Spatial patterns of mortality from respiratory diseases and their relationship with socio-environmental indicators in Brazil

Abstract

This study analyzes the spatial autocorrelation of mortality rates from respiratory diseases in Brazilian municipalities between 1999 and 2022, emphasizing associated socioeconomic and environmental factors, including GDP per capita, population density, urbanization index, and greenhouse gas (GHG) emissions. Statistical analyses related to the Global Moran’s I and Local Moran’s Index (LISA) were applied to identify spatial patterns of dependence, followed by Pearson correlation analysis. The results reveal a reduction in the global autocorrelation of mortality over time, with persistent high-risk clusters in the South and Southeast regions, especially among the elderly and groups with higher education levels, while the North and Northeast regions show greater vulnerability associated with low education, income, and urbanization. GHG emissions maintained distinct spatial patterns over the period, with CH₄ showing relative stability, whereas CO₂ and N₂O showed an increase in spatial autocorrelation in more recent years. The findings reinforce the importance of spatial analysis in identifying territorial health inequalities and provide support for integrated public policies aligned with the Sustainable Development Goals (SDGs) aimed at mitigating the effects of climate change and promoting socio-environmental equity