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dc.rights.licenseAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.contributor.authorSoto-Gordoa, Myriam
dc.contributor.otherArrospide Elgarresta, Mikel
dc.contributor.otherGerovska, Daniela
dc.contributor.otherJauregui García, María L.
dc.contributor.otherMerino Hernández, Marisa L.
dc.contributor.otherAraúzo Bravo, Marcos J.
dc.date.accessioned2024-10-16T13:44:59Z
dc.date.available2024-10-16T13:44:59Z
dc.date.issued2024
dc.identifier.issn2589-0042en
dc.identifier.otherhttps://katalogoa.mondragon.edu/janium-bin/janium_login_opac.pl?find&ficha_no=178127en
dc.identifier.urihttps://hdl.handle.net/20.500.11984/6664
dc.description.abstractMultimorbidity (MM) is the co-occurrence of two or more chronic diseases. We provided a dynamic approach revealing the MM complexity constructing a multistep incidence-age model for all patients with MM between 2014 and 2021 in the Basque Health System, Spain. The multistep model, with eight steps for males and nine for females, is a very well-fitting representation of MM. To gain insight into the MM components, we modeled the 19 diseases used to calculate the Charlson Comorbidity Index (CCI). We observed that the CCI diseases formed a complex interaction network. Hierarchical clustering of the incidence-age profiles clustered the CCI diseases into low- and high-risk of dying pathologies. Diseases with a higher number of steps are better represented by a multistep model. Anatomically, diseases associated with the central nervous system have the highest number of steps, followed by those associated with the kidney, heart, peripheral vasulature, pancreas, joints, cerebral vasculature, lung, stomach, and liver.en
dc.language.isoengen
dc.publisherElsevieren
dc.rights© 2024 The Authorsen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectPathophysiologyen
dc.subjectbioinformaticsen
dc.titleChronic disease incidence explained by stepwise models and co-occurrence among themen
dcterms.accessRightshttp://purl.org/coar/access_right/c_abf2en
dcterms.sourceiScienceen
local.contributor.groupDirección de operaciones logístico productivases
local.description.peerreviewedtrueen
local.identifier.doihttps://doi.org/10.1016/j.isci.2024.110816en
local.source.detailsVol. 27. N. 9. N. art. 110816, 2024
oaire.format.mimetypeapplication/pdfen
oaire.file$DSPACE\assetstoreen
oaire.resourceTypehttp://purl.org/coar/resource_type/c_6501en
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85en
dc.unesco.tesaurohttp://vocabularies.unesco.org/thesaurus/concept251en
dc.unesco.tesaurohttp://vocabularies.unesco.org/thesaurus/concept10264en
oaire.funderNameComisión Europeaen
oaire.funderIdentifierhttps://ror.org/00k4n6c32 / http://data.crossref.org/fundingdata/funder/10.13039/501100000780en
oaire.fundingStreamHorizon 2020en
oaire.awardNumber899417en
oaire.awardTitleCircular DNA in diagnosis and disease models (CIRCULAR VISION)en
oaire.awardURIhttps://doi.org/10.3030/899417en
dc.unesco.clasificacionhttp://skos.um.es/unesco6/3202en
dc.unesco.clasificacionhttp://skos.um.es/unesco6/3212en


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Attribution-NonCommercial-NoDerivatives 4.0 International
Excepto si se señala otra cosa, la licencia del ítem se describe como Attribution-NonCommercial-NoDerivatives 4.0 International