims data 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. 2014-11 published IOP 10.1088/1367-2630/16/11/115013 XJ-14-257 New Journal of Physics; 16:115013 (November 2014) [doi:10.1088/1367-2630/16/11/115013] 307 public New Journal of Physics 16 115013 115013 TRUE 1367-2630 FALSE info:eu-repo/semantics/article