000125298 001__ 125298
000125298 005__ 20241125101151.0
000125298 0247_ $$2doi$$a10.3389/fmed.2023.1016157
000125298 0248_ $$2sideral$$a132929
000125298 037__ $$aART-2023-132929
000125298 041__ $$aeng
000125298 100__ $$0(orcid)0000-0002-7213-1718$$aBentué-Martínez, Carmen$$uUniversidad de Zaragoza
000125298 245__ $$aSpatial patterns in sociodemographic factors explain to a large extent the prevalence of hypertension and diabetes in Aragon (Spain)
000125298 260__ $$c2023
000125298 5060_ $$aAccess copy available to the general public$$fUnrestricted
000125298 5203_ $$aIntroduction: The global burden of multi-morbidity has become a major public health challenge due to the multi stakeholder action required to its prevention and control. The Social Determinants of Health approach is the basis for the establishment of health as a cross-cutting element of public policies toward enhanced and more efficient decision making for prevention and management.
Objective: To identify spatially varying relationships between the multi-morbidity of hypertension and diabetes and the sociodemographic settings (2015–2019) in Aragon (a mediterranean region of Northeastern Spain) from an ecological perspective.
Materials and methods: First, we compiled data on the prevalence of hypertension, diabetes, and sociodemographic variables to build a spatial geodatabase. Then, a Principal Component Analysis (PCA) was performed to derive regression variables, i.e., aggregating prevalence rates into a multi-morbidity component (stratified by sex) and sociodemographic covariate into a reduced but meaningful number of factors. Finally, we applied Geographically Weighted Regression (GWR) and cartographic design techniques to investigate the spatial variability of the relationships between multi-morbidity and sociodemographic variables.
Results: The GWR models revealed spatial explicit relationships with large heterogeneity. The sociodemographic environment participates in the explanation of the spatial behavior of multi-morbidity, reaching maximum local explained variance (R2) of 0.76 in men and 0.91 in women. The spatial gradient in the strength of the observed relationships was sharper in models addressing men’s prevalence, while women’s models attained more consistent and higher explanatory performance.
Conclusion: Modeling the prevalence of chronic diseases using GWR enables to identify specific areas in which the sociodemographic environment is explicitly manifested as a driving factor of multi-morbidity. This is step forward in supporting decision making as it highlights multi-scale contexts of vulnerability, hence allowing specific action suitable to the setting to be taken.
000125298 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000125298 590__ $$a3.1$$b2023
000125298 592__ $$a0.909$$b2023
000125298 591__ $$aMEDICINE, GENERAL & INTERNAL$$b57 / 329 = 0.173$$c2023$$dQ1$$eT1
000125298 593__ $$aMedicine (miscellaneous)$$c2023$$dQ1
000125298 594__ $$a5.1$$b2023
000125298 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000125298 700__ $$0(orcid)0000-0002-0477-0796$$aRodrigues Mimbrero, Marcos$$uUniversidad de Zaragoza
000125298 700__ $$0(orcid)0000-0002-9541-5609$$aZúñiga-Antón, María$$uUniversidad de Zaragoza
000125298 7102_ $$13006$$2435$$aUniversidad de Zaragoza$$bDpto. Geograf. Ordenac.Territ.$$cÁrea Geografía Humana
000125298 7102_ $$13006$$2010$$aUniversidad de Zaragoza$$bDpto. Geograf. Ordenac.Territ.$$cÁrea Análisis Geográfico Regi.
000125298 773__ $$g10 (2023), 1016157 [11 pp.]$$pFront. med.$$tFrontiers in Medicine$$x2296-858X
000125298 8564_ $$s1716762$$uhttps://zaguan.unizar.es/record/125298/files/texto_completo.pdf$$yVersión publicada
000125298 8564_ $$s2182959$$uhttps://zaguan.unizar.es/record/125298/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000125298 909CO $$ooai:zaguan.unizar.es:125298$$particulos$$pdriver
000125298 951__ $$a2024-11-22-12:06:54
000125298 980__ $$aARTICLE