Geographic footprints of life expectancy inequalities in the state of Geneva, Switzerland

Abstract

Introduction Although Switzerland has one of the highest life expectancy in the world, this global indicator may mask significant disparities at a local level. The present study used a spatial cluster detection approach on individual mortality data to investigate the geographic footprint of life expectancy inequalities in the state of Geneva, Switzerland. Methods Individual-level mortality data (n=30,592) were obtained from the Geneva’s mortuary announcements (2003-2017). We measured life expectancy inequalities through Life Expectancy Difference (LED), defined as the difference between the individual’s age at death and their Life Expectancy at Birth. We assessed the spatial dependence of LED across the state of Geneva using spatial autocorrelation statistics (Local Moran’s I). To ensure the robustness of the discovered patterns, we ran the analysis for ten random subsets of 10,000 individuals drawn from the 30,592 deceased. We also repeated the spatial analysis for LED before and after controlling for nationality and neighborhood income.Results LED was not randomly distributed across the state of Geneva. The ten random subsets revealed no significant difference in the geographic footprint of LED and the population characteristics within Local Moran cluster types, suggesting the robustness of the spatial structure obtained. The proportion of women, the proportion of Swiss, and the median neighborhood income were significantly lower for populations within low LED clusters than for populations within high LED clusters. After controlling for nationality and neighborhood income, we observed a slight reduction in the low LED cluster footprints, but we found similar differences in population characteristics between cluster types.Conclusion To the best of our knowledge, this is the first study in Switzerland using spatial cluster detection methods to investigate small area inequalities in life expectancy. We identified a clear geographic footprint of LED, which may support further investigations and guide future public health interventions at the local level.