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

AbstractThough 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.

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

10.21203/rs.3.rs-578380/v1 ◽

2021 ◽

Author(s):

Anaïs Ladoy ◽

Juan Vallarta-Robledo ◽

David De Ridder ◽

José Sandoval ◽

Silvia Stringhini ◽

...

Keyword(s):

Life Expectancy ◽

Local Level ◽

Spatial Cluster ◽

The State ◽

Cluster Detection ◽

Population Characteristics ◽

Mortality Data ◽

Life Expectancy At Birth ◽

Significant Difference ◽

Neighborhood Income

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.

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Appendix for The COVID-19 Pandemic and Life Expectancy: Decomposition Using Individual-Level Mortality Data

SSRN Electronic Journal ◽

10.2139/ssrn.3850192 ◽

2021 ◽

Author(s):

Paula Natalia Barreto Parra ◽

Vladimir A. Atanasov ◽

John Meurer ◽

Jeffrey Whittle ◽

Qian Luo ◽

...

Keyword(s):

Life Expectancy ◽

Mortality Data ◽

Individual Level

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The COVID-19 Pandemic and Life Expectancy: Decomposition Using Individual-Level Mortality Data

SSRN Electronic Journal ◽

10.2139/ssrn.3795801 ◽

2021 ◽

Author(s):

Paula Natalia Barreto Parra ◽

Vladimir A. Atanasov ◽

John Meurer ◽

Jeffrey Whittle ◽

Qian Luo ◽

...

Keyword(s):

Life Expectancy ◽

Mortality Data ◽

Individual Level

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Appendix for The COVID-19 Pandemic, Years of Life Lost, and Life Expectancy: Decomposition Using Individual-Level Mortality Data

SSRN Electronic Journal ◽

10.2139/ssrn.4003532 ◽

2022 ◽

Author(s):

Paula Natalia Barreto Parra ◽

Vladimir A. Atanasov ◽

Jeffrey Whittle ◽

John Meurer ◽

Qian Luo ◽

...

Keyword(s):

Life Expectancy ◽

Mortality Data ◽

Years Of Life Lost ◽

Individual Level

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The disproportionate rise in COVID-19 cases among Hispanic/Latinx in disadvantaged communities of Orange County, California: A socioeconomic case-series

10.1101/2020.05.04.20090878 ◽

2020 ◽

Cited By ~ 2

Author(s):

Daniel S. Chow ◽

Jennifer E. Soun ◽

Justin Glavis-Bloom ◽

Brent Weinberg ◽

Peter D. Chang ◽

...

Keyword(s):

Health Insurance ◽

Orange County ◽

Case Series ◽

Ethnic Disparities ◽

The United States ◽

Median Income ◽

Census Tracts ◽

Individual Level ◽

Significant Difference ◽

Neighborhood Level

AbstractBackgroundRecent epidemiological evidence has demonstrated a higher rate of COVID-19 hospitalizations and deaths among minorities. This pattern of race-ethnic disparities emerging throughout the United States raises the question of what social factors may influence spread of a highly transmissible novel coronavirus. The purpose of this study is to describe race-ethnic and socioeconomic disparities associated with COVID-19 in patients in our community in Orange County, California and understand the role of individual-level factors, neighborhood-level factors, and access to care on outcomes.MethodsThis is a case-series of COVID-19 patients from the University of California, Irvine (UCI) across six-weeks between 3/12/2020 and 4/22/2020. Note, California’s shelter-in-place order began on 3/19/2020. Individual-level factors included race-ethnicity status were recorded. Neighborhood-level factors from census tracts included median household income, mean household size, proportion without a college degree, proportion working from home, and proportion without health insurance were also recorded.ResultsA total of 210-patients tested were COVID-19 positive, of which 73.3% (154/210) resided in Orange County. Hispanic/Latinx patients residing in census tracts below the median income demonstrated exponential growth (rate = 55.9%, R2 = 0.9742) during the study period. In addition, there was a significant difference for both race-ethnic (p < 0.001) and income bracket (p = 0.001) distributions prior to and after California’s shelter-in-place. In addition, the percentage of individuals residing in neighborhoods with denser households (p = 0.046), lower levels of college graduation (p < 0.001), health insurance coverage (p = 0.01), and ability to work from home (p < 0.001) significantly increased over the same timeframe.Conclusions and RelevanceOur study examines the race-ethnic disparities in Orange County, CA, and highlights vulnerable populations that are at increased risk for contracting COVID-19. Our descriptive case series illustrates that we also need to consider socioeconomic factors, which ultimately set the stage for biological and social disparities.

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Spatial and Institutional Analysis of the Voivodships Expenditure on Culture in the Years 2003 – 2015

e-Finanse ◽

10.2478/fiqf-2019-0021 ◽

2019 ◽

Vol 15 (3) ◽

pp. 67-75

Author(s):

Adam Mateusz Suchecki

Keyword(s):

Local Government ◽

Public Administration ◽

Local Governments ◽

Local Level ◽

Institutional Analysis ◽

The State ◽

Cultural Institutions ◽

Location Quotient ◽

Local Authorities ◽

National Heritage

AbstractFollowing the completion of the process of decentralisation of public administration in Poland in 2003, a number of tasks implemented previously by the state authorities were transferred to the local level. One of the most significant changes to the financing and management methods of the local authorities was the transfer of tasks related to culture and national heritage to the set of tasks implemented by local governments. As a result of the decentralisation process, the local government units in Poland were given significant autonomy in determining the purposes of their budgetary expenditures on culture. At the same time, they were obliged to cover these expenses from their own revenues.This paper focuses on the analysis of expenditures on culture covered by the voivodship budgets, taking into consideration the structure of cultural institutions by their types, between 2003-2015. The location quotient (LQ) was applied to two selected years (2006 and 2015) to illustrate the diversity of expenditures on culture in individual voivodships.

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Local mortality estimates during the COVID-19 pandemic in Italy

Journal of Population Economics ◽

10.1007/s00148-021-00857-y ◽

2021 ◽

Author(s):

Augusto Cerqua ◽

Roberta Di Stefano ◽

Marco Letta ◽

Sara Miccoli

Keyword(s):

Machine Learning ◽

Excess Mortality ◽

Control Method ◽

Local Level ◽

Supervised Machine Learning ◽

Machine Learning Techniques ◽

Mortality Data ◽

Official Method ◽

Learning Techniques ◽

Mortality Estimates

AbstractEstimates of the real death toll of the COVID-19 pandemic have proven to be problematic in many countries, Italy being no exception. Mortality estimates at the local level are even more uncertain as they require stringent conditions, such as granularity and accuracy of the data at hand, which are rarely met. The “official” approach adopted by public institutions to estimate the “excess mortality” during the pandemic draws on a comparison between observed all-cause mortality data for 2020 and averages of mortality figures in the past years for the same period. In this paper, we apply the recently developed machine learning control method to build a more realistic counterfactual scenario of mortality in the absence of COVID-19. We demonstrate that supervised machine learning techniques outperform the official method by substantially improving the prediction accuracy of the local mortality in “ordinary” years, especially in small- and medium-sized municipalities. We then apply the best-performing algorithms to derive estimates of local excess mortality for the period between February and September 2020. Such estimates allow us to provide insights about the demographic evolution of the first wave of the pandemic throughout the country. To help improve diagnostic and monitoring efforts, our dataset is freely available to the research community.

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