Abstract
Though Switzerland has one of the highest life expectancies in the world, this global indicator may mask significant disparities at a local level. The present study used a spatial cluster detection approach based on individual death records to investigate the geographical footprint of life expectancy inequalities in the state of Geneva, Switzerland. Individual-level mortality data (n = 22,751) were obtained from Geneva’s official death notices (2009–2016). We measured life expectancy inequalities using the years of potential life lost or gained (YPLLG) metric, defined as the difference between an individual’s age at death and their life expectancy at birth. We assessed the spatial dependence of YPLLG across the state of Geneva using spatial autocorrelation statistics (Local Moran’s I). To ensure the robustness of the patterns discovered, we ran the analyses for ten random subsets of 10,000 individuals taken from the 22,751 deceased. We also repeated the spatial analysis for YPLLG before and after controlling for individual-level and neighborhood-level covariates. The results showed that YPLLG was not randomly distributed across the state of Geneva. The ten random subsets revealed no significant difference with the geographic footprint of YPLLG and the population characteristics within Local Moran cluster types, suggesting robustness for the observed spatial structure. The proportion of women, the proportion of Swiss, the neighborhood median income, and the neighborhood median age were all significantly lower for populations in low YPLLG clusters when compared to populations in high YPLLG clusters. After controlling for individual-level and neighborhood-level covariates, we observed a reduction of 43% and 39% in the size of low and high YPLLG clusters, respectively. To our knowledge, this is the first study in Switzerland using spatial cluster detection methods to investigate inequalities in life expectancy at a local scale and based on individual data. We identified clear geographic footprints of YPLLG, which may support further investigations and guide future public health interventions at the local level.
Highlights
Though Switzerland has one of the highest life expectancies in the world, this global indicator may mask significant disparities at a local level
We assessed the spatial structure of years of potential life lost or gained (YPLLG) using spatial autocorrelation statistics, following which we investigated the influence of individual-level and neighborhood-level covariates on the geographic footprint of YPLLG
Spatial footprint of YPLLG for the raw model. (A) Local Moran cluster map was calculated for a random subset of 10,000 individuals taken from the 22,751 individuals in the state of Geneva for the 2009–2016 period (Subset 8)
Summary
Though Switzerland has one of the highest life expectancies in the world, this global indicator may mask significant disparities at a local level. After controlling for individual-level and neighborhood-level covariates, we observed a reduction of 43% and 39% in the size of low and high YPLLG clusters, respectively To our knowledge, this is the first study in Switzerland using spatial cluster detection methods to investigate inequalities in life expectancy at a local scale and based on individual data. Premature death, evaluated through Years of Potential Life Lost (YPLL), which estimates the years a person did not live compared to an arbitrary age, is one of the most widely used mortality-based indicators to measure the population’s h ealth[8,9,10]. The spatial structure of premature mortality has been found to be significantly associated with deprivation status[20,21,22,23,24,25,26], immigrant population s ize[23], and multiple environmental features (e.g., pollution, greenspace, walkability)[24,25,26]
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