Abstract: After two years of the COVID-19 pandemic, there is extensive research on the spread of the virus and geostatistical analysis of spatial patterns. However, from the perspective of health geography, COVID-19 mortality is still under-studied. This research aims to provide a geographic profile of COVID-19 mortality, in terms of the space-time evolution and the relationship with individual and contextual variables. To this end, we geocoded the daily COVID-19 microdata of deceased persons provided by the Government of Cantabria (in northern Spain) from March 1, 2020 to March 31, 2022. The study also took cadastral variables, population records, and connections to geo-enrichment services accessed through ArcGIS Pro License (ESRI) into account. Using spatial statistics methods, such as 3D bins and emerging hot spots, local bivariate relationships, and ordinary least squares, we propose an exportable and scalable methodology to help policymakers cope with the current stage of living with the epidemic virus. Our results suggest that the spatial distribution of mortality is less clustered than that of contagion and shed light on differences in COVID-19 mortality profiles inside/outside nursing homes, such as higher age, and the temporal concentration of deaths in nursing homes. Spatial regimes showed hot spots of COVID-19 mortality in urban and metropolitan areas, with a pattern of repetition over time, such as sporadic hot spots that accounted for 36.28% of deaths in only 11.88% of the area with COVID-19 deaths. Despite immunization, periods of high contagion meant a subsequent increase in mortality, such as during the Omicron wave, where consecutive metropolitan hot spots accounted for 37.50% of the area and 51.45% of deaths were concentrated. Finally, there were interesting nuances in the significant local context variables of COVID-19 mortality compared with the explanatory factors of COVID-19 cases.

Authorship

Download reference