A Geographical approach to understand the impacts of Social Determinants of Health on COVID-19 outcomes in an intra-urban context in Sao Paulo State, Brazil

In recent years, health geographers have focused on studies related to COVID-19, proposing frameworks for understanding the Brazilian territory and synthesizing its spatial diffusion pattern. In this process, there is a need for a deeper intra-urban analysis of the impact of COVID-19 on the health of populations in small-to medium-sized cities in non-metropolitan areas. Thus, we aimed to articulate the theory of Social Determinants of Health (SDH) and geoprocessing methodologies to analyze the health situation in the municipality of Presidente Prudente (SP). For this purpose, we used Inverse Distance Weighted (IDW) and Geographically Weighted Regression (GWR) as methodological tools. IDW performs a weighted interpolation among neighbors, generating heat surfaces, whereas GWR executes a spatial regression, presenting correlations between variables. For the analyzed period (March 2020 to July 2021), we found medium-strong correlations between cases and the non-white population, per capita income, and elderly population (R2 = 0.46, 0.39, and 0.49, respectively). For deaths, only the elderly population was relevant (R2 = 0.34) and the correlation between cases and deaths was satisfactory (R2 = 0.65 and 0.51). The results highlight areas of concern, demonstrating that geoprocessing is an important tool for health surveillance, identifying areas and population characteristics that require greater attention, and geography as an important science that helps to understand the impacts of COVID-19.