Spatial patterns in sociodemographic factors explain to a large extent the prevalence of hypertension and diabetes in Aragon (Spain)

Carmen Bentué-Martínez; Marcos Rodrigues Mimbrero; María Zúñiga-Antón

doi:10.3389/fmed.2023.1016157

Spatial patterns in sociodemographic factors explain to a large extent the prevalence of hypertension and diabetes in Aragon (Spain)

Front Med (Lausanne). 2023 Jan 25:10:1016157. doi: 10.3389/fmed.2023.1016157. eCollection 2023.

Authors

Carmen Bentué-Martínez¹, Marcos Rodrigues Mimbrero^{1

2}, María Zúñiga-Antón^{1

2

3}

Affiliations

¹ Department of Geography and Territorial Planning, University of Zaragoza, Zaragoza, Spain.
² Institute of Research Into Environmental Sciences of the University of Zaragoza, Zaragoza, Spain.
³ Health Research Institute of Aragon, Zaragoza, Spain.

Abstract

Introduction: 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.

Keywords: decision making; diabetes; hypertension; multi-morbidity; primary health care; social determinants of health.