GeoSES: A socioeconomic index for health and social research in Brazil

Abstract

The individual's socioeconomic conditions are the most relevant to predict the quality of someone's health. However, such information is not usually found in medical records, making studies in the area difficult. Therefore, it is common to use composite indices that characterize a region socioeconomically, such as the Human Development Index (HDI). The main advantage of the HDI is its understanding and adoption on a global scale. However, its applicability is limited for health studies since its longevity dimension presents mathematical redundancy in regression models. Here we introduce the GeoSES, a composite index that summarizes the main dimensions of the Brazilian socioeconomic context for research purposes. We created the index from the 2010 Brazilian Census, whose variables selection was guided by theoretical references for health studies. The proposed index incorporates seven socioeconomic dimensions:

Inference of COVID-19 epidemiological distributions from Brazilian hospital data

“…In order to explain the origin of the geographical variation of average distribution times across states, shown in figure 3, we present a basic exploratory analysis based on relevant high-level features. We examined the correlation between socio-economic factors, such as education, poverty, income, wealth, deprivation and segregation, using a number of socioeconomic state-level indicators obtained from Barrozo et al [43] and additional datasets containing the mean age per state and percentage of people living in the urban areas (urbanicity) [44]. The Pearson correlation coefficients, shown in appendix B, table 8, suggest that poverty, income, segregation and deprivation elements were most strongly correlated with the analysed onset-time datasets.…”

“…In particular, poverty was strongly negatively correlated with hospital-admission-to-death (−0.68), whereas income and segregation had a high positive correlation coefficient for the same distribution (+0.60, +0.62, respectively). The strongest correlation was observed for hospital-admission-to-death and deprivation indicator, which measures the access to sanitation, electricity and other material and non-material goods [ 43 ]. Interestingly, the indicators measuring economic situation were more correlated with average hospitalization times than mean age per state, which suggests that although the low- and middle-income countries typically have younger populations, their healthcare systems are more likely to struggle in response to the COVID-19 epidemic.…”

Spatiotemporal ecological study of COVID-19 mortality in the city of São Paulo, Brazil: Shifting of the high mortality risk from areas with the best to those with the worst socio-economic conditions

“…To measure the socio-economic condition of the population, since individual-level data are not available in the mortality database, we used a socio-economic index elaborated for health research. The Socio-economic Index of the Geographic Context for Health Studies (GeoSES) [ 17 ] was developed using principal component analysis, starting with 41 variables. The index conceives the socio-economic condition by considering seven parameters based on the theoretical background [ 18 , 19 ]: education, mobility, poverty, wealth, income, segregation, and deprivation of resources and services.…”

Geographic access to COVID-19 healthcare in Brazil using a balanced float catchment area approach

“…More research is still needed to investigate how the availability of health professionals could hinder the availability of services to the population. Future studies could also benefit from the work developed by Barrozo et al (2020b) to further examine the relationship between socioeconomic and health conditions, including those associated with COVID-19.…”