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- Health inequalities in cerebro and cardiovascular health – an analysis of the e_COR studyPublication . Afonso dos Santos, Carolina Sofia; Antunes, Marília Cristina de Sousa; Alves, Ana CatarinaAccording to the World Health Organization (WHO), health inequalities are systematic differences in the health status of different population groups with significant social and economic costs for individuals and societies. These differences are unfair and can be mitigated through the action of government policies, so it is necessary to understand their impact. Cardiovascular diseases (CVD) are a group of disorders of the brain, heart and blood vessels and include coronary heart disease, cerebro-cardiovascular disease, rheumatic heart disease and other conditions. More than four out of five CVD deaths are due to heart attacks and strokes, and one third of these deaths occur prematurely in people under 70 years of age (WHO). The events of interest considered in this work were stroke, acute myocardial infarction, and peripheral arterial disease. In Portugal in 2019, strokes were the number one cause of death representing 9.8% of the total mortality and the deaths by myocardial infarction represented 3.8% of the total mortality, according to INE. It is estimated that about 10 670 potential years of life were lost due to cerebro-cardiovascular diseases in Portugal in 2019. In addition to being interesting for being very prevalent across Europe, cerebrovascular diseases are impacted by the lifestyle and behavior of individuals. Several studies have concluded that a substantial part of the risk of developing cerebro-cardiovascular disease can be reduced through personal choices and preventive approaches, such as adapting healthy lifestyles. An individual’s socioeconomic position has a potential effect on their health status and also on the health care they receive, which means that socioeconomic inequalities are determinants of health. The European Deprivation Index (EDI) is a way of quantifying the level of deprivation of individuals and, therefore, the version adapted to the Portuguese reality, developed in 2016 by a multinational and multidisciplinary team of researchers, was incorporated in this work. The main objective of this work is to evaluate the effect of health inequalities, namely those associated with low education and levels of deprivation in cerebro-cardiovascular disease. To achieve this objective, the main set of data used resulted from the e_COR study, developed by the Instituto Nacional de Saúde Doutor Ricardo Jorge, between 2012 and 2014, within the scope of cardiovascular prevention and with the objective of estimating the prevalence of cardiovascular risk factors. In the present work, the past occurrence of at least one cerebro-cardiovascular event and the number of risk factors that each of the participants in the e_COR study had were studied. The variables under study are some of the variables collected during the e_COR study, referring to demographic, physical, metabolic, medical history, and lifestyle characteristics of the participants. Some other variables were collected such as variables that characterize access to healthcare, extracted from INE, and variables related to socioeconomic status, that resulted from the EDI adapted to the Portuguese context. To model the occurrence of a cerebro-cardiovascular event, the logistic regression method was used, as it suits the binary nature of the response variable. Before modeling the data, possible correlations between the variables under study were evaluated. The variables that were included in the Generalized Linear Model were selected through the Stepwise Selection process, considering an inclusion p-value of 0.20 and an exclusion p-value of 0.25. The confounding variables, sex and age, were the first to be introduced in the model and, regardless of their p-value, they always remained in the model. After the Stepwise process, variables related to levels of education and deprivation were introduced. The random effect of the region of residence was added to the final model. Since there was a large imbalance between cases and non-cases of cerebro-cardiovascular disease, the SMOTE technique was used to create a more balanced dataset and the same modeling process was applied. For the validation of the models obtained, the ROC curve with the respective AUC was calculated and cross validation was also performed. Poisson regression was used to model the number of risk factors, since it is one of the methods indicated when the response variable is a count variable. In this model, once again, confounding variables, sex and age were included, as well as the variables related to inequalities, education levels and deprivation levels. Multiple comparisons were performed to assess how variables associated with inequalities affect the number of risk factors that each individual has. The recorded cerebro-cardiovascular events that occurred happened in the past and the risk factors that individuals have are in the present, so the interpretation of the models is not the most conventional and some of the variables would have different values if they had been evaluated before the event occurred. Both logistic regression models suggest that in the presence of two individuals with similar physical and biological characteristics, the influence of their levels of education or deprivation is not significant, and these are not good indicators to distinguish individuals who have already suffered a cerebro-cardiovascular event from those who have not. The random effect of the region is also not statistically significant. Since the event has already occurred, this model is useful mainly to distinguish individuals who have already suffered the event from those who have not suffered, through their characteristics. The Poisson regression model suggests that an individual's education levels affect the number of risk factors that an individual has, i.e., it was found that individuals with more advanced education had a lower number of risk factors.