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Life expectancy inequalities in the elderly by socioeconomic status: evidence from Italy

Overview of attention for article published in Population Health Metrics, April 2018
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (76th percentile)

Mentioned by

blogs
1 blog
twitter
1 tweeter

Citations

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6 Dimensions

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32 Mendeley
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Title
Life expectancy inequalities in the elderly by socioeconomic status: evidence from Italy
Published in
Population Health Metrics, April 2018
DOI 10.1186/s12963-018-0163-7
Pubmed ID
Authors

Carlo Lallo, Michele Raitano

Abstract

Life expectancy considerably increased in most developed countries during the twentieth century. However, the increase in longevity is neither uniform nor random across individuals belonging to various socioeconomic groups. From an economic policy perspective, the difference in mortality by socioeconomic conditions challenges the fairness of the social security systems. We focus on the case of Italy and aim at measuring differences in longevity at older ages by individuals belonging to different socioeconomic groups, also in order to assess the effective fairness of the Italian public pension system, which is based on a notional defined contribution (NDC) benefit computation formula, whose rules do not take into account individual heterogeneity in expected longevity. We use a longitudinal dataset that matches survey data on individual features recorded in the Italian module of the EU-SILC, with information on the whole working life and until death collected in the administrative archives managed by the Italian National Social Security Institute. In more detail, we follow until 2009 a sample of 11,281 individuals aged at least 60 in 2005. We use survival analysis and measure the influence of a number of events experienced in the labor market and individual characteristics on mortality. Furthermore, through Kaplan-Meier simulations of hypothetical social groups, adjusted by a Brass relational model, we estimate and compare differences in life expectancy of individuals belonging to different socioeconomic groups. Our findings confirm that socioeconomic status strongly predicts life expectancy even in old age. All estimated models show that the prevalent type of working activity before retirement is significantly associated with the risk of death, even when controlling for dozens of variables as proxies of individual demographic and socioeconomic characteristics. The risk of death for self-employed individuals is 26% lower than that of employees, and life expectancy at 60 differs by five years between individuals with opposite socioeconomic statuses. Our study is the first that links results based on a micro survival analysis on subgroups of the elderly population with results related to the entire Italian population. The extreme differences in mortality risks by socioeconomic status found in our study confirm the existence of large health inequalities and strongly question the fairness of the Italian public pension system.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 32 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 32 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 6 19%
Student > Doctoral Student 4 13%
Other 4 13%
Researcher 4 13%
Student > Master 3 9%
Other 8 25%
Unknown 3 9%
Readers by discipline Count As %
Social Sciences 5 16%
Biochemistry, Genetics and Molecular Biology 4 13%
Economics, Econometrics and Finance 3 9%
Medicine and Dentistry 3 9%
Nursing and Health Professions 2 6%
Other 9 28%
Unknown 6 19%

Attention Score in Context

This research output has an Altmetric Attention Score of 8. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 14 April 2018.
All research outputs
#1,922,322
of 12,801,247 outputs
Outputs from Population Health Metrics
#71
of 284 outputs
Outputs of similar age
#62,727
of 271,831 outputs
Outputs of similar age from Population Health Metrics
#1
of 1 outputs
Altmetric has tracked 12,801,247 research outputs across all sources so far. Compared to these this one has done well and is in the 84th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 284 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 12.1. This one has gotten more attention than average, scoring higher than 71% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 271,831 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 76% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them