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

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.

Socio-Economic Development and Mental Health: Case Study of the Spanish Region of Aragon (2010-20).

Introduction: Considering health as a cross-cutting element of all public policies leads to rethinking its interactions with the environment in which people live. The collection of large volumes of data by public administrations offers the opportunity to monitor and analyze the possible associations between health and territory. The increase in the incidence and prevalence of mental health diseases, particularly depression, justifies the need to develop studies that seek to identify links with the socioeconomic and environmental setting. Objective: The objective of this study is to explain the behavior of the depression in a mediterranean region of Northeastern Spain from an ecological and diachronic perspective. Methods: We conducted a correlation and multivariate logistic regression analysis to identify explanatory factors of the prevalence of depression in 2010 and 2020 and in the variation rate. Potential explanatory factors are related to the socioeconomic status and to the territorial development level. Results: The regression models retained both socioeconomic and territorial development variables as predictors of the prevalence in both years and in the variation rate. Rural areas seem to play a protective role against the prevalence. Conclusion: It is under the territorial prism that epidemiological studies could offer useful guidelines for proactive decision-making. The integration of data on diseases and territory must be considered when developing policies for the creation of healthier environments and for directing health services with more specific resources to where they may be needed.

Analysis of the impact of social determinants and primary care morbidity on population health outcomes by combining big data: A research protocol.

Background: In recent years, different tools have been developed to facilitate analysis of social determinants of health (SDH) and apply this to health policy. The possibility of generating predictive models of health outcomes which combine a wide range of socioeconomic indicators with health problems is an approach that is receiving increasing attention. Our objectives are twofold: (1) to predict population health outcomes measured as hospital morbidity, taking primary care (PC) morbidity adjusted for SDH as predictors; and (2) to analyze the geographic variability of the impact of SDH-adjusted PC morbidity on hospital morbidity, by combining data sourced from electronic health records and selected operations of the National Statistics Institute (Instituto Nacional de Estadística/INE). Methods: The following will be conducted: a qualitative study to select socio-health indicators using RAND methodology in accordance with SDH frameworks, based on indicators published by the INE in selected operations; and a quantitative study combining two large databases drawn from different Spain's Autonomous Regions (ARs) to enable hospital morbidity to be ascertained, i.e., PC electronic health records and the minimum basic data set (MBDS) for hospital discharges. These will be linked to socioeconomic indicators, previously selected by geographic unit. The outcome variable will be hospital morbidity, and the independent variables will be age, sex, PC morbidity, geographic unit, and socioeconomic indicators. Analysis: To achieve the first objective, predictive models will be used, with a test-and-training technique, fitting multiple logistic regression models. In the analysis of geographic variability, penalized mixed models will be used, with geographic units considered as random effects and independent predictors as fixed effects. Discussion: This study seeks to show the relationship between SDH and population health, and the geographic differences determined by such determinants. The main limitations are posed by the collection of data for healthcare as opposed to research purposes, and the time lag between collection and publication of data, sampling errors and missing data in registries and surveys. The main strength lies in the project's multidisciplinary nature (family medicine, pediatrics, public health, nursing, psychology, engineering, geography).