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Longevity pattern in the Italian region of Emilia Romagna: a dynamic perspective
Giulia Roli1, Alessandra Samoggia, Rossella Miglio
1Department of Statistical Sciences, University of Bologna, 40126 Bologna, Italy. g.roli@unibo.it
Geospatial Health
|May 29, 2012
Summary
This study analyzed longevity patterns in Emilia Romagna, Italy, using spatio-temporal modeling to identify consistent clusters of long-lived individuals and their relationship with mortality factors like circulatory diseases, altitude, and population density.
Area of Science:
- Demography
- Epidemiology
- Geospatial Analysis
Background:
- Investigating longevity patterns is crucial for understanding population aging.
- Geographical variations in longevity can be influenced by socio-economic and environmental factors.
- Spatio-temporal analysis is effective for studying rare events like extreme longevity in specific regions.
Purpose of the Study:
- To map and analyze the spatial distribution of longevity at the municipality level in Emilia Romagna.
- To identify clusters of high and low centenarian rates (CR) across two distinct time periods.
- To explore the influence of mortality patterns, causes of death, altitude, and population density on longevity.
Main Methods:
- Utilized a modified centenarian rate (CR) for two periods: 1995-1999 and 2005-2009.
- Employed spatio-temporal modeling to account for data rarity and spatial autocorrelation.
- Incorporated covariates such as mortality patterns, major causes of death, altitude, and population density.
Main Results:
- Identified persistent clusters of high and low longevity across the study periods.
- Areas in Ravenna and Forli-Cesena provinces, along the Adriatic coast and into Apennine municipalities, showed higher longevity.
- Circulatory diseases, altitude, and population density were significant factors influencing the presence of long-lived individuals.
Conclusions:
- Longevity patterns in Emilia Romagna exhibit spatial and temporal stability.
- Geographical factors and specific mortality causes significantly shape the distribution of extreme longevity.
- Spatio-temporal modeling provides valuable insights into the complex determinants of aging populations.
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