Abstract
In this chapter, we investigate the pattern of longevity in an Italian region at a municipality level in two different periods. Spatio-temporal modeling is used to tackle at both periods the random variations in the occurrence of long-lived individuals, due to the rareness of such events in small areas. This method allows to exploit the spatial proximity smoothing the observed data, as well as to control for the effects of a set of regressors. As a result, clusters of areas characterized by extreme indexes of longevity are well identified and the temporal evolution of the phenomenon can be depicted. A joint analysis of male and female longevity by cohort in the two periods is conducted specifying a set of hierarchical Bayesian models.
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Notes
- 1.
The CR has been shown to be an appropriate indicator of longevity as it takes into account for the effects of the work-related migration. Indeed, it is well known that in Italy, including the Emilia Romagna region, migration was very common in the recent past, especially for working ages population. Under this perspective, the CR removes the unknown influence of the migration process, which is assumed to be negligible only after the 60 years of age.
- 2.
A multiple-year aggregation of data is introduced to avoid random fluctuations due to specific years or cohorts.
- 3.
Since these censuses are referred to the second half of October 1961 and 1971, we conventionally consider the data as a proxy of population on 1 January 1962 and 1972, respectively. Therefore, for each period, area, and gender, the CR95 + is obtained as the ratio of the mean number of people reached age 95 + during the period to the count of individuals aged 60–69 at the censuses.
- 4.
All the results we report for the CRs95 + refer to a number of 1, 000 individuals exposed to “risk”.
- 5.
We cannot exclude that some fluctuations of CR95 + could depend on work-related migration movements. For example, Ferrara has been an emigration area for population in working ages and if we assume that emigrants have a better health profile, those who remain could be a selected subpopulation that unlikely could reach oldest-old ages.
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Roli, G., Miglio, R., Rettaroli, R., Samoggia, A. (2013). The Longevity Pattern in Emilia Romagna, Italy: A Spatio-temporal Analysis. In: Torelli, N., Pesarin, F., Bar-Hen, A. (eds) Advances in Theoretical and Applied Statistics. Studies in Theoretical and Applied Statistics(). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35588-2_36
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