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Laura A Cilek , CCHS-CSIC
Markus Sauerberg, Federal Institute for Population Research
Pavel Grigoriev, Federal Institute for Population Research (BiB), Germany
Sebastian Klüsener, Federal Institute for Population Research (BiB)
The study of spatial variation in mortality tends to focus on regions or individuals situated within regions. Less focus has been given to identifying and understanding disparities between adjacent intra- and international sub-regions. However, focusing on differences e.g. across international vs. subnational borders can be very fruitful to deepen our understanding on factors driving spatial mortality variation. To illustrate the potential of such analyses, we use mortality data collected for 334 European regions. For this dataset we derive all pairwise first-order relationships (i.e. regions that border each other), resulting in 232 international and 1560 intranational borders. We employ methodologies not normally used in a demographic context, such as the Earth Mover’s Distance, to quantify the differences in mortality distributions between two regions, as well as an approach to reimagine clusters of mortality patterns and their boundaries simultaneously. They allow us to provide unique insights into the similarities and dissimilarities of mortality along intra- and international borders and their evolution in the decade before the COVID-19 pandemic. We identify where large differences in mortality exist between neighboring regions, how they differ by spatial distribution and sex, and measure the evolution of these patterns in the decade preceding the COVID-19 pandemic. Preliminary results indicate substantial spatial and sex-specific heterogeneity along both international and internal borders. In the subsequent analysis, results will also assess cause-specific contributions to the observed differences. Our approach to quantitatively study these cross-border disparities can easily be transferred to other research topics within the field of demography.
Presented in Session 52. Modelling Subnational and Spatial Variation