Título Analysis of the impact of social determinants and primary care morbidity on population health outcomes by combining big data: A research protocol
Autores Couso-Viana, Sabela , Bentue-Martinez, Carmen , Victoria Delgado-Martin, Maria , Cabeza-Irigoyen, Elena , Leon-Latre, Montserrat , Concheiro-Guisan, Ana , Xose Rodriguez-Alvarez, Maria , Roman-Rodriguez, Miguel , Roca-Pardinas, Javier , Zuniga-Anton, Maria , Garcia-Flaquer, Ana , Pericas-Pulido, Pau , Sanchez-Recio, Raquel , Gonzalez-Alvarez, Beatriz , Rodriguez-Pastoriza, Sara , GÓMEZ GÓMEZ, IRENE, MOTRICO MARTINEZ, EMMA, Luis Jimenez-Murillo, Jose , Rabanaque, Isabel , Claveria, Ana
Publicación externa No
Medio Front. Med.
Alcance Article
Naturaleza Científica
Cuartil JCR 2
Cuartil SJR 1
Impacto JCR 3.90000
Web https://www.scopus.com/inward/record.uri?eid=2-s2.0-85145436048&doi=10.3389%2ffmed.2022.1012437&partnerID=40&md5=29ea40a0a2d38b06ec4967856ee44e51
Fecha de publicacion 16/12/2022
ISI 000905772800001
Scopus Id 2-s2.0-85145436048
DOI 10.3389/fmed.2022.1012437
Abstract BackgroundIn 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 Estadistica/INE). MethodsThe 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. AnalysisTo 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. DiscussionThis 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).
Palabras clave social determinants of health (MeSH); socioeconomic factors (MeSH); big data; electronic health records-HER; morbidity
Miembros de la Universidad Loyola

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