Exploring Spatial and Scalar Perspectives on the Links Between Urban Socioeconomic Deprivation and Health Outcomes

Understanding urban deprivation and its impact on health is crucial for addressing inequalities in cities. Using Quito as a case study, we developed a census-based socioeconomic urban deprivation index and analyzed three health outcomes: fatal injuries, COVID-19 deaths, and maternal mortality. Spatial patterns were examined using Local Moran’s I, and regression analyses included OLS, spatial lag, spatial error, and GWR models, applied at two spatial scales, census sectors and census zones, with deprivation as the independent variable. Most of the regression models indicated that deprivation does not explain health outcomes, with the exception of fatal injuries and COVID-19 deaths at the census zone scale when spatial error models are applied. Our results also revealed MAUP effects, as spatial patterns and associations between the studied variables vary depending on spatial scale. Spatial models improved explanatory power compared to OLS, uncovering spatial dependence and heterogeneity in the relationships between deprivation and health outcomes. Our findings underscore the importance of multiscale and spatially explicit approaches in urban health research and provide actionable evidence for targeted interventions and urban planning that account for both local and structural patterns of deprivation.