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: education, mobility, poverty, wealth, income, segregation, and deprivation of resources and services. We developed the GeoSES using Principal Component Analysis and evaluated its construct, content, and applicability. GeoSES is defined at three scales: national (GeoSES-BR), Federative Unit (GeoSES-FU), and intra-municipal (GeoSES-IM). GeoSES-BR dimensions showed a good association with HDI-M (correlation above 0.85). The model with the poverty dimension best explained the relative risk of avoidable cause mortality in Brazil. In the intra-municipal scale, the model with GeoSES-IM was the one that best explained the relative risk of mortality from circulatory system diseases. By applying spatial regressions, we demonstrated that GeoSES shows significant explanatory potential in the studied scales, being a compelling complement for future researches in public health.
Highlights
The ZIP code paradigm says that the place where a person lives is a more critical health predictor than their genetic code [1]
We developed the new index for use at three aggregation scales: national (GeoSES-BR), Federative Unit (GeoSES-FU) and intra-municipal (GeoSES-IM, for the 140 municipalities with three or more census sample areas)
Due to the principal component analysis (PCA) mechanism, the selected variables are naturally representative of the dimensions to which they belong, and the dimensions that make up GeoSES are relevant to contextualize the socioeconomic phenomena due to the theoretical frameworks
Summary
The ZIP code paradigm says that the place where a person lives is a more critical health predictor than their genetic code [1]. As individual measures of socioeconomic indicators are rarely available in medical records [6], it is common to use a single geographical variable that summarizes living conditions (e.g., income, education, per capita Gross Domestic Product). This approach helps to understand how an aspect of the socioeconomic context is related to health, the interpretation of the findings is limited, as the socioeconomic context is multidimensional, involving aspects such as employment, income, education, housing, segregation, mobility, among others [7]
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